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Background: Regulations, study design complexity and amounts of collected and shared data in clinical trials render efficient data handling procedures inevitable. Recent research suggests that electronic data capture can be key in this context but evidence is insufficient. This randomized controlled parallel group study tested the hypothesis that time efficiency is superior when electronic (eCRF) instead of paper case report forms (pCRF) are used for data collection. We additionally investigated predictors of time saving effects and data integrity.Methods: This study was conducted on top of a clinical weight loss trial performed at a clinical research facility over six months. All study nurses and patients participating in the clinical trial were eligible to participate and randomly allocated to enter cross-sectional data obtained during routine visits either through pCRF or eCRF. A balanced randomization list was generated before enrolment commenced. 90 and 30 records were gathered for the time that 27 patients and 2 study nurses required to report 2025 and 2037 field values, respectively. The primary hypothesis, that eCRF use is faster than pCRF use, was tested by a two-tailed t-test. Analysis of variance and covariance were used to evaluate predictors of entry performance. Data integrity was evaluated by descriptive statistics.Results: All randomized patients were included in the study (eCRF group n = 13, pCRF group n = 14). eCRF, as compared to pCRF, data collection was associated with significant time savings  across all conditions (8.29 ± 5.15 min vs. 10.54 ± 6.98 min, p = .047). This effect was not defined by participant type, i.e. patients or study nurses (F(1,112) = .15, p = .699), CRF length (F(2,112) = .49, p = .609) or patient age (Beta = .09, p = .534). Additional 5.16 ± 2.83 min per CRF were saved with eCRFs due to data transcription redundancy when patients answered questionnaires directly in eCRFs. Data integrity was superior in the eCRF condition (0 versus 3 data entry errors).Conclusions: This is the first study to prove in direct comparison that using eCRFs instead of pCRFs increases time efficiency of data collection in clinical trials, irrespective of item quantity or patient age, and improves data quality.Trial Registration: Clinical Trials NCT02649907 . [ABSTRACT FROM AUTHOR]

Background: The design of a multi-center randomized controlled trial (RCT) involves multiple considerations, such as the choice of the sample size, the number of centers and their geographic location, the strategy for recruitment of study participants, amongst others. There are plenty of methods to sequentially randomize patients in a multi-center RCT, with or without considering stratification factors. The goal of this paper is to perform a systematic assessment of such randomization methods for a multi-center 1:1 RCT assuming a competitive policy for the patient recruitment process. Methods: We considered a Poisson-gamma model for the patient recruitment process with a uniform distribution of center activation times. We investigated 16 randomization methods (4 unstratified, 4 region-stratified, 4 center-stratified, 3 dynamic balancing randomization (DBR), and a complete randomization design) to sequentially randomize n = 500 patients. Statistical properties of the recruitment process and the randomization procedures were assessed using Monte Carlo simulations. The operating characteristics included time to complete recruitment, number of centers that recruited a given number of patients, several measures of treatment imbalance and estimation efficiency under a linear model for the response, the expected proportions of correct guesses under two different guessing strategies, and the expected proportion of deterministic assignments in the allocation sequence. Results: Maximum tolerated imbalance (MTI) randomization methods such as big stick design, Ehrenfest urn design, and block urn design result in a better balance–randomness tradeoff than the conventional permuted block design (PBD) with or without stratification. Unstratified randomization, region-stratified randomization, and center-stratified randomization provide control of imbalance at a chosen level (trial, region, or center) but may fail to achieve balance at the other two levels. By contrast, DBR does a very good job controlling imbalance at all 3 levels while maintaining the randomized nature of treatment allocation. Adding more centers into the study helps accelerate the recruitment process but at the expense of increasing the number of centers that recruit very few (or no) patients—which may increase center-level imbalances for center-stratified and DBR procedures. Increasing the block size or the MTI threshold(s) may help obtain designs with improved randomness–balance tradeoff. Conclusions: The choice of a randomization method is an important component of planning a multi-center RCT. Dynamic balancing randomization with carefully chosen MTI thresholds could be a very good strategy for trials with the competitive policy for patient recruitment. [ABSTRACT FROM AUTHOR]

The letter responds to a paper that claims there was a risk of selection bias in the NINDS rt-PA for Acute Ischemic Stroke Study. The authors of the letter argue that the paper contains factual errors and misconceptions about the study. They clarify that there were errors in the randomization process, but these errors did not contribute to bias in the treatment effect. The authors also explain the difference between concealment and blinding in clinical trials and refute the suggestion that treatment assignment was manipulated. They emphasize the benefits of thrombolytic therapy for acute ischemic stroke and urge readers to review the literature on the topic. [Extracted from the article]

The current Evidence-Based-Medicine (EBM) approach is generally based on data coming from Randomized Clinical Trials or and, epidemiological observational studies. However, the past few years, with the explosion of available data derived from e-technology, a novel aspect regarding EBM arose, the Real-World-Data (RWD). RWD refers to data collected outside traditional studies, such as e-health records, claims data, patient-generated information, registries, etc. This type of information provides invaluable insights into the effectiveness, safety, and value of medical treatments and interventions when applied in real world settings. European Health Data Space (EHDS) is an initiative launched by the European Commission to create a secure and protected platform for exchanging health data across borders within European Union. The powerful combination of RWD within the EHDS serves as a valuable resource, supporting research initiatives. By analyzing diverse RWD sources, researchers generate Real-World Evidence (RWE) broadening medical knowledge. In this comment paper, methodological issues and opportunities of the application of EHDS in member states are discussed. Undoubtedly, EHDS creates a health-specific ecosystem empowering individuals through increased digital access and control their health data, providing a consistent, truthful and proficient set-up for the use of health data for research, innovation, policy-making and regulatory activities. [ABSTRACT FROM AUTHOR]

Background: Adaptive designs offer added flexibility in the execution of clinical trials, including the possibilities of allocating more patients to the treatments that turned out more successful, and early stopping due to either declared success or futility. Commonly applied adaptive designs, such as group sequential methods, are based on the frequentist paradigm and on ideas from statistical significance testing. Interim checks during the trial will have the effect of inflating the Type 1 error rate, or, if this rate is controlled and kept fixed, lowering the power.Results: The purpose of the paper is to demonstrate the usefulness of the Bayesian approach in the design and in the actual running of randomized clinical trials during phase II and III. This approach is based on comparing the performance of the different treatment arms in terms of the respective joint posterior probabilities evaluated sequentially from the accruing outcome data, and then taking a control action if such posterior probabilities fall below a pre-specified critical threshold value. Two types of actions are considered: treatment allocation, putting on hold at least temporarily further accrual of patients to a treatment arm, and treatment selection, removing an arm from the trial permanently. The main development in the paper is in terms of binary outcomes, but extensions for handling time-to-event data, including data from vaccine trials, are also discussed. The performance of the proposed methodology is tested in extensive simulation experiments, with numerical results and graphical illustrations documented in a Supplement to the main text. As a companion to this paper, an implementation of the methods is provided in the form of a freely available R package 'barts'.Conclusion: The proposed methods for trial design provide an attractive alternative to their frequentist counterparts. [ABSTRACT FROM AUTHOR]

Background: The Schedule for the Evaluation of Individual Quality of Life-Direct Weighting (SEIQoL-DW) is a prevalent face-to-face interview method for measuring quality of life by integrating respondent-generated dimensions. To apply this method in clinical trials, a paper-administered alternative would be of interest. Therefore, our study aimed to analyze the agreement between the SEIQoL-DW and a paper questionnaire version (SEIQoL-PF/G).Methods: In a crossover design, both measures were completed in a random sequence. 104 patients at a heart surgery hospital in Germany were randomly assigned to receive either the SEIQoL-DW or the SEIQoL-PF/G as the first measurement in the sequence. Patients were approached on their earliest stable day after surgery. The average time between both measurements was 1 day (mean 1.3; SD 0.8). Agreement regarding the indices, ratings, and weightings of nominated life areas (cues) was explored using Bland-Altman plots with 95% limits of agreement (LoA). Agreement of the SEIQoL indices was defined as acceptable if the LoA did not exceed a threshold of 10 scale points. Data from n = 99 patients were included in the agreement analysis.Results: Both measures led to similarly nominated cues. The most frequently nominated cues were "physical health" and "family". In the Bland-Altman plot, the indices showed a mean of differences of 2 points (95% CI, - 1 to 6). The upper LoA showed a difference of 36 points (95% CI, 30 to 42), and the lower LoA showed a difference of - 31 points (95% CI, - 37 to - 26). Thus, the LoAs and confidence intervals exceeded the predefined threshold. The Bland-Altman plots for the cue levels and cue weights showed similar results. The SEIQoL-PF/G version showed a tendency for equal weighting of cues, while the weighting procedure of the SEIQoL-DW led to greater variability.Conclusions: For cardiac surgery patients, use of the current version of the SEIQoL-PF/G as a substitute for the SEIQoL-DW is not recommended. The current questionnaire weighting method seems to be unable to distinguish weighting for different cues. Therefore, the further design of a weighting method without interviewer support as a paper-administered measure of individual quality of life is desirable. [ABSTRACT FROM AUTHOR]

Background: Precision medicine is an emerging field that involves the selection of treatments based on patients' individual prognostic data. It is formalized through the identification of individualized treatment rules (ITRs) that maximize a clinical outcome. When the type of outcome is time-to-event, the correct handling of censoring is crucial for estimating reliable optimal ITRs. Methods: We propose a jackknife estimator of the value function to allow for right-censored data for a binary treatment. The jackknife estimator or leave-one-out-cross-validation approach can be used to estimate the value function and select optimal ITRs using existing machine learning methods. We address the issue of censoring in survival data by introducing an inverse probability of censoring weighted (IPCW) adjustment in the expression of the jackknife estimator of the value function. In this paper, we estimate the optimal ITR by using random survival forest (RSF) and Cox proportional hazards model (COX). We use a Z-test to compare the optimal ITRs learned by RSF and COX with the zero-order model (or one-size-fits-all). Through simulation studies, we investigate the asymptotic properties and the performance of our proposed estimator under different censoring rates. We illustrate our proposed method on a phase III clinical trial of non-small cell lung cancer data. Results: Our simulations show that COX outperforms RSF for small sample sizes. As sample sizes increase, the performance of RSF improves, in particular when the expected log failure time is not linear in the covariates. The estimator is fairly normally distributed across different combinations of simulation scenarios and censoring rates. When applied to a non-small-cell lung cancer data set, our method determines the zero-order model (ZOM) as the best performing model. This finding highlights the possibility that tailoring may not be needed for this cancer data set. Conclusion: The jackknife approach for estimating the value function in the presence of right-censored data shows satisfactory performance when there is small to moderate censoring. Winsorizing the upper and lower percentiles of the estimated survival weights for computing the IPCWs stabilizes the estimator. [ABSTRACT FROM AUTHOR]

Background: Current dose-finding designs for phase I clinical trials can correctly select the MTD in a range of 30-80% depending on various conditions based on a sample of 30 subjects. However, there is still an unmet need for efficiency and cost saving.Methods: We propose a novel dose-finding design based on Bayesian stochastic approximation. The design features utilization of dose level information through local adaptive modelling and free assumption of toxicity probabilities and hyper-parameters. It allows a flexible target toxicity rate and varying cohort size. And we extend it to accommodate historical information via prior effective sample size. We compare the proposed design to some commonly used methods in terms of accuracy and safety by simulation.Results: On average, our design can improve the percentage of correct selection to about 60% when the MTD resides at a early or middle position in the search domain and perform comparably to other competitive methods otherwise. A free online software package is provided to facilitate the application, where a simple decision tree for the design can be pre-printed beforehand.Conclusion: The paper proposes a novel dose-finding design for phase I clinical trials. Applying the design to future cancer trials can greatly improve the efficiency, consequently save cost and shorten the development period. [ABSTRACT FROM AUTHOR]

Background: Patient participation in cancer clinical trials is suboptimal. A challenge to capturing physicians' insights about trials has been low response to surveys. We conducted a study using varying combinations of mail and email to recruit a nationally representative sample of medical, surgical, and radiation oncologists to complete a survey on trial accrual.Methods: We randomly assigned eligible physicians identified from the American Medical Association MasterFile (n = 13,251) to mail- or email-based recruitment strategies. Mail-based recruitment included a survey packet with: (1) cover letter describing the survey and inviting participation; (2) paper copy of the survey and postage-paid return envelope; and (3) a web link for completing the survey online. Email-based recruitment included an e-mail describing the survey and inviting participation, along with the web link to the survey, and a reminder postcard 2 weeks later.Results: Response was higher for mail-based (11.8, 95% CI 11.0-12.6%) vs. email-based (4.5, 95% CI 4.0-5.0%) recruitment. In email-based recruitment, only one-quarter of recipients opened the email, and even fewer clicked on the link to complete the survey. Most physicians in mail-based recruitment responded after the first invitation (362 of 770 responders, 47.0%). A higher proportion of responders vs. non-responders were young (ages 25-44 years), men, and radiation or surgical (vs. medical) oncologists.Conclusions: Most physicians assigned to mail-based recruitment actually completed the survey online via the link provided in the cover letter, and those in email-based recruitment did not respond until they received a reminder postcard by mail. Providing the option to return a paper survey or complete it online may have further increased participation in the mail-based group, and future studies should examine how combinations of delivery mode and return options affect physicians' response to surveys. [ABSTRACT FROM AUTHOR]

Background: The classical linear model is widely used in the analysis of clinical trials with continuous outcomes. However, required model assumptions are frequently not met, resulting in estimates of treatment effect that can be inefficient and biased. In addition, traditional models assess treatment effect only on the mean response, and not on other aspects of the response, such as the variance. Distributional regression modelling overcomes these limitations. The purpose of this paper is to demonstrate its usefulness for the analysis of clinical trials, and superior performance to that of traditional models.Methods: Distributional regression models are demonstrated, and contrasted with normal linear models, on data from the LIPID randomized controlled trial, which compared the effects of pravastatin with placebo in patients with coronary heart disease. Systolic blood pressure (SBP) and the biomarker midregional pro-adrenomedullin (MR-proADM) were analysed. Treatment effect was estimated in models that used response distributions more appropriate than the normal (Box-Cox-t and Johnson's Su for MR-proADM and SBP, respectively), applied censoring below the detection limit of MR-proADM, estimated treatment effect on distributional parameters other than the mean, and included random effects for longitudinal observations. A simulation study was conducted to compare the performance of distributional regression models with normal linear regression, under conditions mimicking the LIPID study. The R package gamlss (Generalized Additive Models for Location, Scale and Shape), which implements maximum likelihood estimation for distributional regression modelling, was used throughout.Results: In all cases the distributional regression models fit the data well, in contrast to poor fits obtained for traditional models; for MR-proADM a small but significant treatment effect on the mean was detected by the distributional regression model and not the normal model; and for SBP a beneficial treatment effect on the variance was demonstrated. In the simulation study distributional models strongly outperformed normal models when the response variable was non-normal and heterogeneous; and there was no disadvantage introduced by the use of distributional regression modelling when the response satisfied the normal linear model assumptions.Conclusions: Distributional regression models are a rich framework, largely untapped in the clinical trials world. We have demonstrated a sample of the capabilities of these models for the analysis of trials. If interest lies in accurate estimation of treatment effect on the mean, or other distributional features such as variance, the use of distributional regression modelling will yield superior estimates to traditional normal models, and is strongly recommended.Trial Registration: The LIPID trial was retrospectively registered on ANZCTR on 27/04/2016, registration number ACTRN12616000535471 . [ABSTRACT FROM AUTHOR]

Background: Randomization is the foundation of any clinical trial involving treatment comparison. It helps mitigate selection bias, promotes similarity of treatment groups with respect to important known and unknown confounders, and contributes to the validity of statistical tests. Various restricted randomization procedures with different probabilistic structures and different statistical properties are available. The goal of this paper is to present a systematic roadmap for the choice and application of a restricted randomization procedure in a clinical trial.Methods: We survey available restricted randomization procedures for sequential allocation of subjects in a randomized, comparative, parallel group clinical trial with equal (1:1) allocation. We explore statistical properties of these procedures, including balance/randomness tradeoff, type I error rate and power. We perform head-to-head comparisons of different procedures through simulation under various experimental scenarios, including cases when common model assumptions are violated. We also provide some real-life clinical trial examples to illustrate the thinking process for selecting a randomization procedure for implementation in practice.Results: Restricted randomization procedures targeting 1:1 allocation vary in the degree of balance/randomness they induce, and more importantly, they vary in terms of validity and efficiency of statistical inference when common model assumptions are violated (e.g. when outcomes are affected by a linear time trend; measurement error distribution is misspecified; or selection bias is introduced in the experiment). Some procedures are more robust than others. Covariate-adjusted analysis may be essential to ensure validity of the results. Special considerations are required when selecting a randomization procedure for a clinical trial with very small sample size.Conclusions: The choice of randomization design, data analytic technique (parametric or nonparametric), and analysis strategy (randomization-based or population model-based) are all very important considerations. Randomization-based tests are robust and valid alternatives to likelihood-based tests and should be considered more frequently by clinical investigators. [ABSTRACT FROM AUTHOR]

Background: Data monitoring of clinical trials is a tool aimed at reducing the risks of random errors (e.g. clerical errors) and systematic errors, which include misinterpretation, misunderstandings, and fabrication. Traditional 'good clinical practice data monitoring' with on-site monitors increases trial costs and is time consuming for the local investigators. This paper aims to outline our approach of time-effective central data monitoring for the SafeBoosC-III multicentre randomised clinical trial and present the results from the first three central data monitoring meetings.Methods: The present approach to central data monitoring was implemented for the SafeBoosC-III trial, a large, pragmatic, multicentre, randomised clinical trial evaluating the benefits and harms of treatment based on cerebral oxygenation monitoring in preterm infants during the first days of life versus monitoring and treatment as usual. We aimed to optimise completeness and quality and to minimise deviations, thereby limiting random and systematic errors. We designed an automated report which was blinded to group allocation, to ease the work of data monitoring. The central data monitoring group first reviewed the data using summary plots only, and thereafter included the results of the multivariate Mahalanobis distance of each centre from the common mean. The decisions of the group were manually added to the reports for dissemination, information, correcting errors, preventing furture errors and documentation.Results: The first three central monitoring meetings identified 156 entries of interest, decided upon contacting the local investigators for 146 of these, which resulted in correction of 53 entries. Multiple systematic errors and protocol violations were identified, one of these included 103/818 randomised participants. Accordingly, the electronic participant record form (ePRF) was improved to reduce ambiguity.Discussion: We present a methodology for central data monitoring to optimise quality control and quality development. The initial results included identification of random errors in data entries leading to correction of the ePRF, systematic protocol violations, and potential protocol adherence issues. Central data monitoring may optimise concurrent data completeness and may help timely detection of data deviations due to misunderstandings or fabricated data. [ABSTRACT FROM AUTHOR]

Background: Data-sharing policies in randomized clinical trials (RCTs) should have an evaluation component. The main objective of this case-control study was to assess the impact of published re-uses of RCT data in terms of media attention (Altmetric) and citation rates.Methods: Re-uses of RCT data published up to December 2019 (cases) were searched for by two reviewers on 3 repositories (CSDR, YODA project, and Vivli) and matched to control papers published in the same journal. The Altmetric Attention Score (primary outcome), components of this score (e.g. mention of policy sources, media attention) and the total number of citations were compared between these two groups.Results: 89 re-uses were identified: 48 (53.9%) secondary analyses, 34 (38.2%) meta-analyses, 4 (4.5%) methodological analyses and 3 (3.4%) re-analyses. The median (interquartile range) Altmetric Attention Scores were 5.9 (1.3-22.2) for re-use and 2.8 (0.3-12.3) for controls (p = 0.14). No statistical difference was found on any of the components of in the Altmetric Attention Score. The median (interquartile range) numbers of citations were 3 (1-8) for reuses and 4 (1 - 11.5) for controls (p = 0.30). Only 6/89 re-uses (6.7%) were cited in a policy source.Conclusions: Using all available re-uses of RCT data to date from major data repositories, we were not able to demonstrate that re-uses attracted more attention than a matched sample of studies published in the same journals. Small average differences are still possible, as the sample size was limited. However matching choices have some limitations so results should be interpreted very cautiously. Also, citations by policy sources for re-uses were rare.Trial Registration: Registration: osf.io/fp62e. [ABSTRACT FROM AUTHOR]

Background: Missing data may seriously compromise inferences from randomised clinical trials, especially if missing data are not handled appropriately. The potential bias due to missing data depends on the mechanism causing the data to be missing, and the analytical methods applied to amend the missingness. Therefore, the analysis of trial data with missing values requires careful planning and attention.Methods: The authors had several meetings and discussions considering optimal ways of handling missing data to minimise the bias potential. We also searched PubMed (key words: missing data; randomi*; statistical analysis) and reference lists of known studies for papers (theoretical papers; empirical studies; simulation studies; etc.) on how to deal with missing data when analysing randomised clinical trials.Results: Handling missing data is an important, yet difficult and complex task when analysing results of randomised clinical trials. We consider how to optimise the handling of missing data during the planning stage of a randomised clinical trial and recommend analytical approaches which may prevent bias caused by unavoidable missing data. We consider the strengths and limitations of using of best-worst and worst-best sensitivity analyses, multiple imputation, and full information maximum likelihood. We also present practical flowcharts on how to deal with missing data and an overview of the steps that always need to be considered during the analysis stage of a trial.Conclusions: We present a practical guide and flowcharts describing when and how multiple imputation should be used to handle missing data in randomised clinical. [ABSTRACT FROM AUTHOR]

Background: Phase II clinical trials primarily aim to find the optimal dose and investigate the relationship between dose and efficacy relative to standard of care (control). Therefore, before moving forward to a phase III confirmatory trial, the most effective dose is needed to be identified.Methods: The primary endpoint of a phase II trial is typically a binary endpoint of success or failure. The EMAX model, ubiquitous in pharmacology research, was fit for many compounds and described the data well, except for a single compound, which had nonmonotone dose-response (Thomas et al., Stat Biopharmaceutical Res. 6:302-317 2014). To mitigate the risk of nonmonotone dose response one of the alternative options is a Bayesian hierarchical EMAX model (Gajewski et al., Stat Med. 38:3123-3138 2019). The hierarchical EMAX adapts to its environment.Results: When the dose-response curve is monotonic it enjoys the efficiency of EMAX. When the dose-response curve is non-monotonic the additional random effect hyperprior makes the hierarchical EMAX model more adjustable and flexible. However, the normal dynamic linear model (NDLM) is a useful model to explore dose-response relationships in that the efficacy at the current dose depends on the efficacy of the previous dose(s). Previous research has compared the EMAX to the hierarchical EMAX (Gajewski et al., Stat Med. 38:3123-3138 2019) and the EMAX to the NDLM (Liu et al., BMC Med Res Method 17:149 2017), however, the hierarchical EMAX has not been directly compared to the NDLM.Conclusions: The focus of this paper is to compare these models and discuss the relative merit for each of their uses for an ongoing early phase dose selection study. [ABSTRACT FROM AUTHOR]

Background: Clinical trials are essential to improve knowledge of anesthesia and perioperative medicine. Unfortunately, many studies face participant-recruitment issues and fail to include the planned number of participants. There is limited published data about how information delivered about the study or how the experiences and attitudes of prospective participants influence willingness to participate. The purpose of this study was to identify such factors in the domain of anesthesia care.Methods: We performed a cross-sectional study at the Geneva University Hospitals (Switzerland) using a newly developed paper-based questionnaire on a sample of outpatients with a recent hospital stay and that were aged over 18 years, confident speaking French and free of any disease that could hinder participation. We explored patient personal factors, such as current health, past exposure to clinical research and anesthesia, as well as study-related factors. Six different scenarios for clinical studies were assessed. Linear regression modeling was used to assess the specific association between personal and study-related factors and willingness to participate in the studies described in the scenarios.Results: On the 1318 eligible patients, 398 fully completed the questionnaire. Multivariable adjustment revealed that factors related to altruistic values (β, 9.6, 95% CI 3.4 to 15.7, P = 0.002), to the feeling of benefiting from a more effective treatment (β, 4.7, 95% CI 0.2 to 9.2, P = 0.041) and to the absence of fear about double blinding (β, 5.7, 95% CI 1.3 to 10.2, P = 0.012) were positively associated with willingness to participate. Conversely, concerns about drug-related adverse effects (β, - 11.7, 95% CI - 16.9 to - 6.5, P < 0.001) and anxiety about surgery (β, - 5.2, 95% CI - 10.0 to - 0.5, P = 0.031) were negatively associated with willingness to participate.Conclusion: Our study was based on vignettes illustrating typical scenarios of clinical trials performed in anesthesia. However, their similarities with real studies still remains hypothetical and our results should be interpreted as such. Nevertheless, the study contributes to improve understanding of factors that may act as incentives or barriers to participation in clinical trials. It highlights the importance of providing appropriate information and reassurance to patients. [ABSTRACT FROM AUTHOR]

Background: Missing data are an inevitable challenge in Randomised Controlled Trials (RCTs), particularly those with Patient Reported Outcome Measures. Methodological guidance suggests that to avoid incorrect conclusions, studies should undertake sensitivity analyses which recognise that data may be 'missing not at random' (MNAR). A recommended approach is to elicit expert opinion about the likely outcome differences for those with missing versus observed data. However, few published trials plan and undertake these elicitation exercises, and so lack the external information required for these sensitivity analyses. The aim of this paper is to provide a framework that anticipates and allows for MNAR data in the design and analysis of clinical trials.Methods: We developed a framework for performing and using expert elicitation to frame sensitivity analysis in RCTs with missing outcome data. The framework includes the following steps: first defining the scope of the elicitation exercise, second developing the elicitation tool, third eliciting expert opinion about the missing outcomes, fourth evaluating the elicitation results, and fifth analysing the trial data. We provide guidance on key practical challenges that arise when adopting this approach in trials: the criteria for identifying relevant experts, the outcome scale for presenting data to experts, the appropriate representation of expert opinion, and the evaluation of the elicitation results.The framework was developed within the POPPI trial, which investigated whether a preventive, complex psychological intervention, commenced early in ICU, would reduce the development of patient-reported post-traumatic stress disorder symptom severity, and improve health-related quality of life. We illustrate the key aspects of the proposed framework using the POPPI trial.Results: For the POPPI trial, 113 experts were identified with potentially suitable knowledge and asked to participate in the elicitation exercise. The 113 experts provided 59 usable elicitation questionnaires. The sensitivity analysis found that the results from the primary analysis were robust to alternative MNAR mechanisms.Conclusions: Future studies can adopt this framework to embed expert elicitation within the design of clinical trials. This will provide the information required for MNAR sensitivity analyses that examine the robustness of the trial conclusions to alternative, but realistic assumptions about the missing data. [ABSTRACT FROM AUTHOR]

Background: The application of adaptive design methodology within a clinical trial setting is becoming increasingly popular. However the application of these methods within trials is not being reported as adaptive designs hence making it more difficult to capture the emerging use of these designs. Within this review, we aim to understand how adaptive design methodology is being reported, whether these methods are explicitly stated as an 'adaptive design' or if it has to be inferred and to identify whether these methods are applied prospectively or concurrently.Methods: Three databases; Embase, Ovid and PubMed were chosen to conduct the literature search. The inclusion criteria for the review were phase II, phase III and phase II/III randomised controlled trials within the field of Oncology that published trial results in 2015. A variety of search terms related to adaptive designs were used.Results: A total of 734 results were identified, after screening 54 were eligible. Adaptive designs were more commonly applied in phase III confirmatory trials. The majority of the papers performed an interim analysis, which included some sort of stopping criteria. Additionally only two papers explicitly stated the term 'adaptive design' and therefore for most of the papers, it had to be inferred that adaptive methods was applied. Sixty-five applications of adaptive design methods were applied, from which the most common method was an adaptation using group sequential methods.Conclusions: This review indicated that the reporting of adaptive design methodology within clinical trials needs improving. The proposed extension to the current CONSORT 2010 guidelines could help capture adaptive design methods. Furthermore provide an essential aid to those involved with clinical trials. [ABSTRACT FROM AUTHOR]

Background: The benefit of a given treatment can be evaluated via a randomized clinical trial design. However, protocol deviations may severely compromise treatment effect since such deviations often lead to missing values. The assumption that methods of analysis can account for the missing data cannot be justified and hence methods of analysis based on plausible assumptions should be used. An alternative analysis to the simple imputation methods requires unverifiable assumptions about the missing data. Therefore sensitivity analysis should be performed to investigate the robustness of statistical inferences to alternative assumptions about the missing data.Aims: In this paper, we investigate the effect of tuberculosis pericarditis treatment (prednisolone) on CD4 count changes over time and draw inferences in the presence of missing data. The data come from a multicentre clinical trial (the IMPI trial).Methods: We investigate the effect of prednisolone on CD4 count changes by adjusting for baseline and time-dependent covariates in the fitted model. To draw inferences in the presence of missing data, we investigate sensitivity of statistical inferences to missing data assumptions using the pattern-mixture model with multiple imputation (PM-MI) approach. We also performed simulation experiment to evaluate the performance of the imputation approaches.Results: Our results showed that the prednisolone treatment has no significant effect on CD4 count changes over time and that the prednisolone treatment does not interact with time and anti-retroviral therapy (ART). Also, patients' CD4 count levels significantly increase over the study period and patients on ART treatment have higher CD4 count levels compared with those not on ART. The results also showed that older patients had lower CD4 count levels compared with younger patients, and parameter estimates under the MAR assumption are robust to NMAR assumptions.Conclusions: Since the parameter estimates under the MAR analysis are robust to NMAR analyses, the process that generated the missing data in the CD4 count measurements is missing at random (MAR). The implication is that valid inferences can be obtained using either the likelihood-based methods or multiple imputation approaches. [ABSTRACT FROM AUTHOR]

Background: It is well recognized that treatment effects may not be homogeneous across the study population. Subgroup analyses constitute a fundamental step in the assessment of evidence from confirmatory (Phase III) clinical trials, where conclusions for the overall study population might not hold. Subgroup analyses can have different and distinct purposes, requiring specific design and analysis solutions. It is relevant to evaluate methodological developments in subgroup analyses against these purposes to guide health care professionals and regulators as well as to identify gaps in current methodology.Methods: We defined four purposes for subgroup analyses: (1) Investigate the consistency of treatment effects across subgroups of clinical importance, (2) Explore the treatment effect across different subgroups within an overall non-significant trial, (3) Evaluate safety profiles limited to one or a few subgroup(s), (4) Establish efficacy in the targeted subgroup when included in a confirmatory testing strategy of a single trial. We reviewed the methodology in line with this "purpose-based" framework. The review covered papers published between January 2005 and April 2015 and aimed to classify them in none, one or more of the aforementioned purposes.Results: In total 1857 potentially eligible papers were identified. Forty-eight papers were selected and 20 additional relevant papers were identified from their references, leading to 68 papers in total. Nineteen were dedicated to purpose 1, 16 to purpose 4, one to purpose 2 and none to purpose 3. Seven papers were dedicated to more than one purpose, the 25 remaining could not be classified unambiguously. Purposes of the methods were often not specifically indicated, methods for subgroup analysis for safety purposes were almost absent and a multitude of diverse methods were developed for purpose (1).Conclusions: It is important that researchers developing methodology for subgroup analysis explicitly clarify the objectives of their methods in terms that can be understood from a patient's, health care provider's and/or regulator's perspective. A clear operational definition for consistency of treatment effects across subgroups is lacking, but is needed to improve the usability of subgroup analyses in this setting. Finally, methods to particularly explore benefit-risk systematically across subgroups need more research. [ABSTRACT FROM AUTHOR]

Background: Several papers report deficiencies in the reporting of information about the implementation of interventions in clinical trials. Information about implementation is also required in systematic reviews of complex interventions to facilitate the translation and uptake of evidence of provider-based prevention and treatment programs. To capture whether and how implementation is assessed within systematic effectiveness reviews, we developed a checklist for implementation (Ch-IMP) and piloted it in a cohort of reviews on provider-based prevention and treatment interventions for children and young people. This paper reports on the inter-rater reliability, feasibility and reasons for discrepant ratings. Methods: Checklist domains were informed by a framework for program theory; items within domains were generated from a literature review. The checklist was pilot-tested on a cohort of 27 effectiveness reviews targeting children and youth. Two raters independently extracted information on 47 items. Inter-rater reliability was evaluated using percentage agreement and unweighted kappa coefficients. Reasons for discrepant ratings were content analysed. Results: Kappa coefficients ranged from 0.37 to 1.00 and were not influenced by one-sided bias. Most kappa values were classified as excellent (n = 20) or good (n = 17) with a few items categorised as fair (n = 7) or poor (n = 1). Prevalence-adjusted kappa coefficients indicate good or excellent agreement for all but one item. Four areas contributed to scoring discrepancies: 1) clarity or sufficiency of information provided in the review; 2) information missed in the review; 3) issues encountered with the tool; and 4) issues encountered at the review level. Use of the tool demands time investment and it requires adjustment to improve its feasibility for wider use. Conclusions: The case of provider-based prevention and treatment interventions showed relevancy in developing and piloting the Ch-IMP as a useful tool for assessing the extent to which systematic reviews assess the quality of implementation. The checklist could be used by authors and editors to improve the quality of systematic reviews, and shows promise as a pedagogical tool to facilitate the extraction and reporting of implementation characteristics. [ABSTRACT FROM AUTHOR]

Background: The Template for Intervention Description and Replication (TIDieR) checklist and guide was developed by an international team of experts to promote full and accurate description of trial interventions. It is now widely used in health research. The aim of this paper is to describe the experience of using TIDieR outside of trials, in a range of applied health research contexts, and make recommendations on its usefulness in such settings.Main Body: We used the TIDieR template for intervention description in six applied health research projects. The six cases comprise a diverse sample in terms of clinical problems, population, settings, stage of intervention development and whether the intervention was led by researchers or the service deliverers. There was also variation in how the TIDieR description was produced in terms of contributors and time point in the project. Researchers involved in the six cases met in two workshops to identify issues and themes arising from their experience of using TIDieR. We identified four themes which capture the difficulties or complexities of using TIDieR in applied health research: (i) fidelity and adaptation: all aspects of an intervention can change over time; (ii) voice: the importance of clarity on whose voice the TIDieR description represents; (iii) communication beyond the immediate context: the usefulness of TIDieR for wider dissemination and sharing; (iv) the use of TIDieR as a research tool.Conclusion: We found TIDieR to be a useful tool for applied research outside the context of clinical trials and we suggest four revisions or additions to the original TIDieR which would enable it to better capture these complexities in applied health research: An additional item, 'voice' conveys who was involved in preparing the TIDieR template, such as researchers, service users or service deliverers. An additional item, 'stage of implementation' conveys what stage the intervention has reached, using a continuum of implementation research suggested by the World Health Organisation. A new column, 'modification' reminds authors to describe modifications to any item in the checklist. An extension of the 'how well' item encourages researchers to describe how contextual factors affected intervention delivery. [ABSTRACT FROM AUTHOR]

Background: Many clinical trials focus on the comparison of the treatment effect between two or more groups concerning a rarely occurring event. In this situation, showing a relevant effect with an acceptable power requires the observation of a large number of patients over a long period of time. For feasibility issues, it is therefore often considered to include several event types of interest, non-fatal or fatal, and to combine them within a composite endpoint. Commonly, a composite endpoint is analyzed with standard survival analysis techniques by assessing the time to the first occurring event. This approach neglects that an individual may experience more than one event which leads to a loss of information. As an alternative, composite endpoints could be analyzed by models for recurrent events. There exists a number of such models, e.g. regression models based on count data or Cox-based models such as the approaches of Andersen and Gill, Prentice, Williams and Peterson or, Wei, Lin and Weissfeld. Although some of the methods were already compared within the literature there exists no systematic investigation for the special requirements regarding composite endpoints.Methods: Within this work a simulation-based comparison of recurrent event models applied to composite endpoints is provided for different realistic clinical trial scenarios.Results: We demonstrate that the Andersen-Gill model and the Prentice- Williams-Petersen models show similar results under various data scenarios whereas the Wei-Lin-Weissfeld model delivers effect estimators which can considerably deviate under commonly met data scenarios.Conclusion: Based on the conducted simulation study, this paper helps to understand the pros and cons of the investigated methods in the context of composite endpoints and provides therefore recommendations for an adequate statistical analysis strategy and a meaningful interpretation of results. [ABSTRACT FROM AUTHOR]

Background: In healthcare research, outcomes with skewed probability distributions are common. Sample size calculations for such outcomes are typically based on estimates on a transformed scale (e.g. log) which may sometimes be difficult to obtain. In contrast, estimates of median and variance on the untransformed scale are generally easier to pre-specify. The aim of this paper is to describe how to calculate a sample size for a two group comparison of interest based on median and untransformed variance estimates for log-normal outcome data.Methods: A log-normal distribution for outcome data is assumed and a sample size calculation approach for a two-sample t-test that compares log-transformed outcome data is demonstrated where the change of interest is specified as difference in median values on the untransformed scale. A simulation study is used to compare the method with a non-parametric alternative (Mann-Whitney U test) in a variety of scenarios and the method is applied to a real example in neurosurgery.Results: The method attained a nominal power value in simulation studies and was favourable in comparison to a Mann-Whitney U test and a two-sample t-test of untransformed outcomes. In addition, the method can be adjusted and used in some situations where the outcome distribution is not strictly log-normal.Conclusions: We recommend the use of this sample size calculation approach for outcome data that are expected to be positively skewed and where a two group comparison on a log-transformed scale is planned. An advantage of this method over usual calculations based on estimates on the log-transformed scale is that it allows clinical efficacy to be specified as a difference in medians and requires a variance estimate on the untransformed scale. Such estimates are often easier to obtain and more interpretable than those for log-transformed outcomes. [ABSTRACT FROM AUTHOR]

Background: It is important to quantify the dose response for a drug in phase 2a clinical trials so the optimal doses can then be selected for subsequent late phase trials. In a phase 2a clinical trial of new lead drug being developed for the treatment of rheumatoid arthritis (RA), a U-shaped dose response curve was observed. In the light of this result further research was undertaken to design an efficient phase 2a proof of concept (PoC) trial for a follow-on compound using the lessons learnt from the lead compound.Methods: The planned analysis for the Phase 2a trial for GSK123456 was a Bayesian Emax model which assumes the dose-response relationship follows a monotonic sigmoid "S" shaped curve. This model was found to be suboptimal to model the U-shaped dose response observed in the data from this trial and alternatives approaches were needed to be considered for the next compound for which a Normal dynamic linear model (NDLM) is proposed. This paper compares the statistical properties of the Bayesian Emax model and NDLM model and both models are evaluated using simulation in the context of adaptive Phase 2a PoC design under a variety of assumed dose response curves: linear, Emax model, U-shaped model, and flat response.Results: It is shown that the NDLM method is flexible and can handle a wide variety of dose-responses, including monotonic and non-monotonic relationships. In comparison to the NDLM model the Emax model excelled with higher probability of selecting ED90 and smaller average sample size, when the true dose response followed Emax like curve. In addition, the type I error, probability of incorrectly concluding a drug may work when it does not, is inflated with the Bayesian NDLM model in all scenarios which would represent a development risk to pharmaceutical company. The bias, which is the difference between the estimated effect from the Emax and NDLM models and the simulated value, is comparable if the true dose response follows a placebo like curve, an Emax like curve, or log linear shape curve under fixed dose allocation, no adaptive allocation, half adaptive and adaptive scenarios. The bias though is significantly increased for the Emax model if the true dose response follows a U-shaped curve.Conclusions: In most cases the Bayesian Emax model works effectively and efficiently, with low bias and good probability of success in case of monotonic dose response. However, if there is a belief that the dose response could be non-monotonic then the NDLM is the superior model to assess the dose response. [ABSTRACT FROM AUTHOR]

Background: There is little published guidance as to the sample size required for a pilot or feasibility trial despite the fact that a sample size justification is a key element in the design of a trial. A sample size justification should give the minimum number of participants needed in order to meet the objectives of the trial. This paper seeks to describe the target sample sizes set for pilot and feasibility randomised controlled trials, currently running within the United Kingdom. Methods: Data were gathered from the United Kingdom Clinical Research Network (UKCRN) database using the search terms 'pilot' and 'feasibility'. From this search 513 studies were assessed for eligibility of which 79 met the inclusion criteria. Where the data summary on the UKCRN Database was incomplete, data were also gathered from: the International Standardised Randomised Controlled Trial Number (ISRCTN) register; the clinicaltrials.gov website and the website of the funders. For 62 of the trials, it was necessary to contact members of the research team by email to ensure completeness. Results: Of the 79 trials analysed, 50 (63.3%) were labelled as pilot trials, 25 (31.6%) feasibility and 14 were described as both pilot and feasibility trials. The majority had two arms (n = 68, 86.1%) and the two most common endpoints were continuous (n = 45, 57.0%) and dichotomous (n = 31, 39.2%). Pilot trials were found to have a smaller sample size per arm (median = 30, range = 8 to 114 participants) than feasibility trials (median = 36, range = 10 to 300 participants). By type of endpoint, across feasibility and pilot trials, the median sample size per arm was 36 (range = 10 to 300 participants) for trials with a dichotomous endpoint and 30 (range = 8 to 114 participants) for trials with a continuous endpoint. Publicly funded pilot trials appear to be larger than industry funded pilot trials: median sample sizes of 33 (range = 15 to 114 participants) and 25 (range = 8 to 100 participants) respectively. Conclusion: All studies should have a sample size justification. Not all studies however need to have a sample size calculation. For pilot and feasibility trials, while a sample size justification is important, a formal sample size calculation may not be appropriate. The results in this paper describe the observed sample sizes in feasibility and pilot randomised controlled trials on the UKCRN Database. [ABSTRACT FROM AUTHOR]

Background: Sensitivity analyses play a crucial role in assessing the robustness of the findings or conclusions based on primary analyses of data in clinical trials. They are a critical way to assess the impact, effect or influence of key assumptions or variations-such as different methods of analysis, definitions of outcomes, protocol deviations, missing data, and outliers-on the overall conclusions of a study. The current paper is the second in a series of tutorial-type manuscripts intended to discuss and clarify aspects related to key methodological issues in the design and analysis of clinical trials. Discussion: In this paper we will provide a detailed exploration of the key aspects of sensitivity analyses including: 1) what sensitivity analyses are, why they are needed, and how often they are used in practice; 2) the different types of sensitivity analyses that one can do, with examples from the literature; 3) some frequently asked questions about sensitivity analyses; and 4) some suggestions on how to report the results of sensitivity analyses in clinical trials. Summary: When reporting on a clinical trial, we recommend including planned or posthoc sensitivity analyses, the corresponding rationale and results along with the discussion of the consequences of these analyses on the overall findings of the study. [ABSTRACT FROM AUTHOR]

Background: We sought to understand the main ethical considerations when conducting clinical trials in the prehospital ambulance based setting.Methods: A systematic review of the literature on randomised controlled trials in ambulance settings was undertaken. A search of eight databases identified published studies involving recruitment of ambulance service users. Four independent authors undertook abstract and full-text reviews to determine eligibility and extract relevant data. The data extraction concentrated on ethical considerations, with any discussion of ethics being included for further analysis. The resultant data were combined to form a narrative synthesis.Results: In all, 56 papers were identified as meeting the inclusion criteria. Issues relating to consent were the most significant theme identified. Type of consent differed depending on the condition or intervention being studied. The country in which the research took place did not appear to influence the type of consent, apart from the USA where exception from consent appeared to be most commonly used. A wide range of terms were used to describe consent.Conclusions: Consent was the main ethical consideration in published ambulance based research. A range of consent models were used ranging from informed consent to exception from consent (waiver of consent). Many studies cited international guidelines as informing their choice of consent model but diverse and sometimes confused terms were used to describe these models. This suggests that standardisation of consent models and the terminology used to describe them is warranted. [ABSTRACT FROM AUTHOR]

Background: The goal of medical research is to develop interventions that are in some sense superior, with respect to patient outcome, to interventions currently in use. Similarly, the goal of research in methodological computational statistics is to develop data analysis tools that are themselves superior to the existing tools. The methodology of the evaluation of medical interventions continues to be discussed extensively in the literature and it is now well accepted that medicine should be at least partly "evidence-based". Although we statisticians are convinced of the importance of unbiased, well-thought-out study designs and evidence-based approaches in the context of clinical research, we tend to ignore these principles when designing our own studies for evaluating statistical methods in the context of our methodological research.Main Message: In this paper, we draw an analogy between clinical trials and real-data-based benchmarking experiments in methodological statistical science, with datasets playing the role of patients and methods playing the role of medical interventions. Through this analogy, we suggest directions for improvement in the design and interpretation of studies which use real data to evaluate statistical methods, in particular with respect to dataset inclusion criteria and the reduction of various forms of bias. More generally, we discuss the concept of "evidence-based" statistical research, its limitations and its impact on the design and interpretation of real-data-based benchmark experiments.Conclusion: We suggest that benchmark studies-a method of assessment of statistical methods using real-world datasets-might benefit from adopting (some) concepts from evidence-based medicine towards the goal of more evidence-based statistical research. [ABSTRACT FROM AUTHOR]

Background: The Matchpoint trial aims to identify the optimal dose of ponatinib to give with conventional chemotherapy consisting of fludarabine, cytarabine and idarubicin to chronic myeloid leukaemia patients in blastic transformation phase. The dose should be both tolerable and efficacious. This paper describes our experience implementing EffTox in the Matchpoint trial.Methods: EffTox is a Bayesian adaptive dose-finding trial design that jointly scrutinises binary efficacy and toxicity outcomes. We describe a nomenclature for succinctly describing outcomes in phase I/II dose-finding trials. We use dose-transition pathways, where doses are calculated for each feasible set of outcomes in future cohorts. We introduce the phenomenon of dose ambivalence, where EffTox can recommend different doses after observing the same outcomes. We also describe our experiences with outcome ambiguity, where the categorical evaluation of some primary outcomes is temporarily delayed.Results: We arrived at an EffTox parameterisation that is simulated to perform well over a range of scenarios. In scenarios where dose ambivalence manifested, we were guided by the dose-transition pathways. This technique facilitates planning, and also helped us overcome short-term outcome ambiguity.Conclusions: EffTox is an efficient and powerful design, but not without its challenges. Joint phase I/II clinical trial designs will likely become increasingly important in coming years as we further investigate non-cytotoxic treatments and streamline the drug approval process. We hope this account of the problems we faced and the solutions we used will help others implement this dose-finding clinical trial design.Trial Registration: Matchpoint was added to the European Clinical Trials Database ( https://www.clinicaltrialsregister.eu/ctr-search/trial/2012-005629-65/GB ) on 2013-12-30. [ABSTRACT FROM AUTHOR]

Background: The sample size required to power a study to a nominal level in a paired comparative diagnostic accuracy study, i.e. studies in which the diagnostic accuracy of two testing procedures is compared relative to a gold standard, depends on the conditional dependence between the two tests - the lower the dependence the greater the sample size required. A priori, we usually do not know the dependence between the two tests and thus cannot determine the exact sample size required. One option is to use the implied sample size for the maximal negative dependence, giving the largest possible sample size. However, this is potentially wasteful of resources and unnecessarily burdensome on study participants as the study is likely to be overpowered. A more accurate estimate of the sample size can be determined at a planned interim analysis point where the sample size is re-estimated.Methods: This paper discusses a sample size estimation and re-estimation method based on the maximum likelihood estimates, under an implied multinomial model, of the observed values of conditional dependence between the two tests and, if required, prevalence, at a planned interim. The method is illustrated by comparing the accuracy of two procedures for the detection of pancreatic cancer, one procedure using the standard battery of tests, and the other using the standard battery with the addition of a PET/CT scan all relative to the gold standard of a cell biopsy. Simulation of the proposed method illustrates its robustness under various conditions.Results: The results show that the type I error rate of the overall experiment is stable using our suggested method and that the type II error rate is close to or above nominal. Furthermore, the instances in which the type II error rate is above nominal are in the situations where the lowest sample size is required, meaning a lower impact on the actual number of participants recruited.Conclusion: We recommend multinomial model maximum likelihood estimation of the conditional dependence between paired diagnostic accuracy tests at an interim to reduce the number of participants required to power the study to at least the nominal level.Trial Registration: ISRCTN ISRCTN73852054 . Registered 9th of January 2015. Retrospectively registered. [ABSTRACT FROM AUTHOR]

Background: With the increasing implementation of web-based, mobile health interventions in clinical trials, it is crucial for researchers to address the security and privacy concerns of patient information according to high ethical standards. The full process of meeting these standards is often made more complicated due to the use of internet-based technology and smartphones for treatment, telecommunication, and data collection; however, this process is not well-documented in the literature.Results: The Smart Heart Trial is a single-arm feasibility study that is currently assessing the effects of a web-based, mobile lifestyle intervention for overweight and obese children and youth with congenital heart disease in Southwestern Ontario. Participants receive telephone counseling regarding nutrition and fitness; and complete goal-setting activities on a web-based application. This paper provides a detailed overview of the challenges the study faced in meeting the high standards of our Research Ethics Board, specifically regarding patient privacy.Conclusion: We outline our solutions, successes, limitations, and lessons learned to inform future similar studies; and model much needed transparency in ensuring high quality security and protection of patient privacy when using web-based and mobile devices for telecommunication and data collection in clinical research. [ABSTRACT FROM AUTHOR]

Background: The most rigorous method for evaluating the effectiveness of diagnostic tests is through randomised trials that compare test-treatment interventions: complex interventions comprising episodes of testing, decision-making and treatment. The multi-staged nature of these interventions, combined with the need to relay diagnostic decision-making and treatment planning, has led researchers to hypothesise that test-treatment strategies may be very challenging to document. However, no reviews have yet examined the reporting quality of interventions used in test-treatment RCTs. In this study we evaluate the completeness of intervention descriptions in a systematically identified cohort of test-treatment RCTs.Methods: We ascertained all test-treatment RCTs published 2004-2007, indexed in CENTRAL. Included trials randomized patients to diagnostic tests and measured patient outcomes after treatment. Two raters examined the completeness of test-treatment intervention descriptions in four components: 1) the test, 2) diagnostic decision-making, 3) management decision-making, 4) treatments.Results: One hundred and three trials compared 105 control with 119 experimental interventions, most commonly in cardiovascular medicine (35, 34%), obstetrics and gynecology (17%), gastroenterology (14%) or orthopedics (10%). A broad range of tests were evaluated, including imaging (50, 42%), biochemical assays (21%) and clinical assessment (12%). Only five (5%) trials detailed all four components of experimental and control interventions, none of which also provided a complete care pathway diagram. Experimental arms were missing descriptions of tests, diagnostic-decision making, management planning and treatments (36%, 51%, 55% and 79% of trials respectively); control arms were missing the same details in 61%, 66%, 67% and 84% of trials.Conclusion: Reporting of test-treatment interventions is very poor, inadequate for understanding the results of these trials, and for comparing or translating results into clinical practice. Reporting needs to improve, with greater emphasis on describing the decision-making components of care pathways in both pragmatic and explanatory trials. Please see the companion paper to this article: http://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-016-0287-z . [ABSTRACT FROM AUTHOR]

Background: There is a growing recognition for the need to expand our evidence base for the clinical effectiveness of diagnostic tests. Many international bodies are calling for diagnostic randomized controlled trials to provide the most rigorous evidence of impact to patient health. Although these so-called test-treatment RCTs are very challenging to undertake due to their methodological complexity, they have not been subjected to a systematic appraisal of their methodological quality. The extent to which these trials may be producing biased results therefore remains unknown. We set out to address this issue by conducting a methodological review of published test-treatment trials to determine how often they implement adequate methods to limit bias and safeguard the validity of results.Methods: We ascertained all test-treatment RCTs published 2004-2007, indexed in CENTRAL, including RCTs which randomized patients to diagnostic tests and measured patient outcomes after treatment. Tests used for screening, monitoring or prognosis were excluded. We assessed adequacy of sequence generation, allocation concealment and intention-to-treat, appropriateness of primary analyses, blinding and reporting of power calculations, and extracted study characteristics including the primary outcome.Results: One hundred three trials compared 105 control with 119 experimental interventions, and reported 150 primary outcomes. Randomization and allocation concealment were adequate in 57 and 37% of trials. Blinding was uncommon (patients 5%, clinicians 4%, outcome assessors 21%), as was an adequate intention-to-treat analysis (29%). Overall 101 of 103 trials (98%) were at risk of bias, as judged using standard Cochrane criteria.Conclusion: Test-treatment trials are particularly susceptible to attrition and inadequate primary analyses, lack of blinding and under-powering. These weaknesses pose much greater methodological and practical challenges to conducting reliable RCT evaluations of test-treatment strategies than standard treatment interventions. We suggest a cautious approach that first examines whether a test-treatment intervention can accommodate the methodological safeguards necessary to minimize bias, and highlight that test-treatment RCTs require different methods to ensure reliability than standard treatment trials. Please see the companion paper to this article: http://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-016-0286-0 . [ABSTRACT FROM AUTHOR]

Background: In the two-stage randomised trial design, a randomly sampled subset of study participants are permitted to choose their own treatment, while the remaining participants are randomised to treatment in the usual way. Appropriate analysis of the data from both arms of the study allows investigators to estimate the impact on study outcomes of treatment preferences that patients may have, in addition to evaluating the usual direct effect of treatment. In earlier work, we showed how to optimise this design by making a suitable choice of the proportion of participants who should be assigned to the choice arm of the trial. However, we ignored the possibility of some participants being indifferent to the treatments under study. In this paper, we extend our earlier work to consider the analysis of two-stage randomised trials when some participants have no treatment preference, even if they are assigned to the choice arm and allowed to choose.Methods: We compare alternative characterisations of the response profiles of the indifferent or undecided participants, and derive estimates of the treatment and preference effects on study outcomes. We also present corresponding test statistics for these parameters. The methods are illustrated with data from a clinical trial contrasting medical and surgical interventions.Results: Expressions are obtained to estimate and test the impact of treatment choices on study outcomes, as well as the impact of the actual treatment received. Contrasts are defined between patients with stated treatment preferences and those with no preference. Alternative assumptions concerning the outcomes of undecided participants are described, and an approach leading to unbiased estimation and testing is identified.Conclusions: Use of the two-stage design can provide important insights into determinants of study outcomes that are not identifiable with other designs. The design can remain attractive even in the presence of participants with no stated treatment preference. [ABSTRACT FROM AUTHOR]

The article presents information on a study which examines published pilot/feasibility trials incorporating random allocation, analyzes their aims, results and conclusions drawn, and compares drug and non-drug trials. It used EMBASE and MEDLINE databases and selected a random sample of 50 papers. It was found that most of the papers focused on efficacy with drug trials also focusing on safety and toxicity.

Background: Log-linear association models have been extensively used to investigate the pattern of agreement between ordinal ratings. In 2007, log-linear non-uniform association models were introduced to estimate, from a cross-classification of two independent raters using an ordinal scale, varying degrees of distinguishability between distant and adjacent categories of the scale. Methods: In this paper, a simple method based on simulations was proposed to estimate the power of non-uniform association models to detect heterogeneities across distinguishabilities between adjacent categories of an ordinal scale, illustrating some possible scale defects. Results: Different scenarios of distinguishability patterns were investigated, as well as different scenarios of marginal heterogeneity within rater. For sample size of N = 50, the probabilities of detecting heterogeneities within the tables are lower than .80, whatever the number of categories. In additition, even for large samples, marginal heterogeneities within raters led to a decrease in power estimates. Conclusion: This paper provided some issues about how many objects had to be classified by two independent observers (or by the same observer at two different times) to be able to detect a given scale structure defect. Our results also highlighted the importance of marginal homogeneity within raters, to ensure optimal power when using non-uniform association models. [ABSTRACT FROM AUTHOR]

Background: The success of a human intervention trial depends upon the ability to recruit eligible volunteers. Many trials fail because of unrealistic recruitment targets and flawed recruitment strategies. In order to predict recruitment rates accurately, researchers need information on the relative success of various recruitment strategies. Few published trials include such information and the number of participants screened or approached is not always cited. Methods: This paper will describe in detail the recruitment strategies employed to identify older adults for recruitment to a 6-month randomised controlled dietary intervention trial which aimed to explore the relationship between diet and immune function (The FIT study). The number of people approached and recruited, and the reasons for exclusion, will be discussed. Results: Two hundred and seventeen participants were recruited to the trial. A total of 7,482 letters were sent to potential recruits using names and addresses that had been supplied by local Family (General) Practices. Eight hundred and forty three potential recruits replied to all methods of recruitment (528 from GP letters and 315 from other methods). The eligibility of those who replied was determined using a screening telephone interview, 217 of whom were found to be suitable and agreed to take part in the study. Conclusion: The study demonstrates the application of multiple recruitment methods to successfully recruit older people to a randomised controlled trial. The most successful recruitment method was by contacting potential recruits by letter on NHS headed note paper using contacts provided from General Practices. Ninety percent of recruitment was achieved using this method. Adequate recruitment is fundamental to the success of a research project, and appropriate strategies must therefore be adopted in order to identify eligible individuals and achieve recruitment targets. [ABSTRACT FROM AUTHOR]

Background: The most commonly used survey methods are self-administered questionnaires, telephone interviews, and a mixture of both. But until now evidence out of randomised controlled trials as to whether patient responses differ depending on the survey mode is lacking. Therefore this study assessed whether patient responses to surveys depend on the mode of survey administration. The comparison was between mailed, self-administered questionnaires and telephone interviews. Methods: A four-armed, randomised controlled two-period change-over design. Each patient responded to the same survey twice, once in written form and once by telephone interview, separated by at least a fortnight. The study was conducted in 2003/2004 in Germany. 1087 patients taking part in the German Acupuncture Trials (GERAC cohort study), who agreed to participate in a survey after completing acupuncture treatment from an acupuncture-certified family physician for headache, were randomised. Of these, 823 (664 women) from the ages of 18 to 83 (mean 51.7) completed both parts of the study. The main outcome measure was the comparison of the scores on the 12-Item Short-Form Health Survey (SF-12) and the Graded Chronic Pain Scale (GCPS) questionnaire for the two survey modes. Results: Computer-aided telephone interviews (CATI) resulted in significantly fewer missing data (0.5%) than did mailed questionnaires (2.8%; p < 0.001). The analysis of equivalence revealed a difference between the survey modes only for the SF-12 mental scales. On average, reported mental status score was 3.5 score points (2.9 to 4.0) lower on the self-administered questionnaire compared to the telephone interview. The order of administration affected results. Patients who responded to the telephone interview first reported better mental health in the subsequent paper questionnaire (mean difference 2.8 score points) compared to those who responded to the paper questionnaire first (mean difference 4.1 score points). Conclusion: Despite the comparatively high cost of telephone interviews, they offer clear advantages over mailed self-administered questionnaires as regards completeness of data. Only items concerning mental status were dependent on the survey mode and sequence of administration. Items on physical status were not affected. Normative data for standardized telephone questionnaires could contribute to a better comparability with the results of the corresponding standardized paper questionnaires. [ABSTRACT FROM AUTHOR]

Background: Several reviews of published cluster randomised trials have reported that about half did not take clustering into account in the analysis, which was thus incorrect and potentially misleading. In this paper I ask whether cluster randomised trials are increasing in both number and quality of reporting. Methods: Computer search for papers on cluster randomised trials since 1980, hand search of trial reports published in selected volumes of the British Medical Journal over 20 years. Results: There has been a large increase in the numbers of methodological papers and of trial reports using the term 'cluster random' in recent years, with about equal numbers of each type of paper. The British Medical Journal contained more such reports than any other journal. In this journal there was a corresponding increase over time in the number of trials where subjects were randomised in clusters. In 2003 all reports showed awareness of the need to allow for clustering in the analysis. In 1993 and before clustering was ignored in most such trials. Conclusion: Cluster trials are becoming more frequent and reporting is of higher quality. Perhaps statistician pressure works. [ABSTRACT FROM AUTHOR]

Background: Two main pathways exist for the development of knowledge in clinical homeopathy. These comprise clinical trials conducted primarily by university-based researchers and cases reports and homeopathic "provings" compiled by engaged homeopathic practitioners. In this paper the relative merits of these methods are examined and a middle way proposed. This consists of the "Formal Case Study" (FCS) in which qualitative methods are used to increase the rigour and sophistication with which homeopathic cases are studied. Before going into design issues this paper places the FCS in an historical and academic context and describes the relative merits of the method. Discussion: Like any research, the FCS should have a clear focus. This focus can be both "internal", grounded in the discourse of homeopathy and also encompass issues of wider appeal. A selection of possible "internal" and "external" research questions is introduced. Data generation should be from multiple sources to ensure adequate triangulation. This could include the recording and transcription of actual consultations. Analysis is built around existing theory, involves cross-case comparison and the search for deviant cases. The trustworthiness of conclusions is ensured by the application of concepts from qualitative research including triangulation, groundedness, respondent validation and reflexivity. Though homeopathic case studies have been reported in mainstream literature, none has used formal qualitative methods -- though some such studies are in progress. Summary: This paper introduces the reader to a new strategy for homeopathic research. This strategy, termed the "formal case study", allows for a naturalistic enquiry into the players, processes and outcomes of homeopathic practice. Using ideas from qualitative research, it allows a rigorous approach to types of research question that cannot typically be addressed through clinical trials and numeric outcome studies. The FCS provides an opportunity for the practitioner-researcher to contribute to the evidence-base in homeopathy in a systematic fashion. The FCS can also be used to inform the design of clinical trials through holistic study of the "active ingredients" of the therapeutic process and its clinical outcomes. [ABSTRACT FROM AUTHOR]

Background: A systematic review on the use of incentives to promote questionnaire return in clinical trials suggest they are effective, but not all studies have sufficient funds to use them. Promising an incentive once data are returned can reduce the cost-burden of this approach, with possible further cost-savings if the offer were restricted to reminder letters only. This study aimed to evaluate the effect of promising a monetary incentive at first mailout versus a promise on reminder letters only.Methods: This was a randomised Study Within A Trial (SWAT) nested within BUMPES, a multicentre randomised controlled trial of maternal position in the late stage of labour in women with an epidural. The follow-up questionnaire asked for information on the women's health, wellbeing and health service use one year following the birth of their baby. Women who consented to be contacted were randomised to a promise of a monetary incentive at first mailout or a promise on reminder letters only. Women were given an option of completing the questionnaire on paper or on online. The incentive was posted out on receipt of a completed questionnaire. The primary outcome was the overall return rate, and secondary outcomes were the return rate without any chasing from the study office, and the total cost of the vouchers.Results: A total of 1,029 women were randomised, 508 to the first mailout group and 518 to the reminder group. There was no evidence to suggest a difference between groups in the overall return rate (adjusted RR 1.03 (95 % CI 0.96 to 1.11), however the proportion returned without chasing was higher in the first mailout group (adjusted RR 1.22, 95 % CI 1.07 to 1.39). The total cost of the vouchers per participant was higher in the first mailout group (mean difference £4.56, 95 % CI £4.02 to £5.11).Conclusions: Offering a monetary incentive when a reminder is required could be cost-effective depending on the sample size of the study and the resources available to administer the reminder letters.Trial Registration: The BUMPES Trial is registered with Current Controlled Trials: ISRCTN35706297 , 26(th) August 2009. [ABSTRACT FROM AUTHOR]

Background: Previous reviews have focussed on the rationale for employing the stepped wedge design (SWD), the areas of research to which the design has been applied and the general characteristics of the design. However these did not focus on the statistical methods nor addressed the appropriateness of sample size methods used.This was a review of the literature of the statistical methodology used in stepped wedge cluster randomised trials.Methods: Literature Review. The Medline, Embase, PsycINFO, CINAHL and Cochrane databases were searched for methodological guides and RCTs which employed the stepped wedge design.Results: This review identified 102 trials which employed the stepped wedge design compared to 37 from the most recent review by Beard et al. 2015. Forty six trials were cohort designs and 45 % (n = 46) had fewer than 10 clusters. Of the 42 articles discussing the design methodology 10 covered analysis and seven covered sample size. For cohort stepped wedge designs there was only one paper considering analysis and one considering sample size methods. Most trials employed either a GEE or mixed model approach to analysis (n = 77) but only 22 trials (22 %) estimated sample size in a way which accounted for the stepped wedge design that was subsequently used.Conclusions: Many studies which employ the stepped wedge design have few clusters but use methods of analysis which may require more clusters for unbiased and efficient intervention effect estimates. There is the need for research on the minimum number of clusters required for both types of stepped wedge design. Researchers should distinguish in the sample size calculation between cohort and cross sectional stepped wedge designs. Further research is needed on the effect of adjusting for the potential confounding of time on the study power. [ABSTRACT FROM AUTHOR]

Background: Chalmers and Glasziou's paper published in 2014 recommends research funding bodies should mandate that proposals for additional primary research are built on systematic reviews of existing evidence showing what is already known. Jones et al. identified 11 (23%) of 48 trials funded during 2006-8 by the National Institute for Health Research Health Technology Assessment (NIHR HTA) Programme did not reference a systematic review. This study did not explore the reasons for trials not referencing a systematic review or consider trials using any other evidence in the absence of a systematic review. Referencing a systematic review may not be possible in certain circumstances, for instance if the systematic review does not address the question being proposed in the trial. The current study extended Jones' study by exploring the reasons for why trials did not reference a systematic review and included a more recent cohort of trials funded in 2013 to determine if there were any changes in the referencing or use of systematic reviews.Methods: Two cohorts of NIHR HTA randomised controlled trials were included. Cohort I included the same trials as Jones et al. (with the exception of one trial which was discontinued). Cohort II included NIHR HTA trials funded in 2013. Data extraction was undertaken independently by two reviewers using full applications and trial protocols. Descriptive statistics was used and no formal statistical analyses were conducted.Results: Five (11%) trials of the 47 funded during 2006-2008 did not reference a systematic review. These 5 trials had warranted reasons for not referencing systematic reviews. All trials from Cohort II referenced a systematic review. A quarter of all those trials with a preceding systematic review used a different primary outcome than those stated in the reviews.Conclusions: The NIHR requires that proposals for new primary research are justified by existing evidence and the findings of this study confirm the adherence to this requirement with a high rate of applications using systematic reviews. [ABSTRACT FROM AUTHOR]

Background: Cut-point finding is a crucial step for clinical decision making when dealing with diagnostic (or prognostic) biomarkers. The extension of ROC-based cut-point finding methods to the case of censored failure time outcome is of interest when we are in the presence of a biomarker, measured at baseline, used to identify whether there will be the development, or not, of some disease condition within a given time point τ of clinical interest. Methods: Three widely used cut-point finding methods, namely the Youden index, the concordance probability and the point closest to-(0,1) corner in the ROC plane, are extended to the case of censored failure time outcome resorting to non-parametric estimators of the sensitivity and specificity that account for censoring. The performance of these methods in finding the optimal cut-point is compared under Normal and Gamma distributions of the biomarker (in subjects developing or not the disease condition). Normality ensures that estimators point theoretically to the same cut-point. Two motivating examples are provided in the paper. Results: The point closest-to-(0,1) corner approach has the best performance from simulations in terms of mean square error and relative bias. Conclusions: We discuss the use of the Youden index or concordance probability associated to the cut-point identified through the closest-to-(0,1) corner approach to ease interpretability of the classification performance of the dichotomized biomarker. In addition, the achieved performance of the dichotomized biomarker classification associated to the estimated cut-point can be represented through a confidence interval of the point on the ROC curve. [ABSTRACT FROM AUTHOR]

Background: Recent evidence suggests that there is often substantial variation in the benefits and harms across a trial population. We aimed to identify regression modeling approaches that assess heterogeneity of treatment effect within a randomized clinical trial.Methods: We performed a literature review using a broad search strategy, complemented by suggestions of a technical expert panel.Results: The approaches are classified into 3 categories: 1) Risk-based methods (11 papers) use only prognostic factors to define patient subgroups, relying on the mathematical dependency of the absolute risk difference on baseline risk; 2) Treatment effect modeling methods (9 papers) use both prognostic factors and treatment effect modifiers to explore characteristics that interact with the effects of therapy on a relative scale. These methods couple data-driven subgroup identification with approaches to prevent overfitting, such as penalization or use of separate data sets for subgroup identification and effect estimation. 3) Optimal treatment regime methods (12 papers) focus primarily on treatment effect modifiers to classify the trial population into those who benefit from treatment and those who do not. Finally, we also identified papers which describe model evaluation methods (4 papers).Conclusions: Three classes of approaches were identified to assess heterogeneity of treatment effect. Methodological research, including both simulations and empirical evaluations, is required to compare the available methods in different settings and to derive well-informed guidance for their application in RCT analysis. [ABSTRACT FROM AUTHOR]

Background Controversies are common in medicine. Some arise when the conclusions of research publications directly contradict each other, creating uncertainty for frontline clinicians. Discussion In this paper, we review how researchers can look at very similar data yet have completely different conclusions based purely on an over-reliance of statistical significance and an unclear understanding of confidence intervals. The dogmatic adherence to statistical significant thresholds can lead authors to write dichotomized absolute conclusions while ignoring the broader interpretations of very consistent findings. We describe three examples of controversy around the potential benefit of a medication, a comparison between new medications, and a medication with a potential harm. The examples include the highest levels of evidence, both meta-analyses and randomized controlled trials. We will show how in each case the confidence intervals and point estimates were very similar. The only identifiable differences to account for the contrasting conclusions arise from the serendipitous finding of confidence intervals that either marginally cross or just fail to cross the line of statistical significance. Summary These opposing conclusions are false disagreements that create unnecessary clinical uncertainty. We provide helpful recommendations in approaching conflicting conclusions when they are associated with remarkably similar results. [ABSTRACT FROM AUTHOR]

Background: Numerous studies on publication bias in clinical drug research have been undertaken, particularly on the association between sponsorship and favourable outcomes. However, no standardized methodology for the classification of outcomes and sponsorship has been described. Dissimilarities and ambiguities in this assessment impede the ability to compare and summarize results of studies on publication bias. To guide authors undertaking such studies, this paper provides recommendations for a uniform assessment of publication bias related to funding source. Methods and results: As part of ongoing research into publication bias, 472 manuscripts on randomised controlled trials (RCTs) with drugs, submitted to eight medical journals from January 2010 through April 2012, were reviewed. Information on trial results and sponsorship was extracted from manuscripts. During the start of this evaluation, several problems related to the classification of outcomes, inclusion of post-hoc analyses and follow-up studies of RCTs in the study sample, and assessment of the role of the funding source were encountered. A comprehensive list of recommendations addressing these problems was composed. To assess internal validity, reliability and usability of these recommendations were tested through evaluation of manuscripts submitted to journals included in our study. Conclusions: The proposed recommendations represent a first step towards a uniform method of classifying trial outcomes and sponsorship. This is essential to draw valid conclusions on the role of the funding source in publication bias and will ensure consistency across future studies. [ABSTRACT FROM AUTHOR]

Background: Monitoring and reporting of drug safety during a clinical trial is essential to its success. More recent attention to drug safety has encouraged statistical methods development for monitoring and detecting potential safety signals. This paper investigates the potential impact of the process of the blinded investigator identifying a potential safety signal, which should be further investigated by the Data and Safety Monitoring Board with an unblinded safety data analysis.Methods: In this paper, two-stage Bayesian hierarchical models are proposed for safety signal detection following a pre-specified set of interim analyses that are applied to efficacy. At stage 1, a hierarchical blinded model uses blinded safety data to detect a potential safety signal and at stage 2, a hierarchical logistic model is applied to confirm the signal with unblinded safety data.Results: Any interim safety monitoring analysis is usually scheduled via negotiation between the trial sponsor and the Data and Safety Monitoring Board. The proposed safety monitoring process starts once 53 subjects have been enrolled into an eight-arm phase II clinical trial for the first interim analysis. Operating characteristics describing the performance of this proposed workflow are investigated using simulations based on the different scenarios.Conclusions: The two-stage Bayesian safety procedure in this paper provides a statistical view to monitor safety during the clinical trials. The proposed two-stage monitoring model has an excellent accuracy of detecting and flagging a potential safety signal at stage 1, and with the most important feature that further action at stage 2 could confirm the safety issue. [ABSTRACT FROM AUTHOR]

Background The UK leads the world in recruitment of patients to cancer clinical trials, with a six-fold increase in recruitment during 2001-2010. However, there are large variations across cancer centres. This paper details recruitment to a large multi-centre prospective cohort study and discusses lessons learnt to enhance recruitment. Methods During CREW (ColoREctal Wellbeing) cohort study set up and recruitment, data were systematically collected on all centres that applied to participate, time from study approval to first participant recruited and the percentage of eligible patients recruited into the study. Results 30 participating NHS cancer centres were selected through an open competition via the cancer networks. Time from study approval to first participant recruited took a median 124 days (min 53, max 290). Of 1350 eligible people in the study time frame, 78% (n = 1056) were recruited into the study, varying from 30-100% eligible across centres. Recruitment of 1056 participants took 17 months. Conclusion In partnership with the National Cancer Research Network, this successful study prioritised relationship building and education. Key points for effective recruitment: pre-screening and selection of centres; nurses as PIs; attendance at study days; frequent communication and a reduced level of consent to enhance uptake amongst underrepresented groups. [ABSTRACT FROM AUTHOR]