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In clinical settings with no commonly accepted standard-of-care, multiple treatment regimens are potentially useful, but some treatments may not be appropriate for some patients. A personalized randomized controlled trial (PRACTical) design has been proposed for this setting. For a network of treatments, each patient is randomized only among treatments which are appropriate for them. The aim is to produce treatment rankings that can inform clinical decisions about treatment choices for individual patients. Here we propose methods for determining sample size in a PRACTical design, since standard power-based methods are not applicable. We derive a sample size by evaluating information gained from trials of varying sizes. For a binary outcome, we quantify how many adverse outcomes would be prevented by choosing the top-ranked treatment for each patient based on trial results rather than choosing a random treatment from the appropriate personalized randomization list. In simulations, we evaluate three performance measures: mean reduction in adverse outcomes using sample information, proportion of simulated patients for whom the top-ranked treatment performed as well or almost as well as the best appropriate treatment, and proportion of simulated trials in which the top-ranked treatment performed better than a randomly chosen treatment. We apply the methods to a trial evaluating eight different combination antibiotic regimens for neonatal sepsis (NeoSep1), in which a PRACTical design addresses varying patterns of antibiotic choice based on disease characteristics and resistance. Our proposed approach produces results that are more relevant to complex decision making by clinicians and policy makers., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Background: Older people admitted to hospital in an emergency often have prolonged inpatient stays that worsen their outcomes, increase health-care costs, and reduce bed availability. Growing evidence suggests that the biopsychosocial complexity of their problems, which include cognitive impairment, depression, anxiety, multiple medical illnesses, and care needs resulting from functional dependency, prolongs hospital stays by making medical treatment less efficient and the planning of post-discharge care more difficult. We aimed to assess the effects of enhancing older inpatients' care with Proactive Integrated Consultation-Liaison Psychiatry (PICLP) in The HOME Study. We have previously described the benefits of PICLP reported by patients and clinicians. In this Article, we report the effectiveness and cost-effectiveness of PICLP-enhanced care, compared with usual care alone, in reducing time in hospital., Methods: We did a parallel-group, multicentre, randomised controlled trial in 24 medical wards of three English acute general hospitals. Patients were eligible to take part if they were 65 years or older, had been admitted in an emergency, and were expected to remain in hospital for at least 2 days from the time of enrolment. Participants were randomly allocated to PICLP or usual care in a 1:1 ratio by a database software algorithm that used stratification by hospital, sex, and age, and randomly selected block sizes to ensure allocation concealment. PICLP clinicians (consultation-liaison psychiatrists supported by assisting clinicians) made proactive biopsychosocial assessments of patients' problems, then delivered discharge-focused care as integrated members of ward teams. The primary outcome was time spent as an inpatient (during the index admission and any emergency readmissions) in the 30 days post-randomisation. Secondary outcomes were the rate of discharge from hospital for the total length of the index admission; discharge destination; the length of the index admission after random allocation truncated at 30 days; the number of emergency readmissions to hospital, the number of days spent as an inpatient in an acute general hospital, and the rate of death in the year after random allocation; the patient's experience of the hospital stay; their view on the length of the hospital stay; anxiety (Generalized Anxiety Disorder-2); depression (Patient Health Questionnaire-2); cognitive function (Montreal Cognitive Assessment-Telephone version); independent functioning (Barthel Index of Activities of Daily Living); health-related quality of life (five-level EuroQol five-dimension questionnaire); and overall quality of life. Statisticians and data collectors were masked to treatment allocation; participants and ward staff could not be. Analyses were intention-to-treat. The trial had a patient and public involvement panel and was registered with ISRTCN (ISRCTN86120296)., Findings: 2744 participants (1399 [51·0%] male and 1345 [49·0%] female) were enrolled between May 2, 2018, and March 5, 2020; 1373 were allocated to PICLP and 1371 to usual care. Participants' mean age was 82·3 years (SD 8·2) and 2565 (93·5%) participants were White. The mean time spent in hospital in the 30 days post-randomisation (analysed for 2710 [98·8%] participants) was 11·37 days (SD 8·74) with PICLP and 11·85 days (SD 9·00) with usual care; adjusted mean difference -0·45 (95% CI -1·11 to 0·21; p=0·18). The only statistically and clinically significant difference in secondary outcomes was the rate of discharge, which was 8.5% higher (rate ratio 1·09 [95% CI 1·00 to 1·17]; p=0·042) with PICLP-a difference most apparent in patients who stayed for more than 2 weeks. Compared with usual care, PICLP was estimated to be modestly cost-saving and cost-effective over 1 and 3, but not 12, months. No intervention-related serious adverse events occurred., Interpretation: This is the first randomised controlled trial of PICLP. PICLP is experienced by older medical inpatients and ward staff as enhancing medical care. It is also likely to be cost-saving in the short-term. Although the trial does not provide strong evidence that PICLP reduces time in hospital, it does support and inform its future development and evaluation., Funding: UK National Institute for Health and Care Research., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The estimands framework outlined in ICH E9 (R1) describes the components needed to precisely define the effects to be estimated in clinical trials, which includes how post-baseline 'intercurrent' events (IEs) are to be handled. In late-stage clinical trials, it is common to handle IEs like 'treatment discontinuation' using the treatment policy strategy and target the treatment effect on outcomes regardless of treatment discontinuation. For continuous repeated measures, this type of effect is often estimated using all observed data before and after discontinuation using either a mixed model for repeated measures (MMRM) or multiple imputation (MI) to handle any missing data. In basic form, both these estimation methods ignore treatment discontinuation in the analysis and therefore may be biased if there are differences in patient outcomes after treatment discontinuation compared with patients still assigned to treatment, and missing data being more common for patients who have discontinued treatment. We therefore propose and evaluate a set of MI models that can accommodate differences between outcomes before and after treatment discontinuation. The models are evaluated in the context of planning a Phase 3 trial for a respiratory disease. We show that analyses ignoring treatment discontinuation can introduce substantial bias and can sometimes underestimate variability. We also show that some of the MI models proposed can successfully correct the bias, but inevitably lead to increases in variance. We conclude that some of the proposed MI models are preferable to the traditional analysis ignoring treatment discontinuation, but the precise choice of MI model will likely depend on the trial design, disease of interest and amount of observed and missing data following treatment discontinuation., (© 2024 The Author(s). Pharmaceutical Statistics published by John Wiley & Sons Ltd.)

Background: Risk prediction models are routinely used to assist in clinical decision making. A small sample size for model development can compromise model performance when the model is applied to new patients. For binary outcomes, the calibration slope (CS) and the mean absolute prediction error (MAPE) are two key measures on which sample size calculations for the development of risk models have been based. CS quantifies the degree of model overfitting while MAPE assesses the accuracy of individual predictions., Methods: Recently, two formulae were proposed to calculate the sample size required, given anticipated features of the development data such as the outcome prevalence and c-statistic, to ensure that the expectation of the CS and MAPE (over repeated samples) in models fitted using MLE will meet prespecified target values. In this article, we use a simulation study to evaluate the performance of these formulae., Results: We found that both formulae work reasonably well when the anticipated model strength is not too high (c-statistic < 0.8), regardless of the outcome prevalence. However, for higher model strengths the CS formula underestimates the sample size substantially. For example, for c-statistic = 0.85 and 0.9, the sample size needed to be increased by at least 50% and 100%, respectively, to meet the target expected CS. On the other hand, the MAPE formula tends to overestimate the sample size for high model strengths. These conclusions were more pronounced for higher prevalence than for lower prevalence. Similar results were drawn when the outcome was time to event with censoring. Given these findings, we propose a simulation-based approach, implemented in the new R package 'samplesizedev', to correctly estimate the sample size even for high model strengths. The software can also calculate the variability in CS and MAPE, thus allowing for assessment of model stability., Conclusions: The calibration and MAPE formulae suggest sample sizes that are generally appropriate for use when the model strength is not too high. However, they tend to be biased for higher model strengths, which are not uncommon in clinical risk prediction studies. On those occasions, our proposed adjustments to the sample size calculations will be relevant., (© 2024. The Author(s).)

Targeted maximum likelihood estimation (TMLE) is increasingly used for doubly robust causal inference, but how missing data should be handled when using TMLE with data-adaptive approaches is unclear. Based on data (1992-1998) from the Victorian Adolescent Health Cohort Study, we conducted a simulation study to evaluate 8 missing-data methods in this context: complete-case analysis, extended TMLE incorporating an outcome-missingness model, the missing covariate missing indicator method, and 5 multiple imputation (MI) approaches using parametric or machine-learning models. We considered 6 scenarios that varied in terms of exposure/outcome generation models (presence of confounder-confounder interactions) and missingness mechanisms (whether outcome influenced missingness in other variables and presence of interaction/nonlinear terms in missingness models). Complete-case analysis and extended TMLE had small biases when outcome did not influence missingness in other variables. Parametric MI without interactions had large bias when exposure/outcome generation models included interactions. Parametric MI including interactions performed best in bias and variance reduction across all settings, except when missingness models included a nonlinear term. When choosing a method for handling missing data in the context of TMLE, researchers must consider the missingness mechanism and, for MI, compatibility with the analysis method. In many settings, a parametric MI approach that incorporates interactions and nonlinearities is expected to perform well., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: core support from the Medical Research Council (MRC) UK to the MRC Clinical Trials Unit for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Frequent use of methylchloroisothiazolinone/methylisothiazolinone (MCI/MI) and MI in cosmetic products has been the main cause of widespread sensitization and allergic contact dermatitis to these preservatives (biocides). Their use in non-cosmetic products is also an important source of sensitization. Less is known about sensitization rates and use of benzisothiazolinone (BIT), octylisothiazolinone (OIT), and dichlorooctylisothiazolinone (DCOIT), which have never been permitted in cosmetic products in Europe. BIT and OIT have occasionally been routinely patch-tested. These preservatives are often used together in chemical products and articles. In this study, we review the occurrence of contact allergy to MI, BIT, OIT, and DCOIT over time, based on concomitant patch testing in large studies, and case reports. We review EU legislations, and we discuss the role of industry, regulators, and dermatology in prevention of sensitization and protection of health. The frequency of contact allergy to MI, BIT, and OIT has increased. The frequency of contact allergy to DCOIT is not known because it has seldom been patch-tested. Label information on isothiazolinones in chemical products and articles, irrespective of concentration, is required for assessment of relevance, information to patients, and avoidance of exposure and allergic contact dermatitis., (© 2024 The Authors. Contact Dermatitis published by John Wiley & Sons Ltd.)

Background: A 2×2 factorial design evaluates two interventions (A versus control and B versus control) by randomising to control, A-only, B-only or both A and B together. Extended factorial designs are also possible (e.g. 3×3 or 2×2×2). Factorial designs often require fewer resources and participants than alternative randomised controlled trials, but they are not widely used. We identified several issues that investigators considering this design need to address, before they use it in a late-phase setting., Methods: We surveyed journal articles published in 2000-2022 relating to designing factorial randomised controlled trials. We identified issues to consider based on these and our personal experiences., Results: We identified clinical, practical, statistical and external issues that make factorial randomised controlled trials more desirable. Clinical issues are (1) interventions can be easily co-administered; (2) risk of safety issues from co-administration above individual risks of the separate interventions is low; (3) safety or efficacy data are wanted on the combination intervention; (4) potential for interaction (e.g. effect of A differing when B administered) is low; (5) it is important to compare interventions with other interventions balanced, rather than allowing randomised interventions to affect the choice of other interventions; (6) eligibility criteria for different interventions are similar. Practical issues are (7) recruitment is not harmed by testing many interventions; (8) each intervention and associated toxicities is unlikely to reduce either adherence to the other intervention or overall follow-up; (9) blinding is easy to implement or not required. Statistical issues are (10) a suitable scale of analysis can be identified; (11) adjustment for multiplicity is not required; (12) early stopping for efficacy or lack of benefit can be done effectively. External issues are (13) adequate funding is available and (14) the trial is not intended for licensing purposes. An overarching issue (15) is that factorial design should give a lower sample size requirement than alternative designs. Across designs with varying non-adherence, retention, intervention effects and interaction effects, 2×2 factorial designs require lower sample size than a three-arm alternative when one intervention effect is reduced by no more than 24%-48% in the presence of the other intervention compared with in the absence of the other intervention., Conclusions: Factorial designs are not widely used and should be considered more often using our issues to consider. Low potential for at most small to modest interaction is key, for example, where the interventions have different mechanisms of action or target different aspects of the disease being studied., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Simulation studies are powerful tools in epidemiology and biostatistics, but they can be hard to conduct successfully. Sometimes unexpected results are obtained. We offer advice on how to check a simulation study when this occurs, and how to design and conduct the study to give results that are easier to check. Simulation studies should be designed to include some settings in which answers are already known. They should be coded in stages, with data-generating mechanisms checked before simulated data are analysed. Results should be explored carefully, with scatterplots of standard error estimates against point estimates surprisingly powerful tools. Failed estimation and outlying estimates should be identified and dealt with by changing data-generating mechanisms or coding realistic hybrid analysis procedures. Finally, we give a series of ideas that have been useful to us in the past for checking unexpected results. Following our advice may help to prevent errors and to improve the quality of published simulation studies., (© The Author(s) 2023. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Competing Interests: Conflicts of interest The authors declare no conflicts of interest.

Introduction: Network meta-analyses (NMAs) have gained popularity and grown in number due to their ability to provide estimates of the comparative effectiveness of multiple treatments for the same condition. The aim of this study is to conduct a methodological review to compile a preliminary list of concepts related to bias in NMAs., Methods and Analysis: We included papers that present items related to bias, reporting or methodological quality, papers assessing the quality of NMAs, or method papers. We searched MEDLINE, the Cochrane Library and unpublished literature (up to July 2020). We extracted items related to bias in NMAs. An item was excluded if it related to general systematic review quality or bias and was included in currently available tools such as ROBIS or AMSTAR 2. We reworded items, typically structured as questions, into concepts (i.e. general notions)., Results: One hundred eighty-one articles were assessed in full text and 58 were included. Of these articles, 12 were tools, checklists or journal standards; 13 were guidance documents for NMAs; 27 were studies related to bias or NMA methods; and 6 were papers assessing the quality of NMAs. These studies yielded 99 items of which the majority related to general systematic review quality and biases and were therefore excluded. The 22 items we included were reworded into concepts specific to bias in NMAs., Conclusions: A list of 22 concepts was included. This list is not intended to be used to assess biases in NMAs, but to inform the development of items to be included in our tool., (© 2024. The Author(s).)

Multiple imputation (MI) is a popular method for handling missing data. Auxiliary variables can be added to the imputation model(s) to improve MI estimates. However, the choice of which auxiliary variables to include is not always straightforward. Several data-driven auxiliary variable selection strategies have been proposed, but there has been limited evaluation of their performance. Using a simulation study we evaluated the performance of eight auxiliary variable selection strategies: (1, 2) two versions of selection based on correlations in the observed data; (3) selection using hypothesis tests of the "missing completely at random" assumption; (4) replacing auxiliary variables with their principal components; (5, 6) forward and forward stepwise selection; (7) forward selection based on the estimated fraction of missing information; and (8) selection via the least absolute shrinkage and selection operator (LASSO). A complete case analysis and an MI analysis using all auxiliary variables (the "full model") were included for comparison. We also applied all strategies to a motivating case study. The full model outperformed all auxiliary variable selection strategies in the simulation study, with the LASSO strategy the best performing auxiliary variable selection strategy overall. All MI analysis strategies that we were able to apply to the case study led to similar estimates, although computational time was substantially reduced when variable selection was employed. This study provides further support for adopting an inclusive auxiliary variable strategy where possible. Auxiliary variable selection using the LASSO may be a promising alternative when the full model fails or is too burdensome., (© 2024 The Authors. Biometrical Journal published by Wiley-VCH GmbH.)

Although new biostatistical methods are published at a very high rate, many of these developments are not trustworthy enough to be adopted by the scientific community. We propose a framework to think about how a piece of methodological work contributes to the evidence base for a method. Similar to the well-known phases of clinical research in drug development, we propose to define four phases of methodological research. These four phases cover (I) proposing a new methodological idea while providing, for example, logical reasoning or proofs, (II) providing empirical evidence, first in a narrow target setting, then (III) in an extended range of settings and for various outcomes, accompanied by appropriate application examples, and (IV) investigations that establish a method as sufficiently well-understood to know when it is preferred over others and when it is not; that is, its pitfalls. We suggest basic definitions of the four phases to provoke thought and discussion rather than devising an unambiguous classification of studies into phases. Too many methodological developments finish before phase III/IV, but we give two examples with references. Our concept rebalances the emphasis to studies in phases III and IV, that is, carefully planned method comparison studies and studies that explore the empirical properties of existing methods in a wider range of problems., (© 2023 The Authors. Biometrical Journal published by Wiley-VCH GmbH.)

Individual participant data (IPD) meta-analyses of randomised trials are considered a reliable way to assess participant-level treatment effect modifiers but may not make the best use of the available data. Traditionally, effect modifiers are explored one covariate at a time, which gives rise to the possibility that evidence of treatment-covariate interaction may be due to confounding from a different, related covariate. We aimed to evaluate current practice when estimating treatment-covariate interactions in IPD meta-analysis, specifically focusing on involvement of additional covariates in the models. We reviewed 100 IPD meta-analyses of randomised trials, published between 2015 and 2020, that assessed at least one treatment-covariate interaction. We identified four approaches to handling additional covariates: (1) Single interaction model (unadjusted): No additional covariates included (57/100 IPD meta-analyses); (2) Single interaction model (adjusted): Adjustment for the main effect of at least one additional covariate (35/100); (3) Multiple interactions model: Adjustment for at least one two-way interaction between treatment and an additional covariate (3/100); and (4) Three-way interaction model: Three-way interaction formed between treatment, the additional covariate and the potential effect modifier (5/100). IPD is not being utilised to its fullest extent. In an exemplar dataset, we demonstrate how these approaches lead to different conclusions. Researchers should adjust for additional covariates when estimating interactions in IPD meta-analysis providing they adjust their main effects, which is already widely recommended. Further, they should consider whether more complex approaches could provide better information on who might benefit most from treatments, improving patient choice and treatment policy and practice., (© 2023 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.)

Background: The population-level summary measure is a key component of the estimand for clinical trials with time-to-event outcomes. This is particularly the case for non-inferiority trials, because different summary measures imply different null hypotheses. Most trials are designed using the hazard ratio as summary measure, but recent studies suggested that the difference in restricted mean survival time might be more powerful, at least in certain situations. In a recent letter, we conjectured that differences between summary measures can be explained using the concept of the non-inferiority frontier and that for a fair simulation comparison of summary measures, the same analysis methods, making the same assumptions, should be used to estimate different summary measures. The aim of this article is to make such a comparison between three commonly used summary measures: hazard ratio, difference in restricted mean survival time and difference in survival at a fixed time point. In addition, we aim to investigate the impact of using an analysis method that assumes proportional hazards on the operating characteristics of a trial designed with any of the three summary measures., Methods: We conduct a simulation study in the proportional hazards setting. We estimate difference in restricted mean survival time and difference in survival non-parametrically, without assuming proportional hazards. We also estimate all three measures parametrically, using flexible survival regression, under the proportional hazards assumption., Results: Comparing the hazard ratio assuming proportional hazards with the other summary measures not assuming proportional hazards, relative performance varies substantially depending on the specific scenario. Fixing the summary measure, assuming proportional hazards always leads to substantial power gains compared to using non-parametric methods. Fixing the modelling approach to flexible parametric regression assuming proportional hazards, difference in restricted mean survival time is most often the most powerful summary measure among those considered., Conclusion: When the hazards are likely to be approximately proportional, reflecting this in the analysis can lead to large gains in power for difference in restricted mean survival time and difference in survival. The choice of summary measure for a non-inferiority trial with time-to-event outcomes should be made on clinical grounds; when any of the three summary measures discussed here is equally justifiable, difference in restricted mean survival time is most often associated with the most powerful test, on the condition that it is estimated under proportional hazards., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

In network meta-analysis, studies evaluating multiple treatment comparisons are modeled simultaneously, and estimation is informed by a combination of direct and indirect evidence. Network meta-analysis relies on an assumption of consistency, meaning that direct and indirect evidence should agree for each treatment comparison. Here we propose new local and global tests for inconsistency and demonstrate their application to three example networks. Because inconsistency is a property of a loop of treatments in the network meta-analysis, we locate the local test in a loop. We define a model with one inconsistency parameter that can be interpreted as loop inconsistency. The model builds on the existing ideas of node-splitting and side-splitting in network meta-analysis. To provide a global test for inconsistency, we extend the model across multiple independent loops with one degree of freedom per loop. We develop a new algorithm for identifying independent loops within a network meta-analysis. Our proposed models handle treatments symmetrically, locate inconsistency in loops rather than in nodes or treatment comparisons, and are invariant to choice of reference treatment, making the results less dependent on model parameterization. For testing global inconsistency in network meta-analysis, our global model uses fewer degrees of freedom than the existing design-by-treatment interaction approach and has the potential to increase power. To illustrate our methods, we fit the models to three network meta-analyses varying in size and complexity. Local and global tests for inconsistency are performed and we demonstrate that the global model is invariant to choice of independent loops., (© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Background: For certain conditions, treatments aim to lessen deterioration over time. A trial outcome could be change in a continuous measure, analysed using a random slopes model with a different slope in each treatment group. A sample size for a trial with a particular schedule of visits (e.g. annually for three years) can be obtained using a two-stage process. First, relevant (co-) variances are estimated from a pre-existing dataset e.g. an observational study conducted in a similar setting. Second, standard formulae are used to calculate sample size. However, the random slopes model assumes linear trajectories with any difference in group means increasing proportionally to follow-up time. The impact of these assumptions failing is unclear., Methods: We used simulation to assess the impact of a non-linear trajectory and/or non-proportional treatment effect on the proposed trial's power. We used four trajectories, both linear and non-linear, and simulated observational studies to calculate sample sizes. Trials of this size were then simulated, with treatment effects proportional or non-proportional to time., Results: For a proportional treatment effect and a trial visit schedule matching the observational study, powers are close to nominal even for non-linear trajectories. However, if the schedule does not match the observational study, powers can be above or below nominal levels, with the extent of this depending on parameters such as the residual error variance. For a non-proportional treatment effect, using a random slopes model can lead to powers far from nominal levels., Conclusions: If trajectories are suspected to be non-linear, observational data used to inform power calculations should have the same visit schedule as the proposed trial where possible. Additionally, if the treatment effect is expected to be non-proportional, the random slopes model should not be used. A model allowing trajectories to vary freely over time could be used instead, either as a second line analysis method (bearing in mind that power will be lost) or when powering the trial., (© 2023. The Author(s).)

Background: Overall survival is the "gold standard" endpoint in cancer clinical trials. It plays a key role in determining the clinical- and cost-effectiveness of a new intervention and whether it is recommended for use in standard of care. The assessment of overall survival usually requires trial participants to be followed up for a long period of time. In this time, they may stop receiving the trial intervention and receive subsequent anti-cancer treatments, which also aim to extend survival, during trial follow-up. This can potentially change the interpretation of overall survival in the context of the clinical trial. This review aimed to determine how overall survival has been assessed in cancer clinical trials and whether subsequent anti-cancer treatments are considered., Methods: Two searches were conducted using MEDLINE within OVID© on the 9th of November 2021. The first sought to identify papers publishing overall survival results from randomised controlled trials in eight reputable journals and the second to identify papers mentioning or considering subsequent treatments. Papers published since 2010 were included if presenting or discussing overall survival in the context of treating cancer., Results: One hundred and thirty-four papers were included. The majority of these were presenting clinical trial results (98, 73%). Of these, 45 (46%) reported overall survival as a (co-) primary endpoint. A lower proportion of papers including overall survival as a (co-) primary endpoint compared to a secondary endpoint were published in recent years. The primary analysis of overall survival varied across the papers. Fifty-nine (60%) mentioned subsequent treatments. Seven papers performed additional analysis, primarily when patients in the control arm received the experimental treatment during trial follow-up (treatment switching)., Discussion: Overall survival has steadily moved from being the primary to a secondary endpoint. However, it is still of interest with papers presenting overall survival results with the caveat of subsequent treatments, but little or no investigation into their effect. This review shows that there is a methodological gap for what researchers should do when trial participants receive anti-cancer treatment during trial follow-up. Future research will identify the stakeholder opinions, on how this methodological gap should be addressed., (© 2023. The Author(s).)

Background: In a non-inferiority trial, the choice of margin depends on the expected control event risk. If the true risk differs from expected, power and interpretability of results can be affected. A non-inferiority frontier pre-specifies an appropriate non-inferiority margin for each value of control event risk. D3 is a non-inferiority trial comparing two treatment regimens in children living with HIV, designed assuming a control event risk of 12%, a non-inferiority margin of 10%, 80% power and a significance level (α) of 0.025. We consider approaches to choosing and implementing a frontier for this already funded trial, where changing the sample size substantially would be difficult., Methods: In D3, we fix the non-inferiority margin at 10%, 8% and 5% for control event risks of ≥9%, 5% and 1%, respectively. We propose four frontiers which fit these fixed points, including a Smooth Away From Expected (SAFE) frontier. Analysis approaches considered are as follows: using the pre-specified significance level (α=0.025); always using a reduced significance level (to achieve α≤0.025 across control event risks); reducing significance levels only when the control event risk differs significantly from expected (control event risk <9%); and using a likelihood ratio test. We compare power and type 1 error for SAFE with other frontiers., Results: Changing the significance level only when the control event risk is <9% achieves approximately nominal (<3%) type I error rate and maintains reasonable power for control event risks between 1 and 15%. The likelihood ratio test method performs similarly, but the results are more complex to present. Other analysis methods lead to either inflated type 1 error or badly reduced power. The SAFE frontier gives more interpretable results with low control event risks than other frontiers (i.e. it uses more reasonable non-inferiority margins). Other frontiers do not achieve power close (i.e. within 1%) to SAFE across the range of likely control event risks while controlling type I error., Conclusions: The SAFE non-inferiority frontier will be used in D3, and the non-inferiority margin and significance level will be modified if the control event risk is lower than expected. This ensures results will remain interpretable if design assumptions are incorrect, while achieving similar power. A similar approach could be considered for other non-inferiority trials where the control event risk is uncertain., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Aims: To assess the effects of bed rest duration on short-term complications following transfemoral catheterization., Methods and Results: A systematic search was carried out in MEDLINE, Embase, CINAHL, Cochrane Database of Systematic Reviews, Scopus, SciELO and in five registries of grey literature. Randomized controlled trials and quasi-experimental studies comparing different durations of bed rest after transfemoral catheterization were included. Primary outcomes were haematoma and bleeding near the access site. Secondary outcomes were arteriovenous fistula, pseudoaneurysm, back pain, general patient discomfort and urinary discomfort. Study findings were summarized using a network meta-analysis (NMA). Twenty-eight studies and 9217 participants were included (mean age 60.4 years). In NMA, bed rest duration was not consistently associated with either primary outcome, and this was confirmed in sensitivity analyses. There was no evidence of associations with secondary outcomes, except for two effects related to back pain. A bed rest duration of 2-2.9 h was associated with lower risk of back pain [risk ratio (RR) 0.33, 95% confidence interval (CI) 0.17-0.62] and a duration over 12 h with greater risk of back pain (RR 1.94, 95% CI 1.16-3.24), when compared with the 4-5.9 h interval. Post hoc analysis revealed an increased risk of back pain per hour of bed rest (RR 1.08, 95% CI 1.04-1.11)., Conclusion: A short bed rest was not associated with complications in patients undergoing transfemoral catheterization; the greater the duration of bed rest, the more likely the patients were to experience back pain. Ambulation as early as 2 h after transfemoral catheterization can be safely implemented., Registration: PROSPERO: CRD42014014222., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Introduction: Mindfulness-based programmes (MBPs) are widely used to prevent mental ill-health that is becoming the leading global cause of morbidity. Evidence suggests beneficial average effects but wide variability. We aimed to confirm the effect of MBPs on psychological distress, and to understand whether and how baseline distress, gender, age, education, and dispositional mindfulness modify the effect of MBPs on distress among adults in non-clinical settings., Methods: We conducted a pre-registered systematic review and individual participant data (IPD) meta-analysis (PROSPERO CRD42020200117). Thirteen databases were searched in December 2020 for randomised controlled trials satisfying a quality threshold and comparing in-person, expert-defined MBPs in non-clinical settings with passive control groups. Two researchers independently selected, extracted, and appraised trials using the revised Cochrane Risk-of-Bias Tool (RoB2). Anonymised IPD of eligible trials were sought from collaborating authors. The primary outcome was psychological distress (unpleasant mental or emotional experiences including anxiety and depression) at 1 to 6 months after programme completion. Data were checked and imputed if missing. Pairwise, random-effects, two-stage IPD meta-analyses were conducted. Effect modification analyses followed a within-studies approach. Public and professional stakeholders were involved in the planning, conduct and dissemination of this study., Results: Fifteen trials were eligible, 13 trialists shared IPD (2,371 participants representing 8 countries, median age 34 years-old, 71% women, moderately distressed on average, 20% missing outcome data). In comparison with passive control groups, MBPs reduced average distress between one- and six-months post-intervention with a small to moderate effect size (standardised mean difference (SMD) -0.32; 95% confidence interval (CI) -0.41 to -0.24; p-value < 0.001; 95% prediction interval (PI) -0.41 to -0.24 (no heterogeneity)). Results were robust to sensitivity analyses, and similar for the other psychological distress time point ranges. Confidence in the primary outcome result is high. We found no clear indication that this effect is modified by baseline psychological distress, gender, age, education level, or dispositional mindfulness., Conclusions: Group-based teacher-led MBPs generally reduce psychological distress among community adults who volunteer to receive this type of intervention. More research is needed to identify sources of variability in outcomes at an individual level., Competing Interests: Competing Interests Statement JG, CF, TD, IRW, PBJ, NA, MAdJ, BB, SMB, MMC, MSC, AE, SBG, CUG, MJH, SLH, MH, YSH, OM, MAR, MPJS, NMS, NNS, AEMS, FCT, LTM have no conflicts of interest to declare. JKB has been in the past 3 years a consultant to / member of advisory board of / and/or speaker for Medice, Angelini, Janssen, Boehringer-Ingelheim, and Servier, he is not an employee of any of these companies, and not a stock shareholder of any of these companies, he has no other financial or material support, including expert testimony, patents, royalties. RJD is the founder and president of Healthy Minds Innovations, a non-profit organisation.

Background: Adding docetaxel to androgen deprivation therapy (ADT) improves survival in patients with metastatic, hormone-sensitive prostate cancer, but uncertainty remains about who benefits most. We therefore aimed to obtain up-to-date estimates of the overall effects of docetaxel and to assess whether these effects varied according to prespecified characteristics of the patients or their tumours., Methods: The STOPCAP M1 collaboration conducted a systematic review and meta-analysis of individual participant data. We searched MEDLINE (from database inception to March 31, 2022), Embase (from database inception to March 31, 2022), the Cochrane Central Register of Controlled Trials (from database inception to March 31, 2022), proceedings of relevant conferences (from Jan 1, 1990, to Dec 31, 2022), and ClinicalTrials.gov (from database inception to March 28, 2023) to identify eligible randomised trials that assessed docetaxel plus ADT compared with ADT alone in patients with metastatic, hormone-sensitive prostate cancer. Detailed and updated individual participant data were requested directly from study investigators or through relevant repositories. The primary outcome was overall survival. Secondary outcomes were progression-free survival and failure-free survival. Overall pooled effects were estimated using an adjusted, intention-to-treat, two-stage, fixed-effect meta-analysis, with one-stage and random-effects sensitivity analyses. Missing covariate values were imputed. Differences in effect by participant characteristics were estimated using adjusted two-stage, fixed-effect meta-analysis of within-trial interactions on the basis of progression-free survival to maximise power. Identified effect modifiers were also assessed on the basis of overall survival. To explore multiple subgroup interactions and derive subgroup-specific absolute treatment effects we used one-stage flexible parametric modelling and regression standardisation. We assessed the risk of bias using the Cochrane Risk of Bias 2 tool. This study is registered with PROSPERO, CRD42019140591., Findings: We obtained individual participant data from 2261 patients (98% of those randomised) from three eligible trials (GETUG-AFU15, CHAARTED, and STAMPEDE trials), with a median follow-up of 72 months (IQR 55-85). Individual participant data were not obtained from two additional small trials. Based on all included trials and patients, there were clear benefits of docetaxel on overall survival (hazard ratio [HR] 0·79, 95% CI 0·70 to 0·88; p<0·0001), progression-free survival (0·70, 0·63 to 0·77; p<0·0001), and failure-free survival (0·64, 0·58 to 0·71; p<0·0001), representing 5-year absolute improvements of around 9-11%. The overall risk of bias was assessed to be low, and there was no strong evidence of differences in effect between trials for all three main outcomes. The relative effect of docetaxel on progression-free survival appeared to be greater with increasing clinical T stage (p interaction =0·0019), higher volume of metastases (p interaction =0·020), and, to a lesser extent, synchronous diagnosis of metastatic disease (p interaction =0·077). Taking into account the other interactions, the effect of docetaxel was independently modified by volume and clinical T stage, but not timing. There was no strong evidence that docetaxel improved absolute effects at 5 years for patients with low-volume, metachronous disease (-1%, 95% CI -15 to 12, for progression-free survival; 0%, -10 to 12, for overall survival). The largest absolute improvement at 5 years was observed for those with high-volume, clinical T stage 4 disease (27%, 95% CI 17 to 37, for progression-free survival; 35%, 24 to 47, for overall survival)., Interpretation: The addition of docetaxel to hormone therapy is best suited to patients with poorer prognosis for metastatic, hormone-sensitive prostate cancer based on a high volume of disease and potentially the bulkiness of the primary tumour. There is no evidence of meaningful benefit for patients with metachronous, low-volume disease who should therefore be managed differently. These results will better characterise patients most and, importantly, least likely to gain benefit from docetaxel, potentially changing international practice, guiding clinical decision making, better informing treatment policy, and improving patient outcomes., Funding: UK Medical Research Council and Prostate Cancer UK., Competing Interests: Declaration of interests BT declares consulting fees from Amgen, Astellas, Bayer, Ferring, Novartis, and Janssen; payment or honoraria for lectures, presentations, speaker bureaus, manuscript writing, or educational events from Amgen, Astellas, Bayer, Ferring, Novartis, Janssen, and Myovant during the past 36 months. CJS declares grants or contracts from Bayer, Sanofi, Astellas/Pfizer, Dendreon, and Janssen, and personal consulting fees from Bayer, Astellas, Janssen, CellCentric, PointBiopharma, Pfizer, Novartis, Genentech/Roche, Bristol Myers Squibb (BMS), Lilly, Henguiu, and AstraZeneca during the past 36 months. GG declares institutional grants from BMS; institutional payment or honoraria for lectures, presentations, speaker bureaus, manuscript writing, or educational events from Janssen, Amgen, BMS, IPSEN, AAA, Curium, AstraZeneca, Bayer, Pfizer/Merck, and Astellas; personal payment for expert testimony from BMS, Bayer, and Pfizer/Merck; and institutional support for attending meetings or travel from Janssen, BMS, AstraZeneca, Bayer, and Pfizer/Merck during the past 36 months. GL declares payment or honoraria for lectures, presentations, speaker bureaus, manuscript writing, or educational events from Sanofi-Genzyme; participation on a data safety monitoring board or advisory board from Janssen; and leadership or fiduciary role in other board, society, committee, or advocacy group, paid or unpaid, from ECOG-ACRIN (Prostate Cancer Subcommittee Chair) during the past 36 months. MRS declares grants or contracts from Astellas, Clovis Oncology, Janssen, Pfizer, Novartis, and Sanofi-Aventis (all on research that is independent of this manuscript); consulting fees from Eli Lilly; and payment or honoraria for lectures, presentations, speaker bureaus, manuscript writing, or educational events from Lilly Oncology and Janssen (speaker fees not relating to any particular medicinal product) during the past 36 months. KF declares institutional honoraria for participation on advisory boards and in talks for Amgen, Astellas, AstraZeneca, Bayer, Clovis, Daiichi Sankyo, Janssen, MSD, Novartis/AAA, Pfizer, and Sanofi, and personal honoraria for participation in advisory boards with Arvinas, CureVac and Orion during the past 36 months. All other authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: A common intercurrent event affecting many trials is when some participants do not begin their assigned treatment. For example, in a double-blind drug trial, some participants may not receive any dose of study medication. Many trials use a 'modified intention-to-treat' approach, whereby participants who do not initiate treatment are excluded from the analysis. However, it is not clear (a) the estimand being targeted by such an approach and (b) the assumptions necessary for such an approach to be unbiased., Methods: Using potential outcome notation, we demonstrate that a modified intention-to-treat analysis which excludes participants who do not begin treatment is estimating a principal stratum estimand (i.e. the treatment effect in the subpopulation of participants who would begin treatment, regardless of which arm they were assigned to). The modified intention-to-treat estimator is unbiased for the principal stratum estimand under the assumption that the intercurrent event is not affected by the assigned treatment arm, that is, participants who initiate treatment in one arm would also do so in the other arm (i.e. if someone began the intervention, they would also have begun the control, and vice versa)., Results: We identify two key criteria in determining whether the modified intention-to-treat estimator is likely to be unbiased: first, we must be able to measure the participants in each treatment arm who experience the intercurrent event, and second, the assumption that treatment allocation will not affect whether the participant begins treatment must be reasonable. Most double-blind trials will satisfy these criteria, as the decision to start treatment cannot be influenced by the allocation, and we provide an example of an open-label trial where these criteria are likely to be satisfied as well, implying that a modified intention-to-treat analysis which excludes participants who do not begin treatment is an unbiased estimator for the principal stratum effect in these settings. We also give two examples where these criteria will not be satisfied (one comparing an active intervention vs usual care, where we cannot identify which usual care participants would have initiated the active intervention, and another comparing two active interventions in an unblinded manner, where knowledge of the assigned treatment arm may affect the participant's choice to begin or not), implying that a modified intention-to-treat estimator will be biased in these settings., Conclusion: A modified intention-to-treat analysis which excludes participants who do not begin treatment can be an unbiased estimator for the principal stratum estimand. Our framework can help identify when the assumptions for unbiasedness are likely to hold, and thus whether modified intention-to-treat is appropriate or not.

Ventilator-associated pneumonia commonly occurs in critically ill patients. Clinical suspicion results in overuse of antibiotics, which in turn promotes antimicrobial resistance. Detection of volatile organic compounds in the exhaled breath of critically ill patients might allow earlier detection of pneumonia and avoid unnecessary antibiotic prescription. We report a proof of concept study for non-invasive diagnosis of ventilator-associated pneumonia in intensive care (the BRAVo study). Mechanically ventilated critically ill patients commenced on antibiotics for clinical suspicion of ventilator-associated pneumonia were recruited within the first 24 h of treatment. Paired exhaled breath and respiratory tract samples were collected. Exhaled breath was captured on sorbent tubes and then analysed using thermal desorption gas chromatography-mass spectrometry to detect volatile organic compounds. Microbiological culture of a pathogenic bacteria in respiratory tract samples provided confirmation of ventilator-associated pneumonia. Univariable and multivariable analyses of volatile organic compounds were performed to identify potential biomarkers for a 'rule-out' test. Ninety-six participants were enrolled in the trial, with exhaled breath available from 92. Of all compounds tested, the four highest performing candidate biomarkers were benzene, cyclohexanone, pentanol and undecanal with area under the receiver operating characteristic curve ranging from 0.67 to 0.77 and negative predictive values from 85% to 88%. Identified volatile organic compounds in the exhaled breath of mechanically ventilated critically ill patients show promise as a useful non-invasive 'rule-out' test for ventilator-associated pneumonia., (© 2023 The Authors. Anaesthesia published by John Wiley & Sons Ltd on behalf of Association of Anaesthetists.)

Competing Interests: Competing interests: IRW has received royalties as co-editor from sales of the Handbook of Meta-Analysis.

Objectives: We evaluated the error detection performance of the DetectDeviatingCells (DDC) algorithm which flags data anomalies at observation (casewise) and variable (cellwise) level in continuous variables. We compared its performance to other approaches in a simulated dataset., Study Design and Setting: We simulated height and weight data for hypothetical individuals aged 2-20 years. We changed a proportion of height values according to predetermined error patterns. We applied the DDC algorithm and other error-detection approaches (descriptive statistics, plots, fixed-threshold rules, classic, and robust Mahalanobis distance) and we compared error detection performance with sensitivity, specificity, likelihood ratios, predictive values, and receiver operating characteristic (ROC) curves., Results: At our chosen thresholds error detection specificity was excellent across all scenarios for all methods and sensitivity was higher for multivariable and robust methods. The DDC algorithm performance was similar to other robust multivariable methods. Analysis of ROC curves suggested that all methods had comparable performance for gross errors (e.g., wrong measurement unit), but the DDC algorithm outperformed the others for more complex error patterns (e.g., transcription errors that are still plausible, although extreme)., Conclusions: The DDC algorithm has the potential to improve error detection processes for observational data., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Bayesian analysis of a non-inferiority trial is advantageous in allowing direct probability statements to be made about the relative treatment difference rather than relying on an arbitrary and often poorly justified non-inferiority margin. When the primary analysis will be Bayesian, a Bayesian approach to sample size determination will often be appropriate for consistency with the analysis. We demonstrate three Bayesian approaches to choosing sample size for non-inferiority trials with binary outcomes and review their advantages and disadvantages. First, we present a predictive power approach for determining sample size using the probability that the trial will produce a convincing result in the final analysis. Next, we determine sample size by considering the expected posterior probability of non-inferiority in the trial. Finally, we demonstrate a precision-based approach. We apply these methods to a non-inferiority trial in antiretroviral therapy for treatment of HIV-infected children. A predictive power approach would be most accessible in practical settings, because it is analogous to the standard frequentist approach. Sample sizes are larger than with frequentist calculations unless an informative analysis prior is specified, because appropriate allowance is made for uncertainty in the assumed design parameters, ignored in frequentist calculations. An expected posterior probability approach will lead to a smaller sample size and is appropriate when the focus is on estimating posterior probability rather than on testing. A precision-based approach would be useful when sample size is restricted by limits on recruitment or costs, but it would be difficult to decide on sample size using this approach alone., (© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

In some clinical scenarios, for example, severe sepsis caused by extensively drug resistant bacteria, there is uncertainty between many common treatments, but a conventional multiarm randomized trial is not possible because individual participants may not be eligible to receive certain treatments. The Personalised Randomized Controlled Trial design allows each participant to be randomized between a "personalised randomization list" of treatments that are suitable for them. The primary aim is to produce treatment rankings that can guide choice of treatment, rather than focusing on the estimates of relative treatment effects. Here we use simulation to assess several novel analysis approaches for this innovative trial design. One of the approaches is like a network meta-analysis, where participants with the same personalised randomization list are like a trial, and both direct and indirect evidence are used. We evaluate this proposed analysis and compare it with analyses making less use of indirect evidence. We also propose new performance measures including the expected improvement in outcome if the trial's rankings are used to inform future treatment rather than random choice. We conclude that analysis of a personalized randomized controlled trial can be performed by pooling data from different types of participants and is robust to moderate subgroup-by-intervention interactions based on the parameters of our simulation. The proposed approach performs well with respect to estimation bias and coverage. It provides an overall treatment ranking list with reasonable precision, and is likely to improve outcome on average if used to determine intervention policies and guide individual clinical decisions., (© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

When data are available from individual patients receiving either a treatment or a control intervention in a randomized trial, various statistical and machine learning methods can be used to develop models for predicting future outcomes under the two conditions, and thus to predict treatment effect at the patient level. These predictions can subsequently guide personalized treatment choices. Although several methods for validating prediction models are available, little attention has been given to measuring the performance of predictions of personalized treatment effect. In this article, we propose a range of measures that can be used to this end. We start by defining two dimensions of model accuracy for treatment effects, for a single outcome: discrimination for benefit and calibration for benefit. We then amalgamate these two dimensions into an additional concept, decision accuracy, which quantifies the model's ability to identify patients for whom the benefit from treatment exceeds a given threshold. Subsequently, we propose a series of performance measures related to these dimensions and discuss estimating procedures, focusing on randomized data. Our methods are applicable for continuous or binary outcomes, for any type of prediction model, as long as it uses baseline covariates to predict outcomes under treatment and control. We illustrate all methods using two simulated datasets and a real dataset from a trial in depression. We implement all methods in the R package predieval. Results suggest that the proposed measures can be useful in evaluating and comparing the performance of competing models in predicting individualized treatment effect., (© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Background: Early and accurate recognition of respiratory pathogens is crucial to prevent increased risk of mortality in critically ill patients. Microbial-derived volatile organic compounds (mVOCs) in exhaled breath could be used as noninvasive biomarkers of infection to support clinical diagnosis., Methods: In this study, we investigated the diagnostic potential of in vitro-confirmed mVOCs in the exhaled breath of patients under mechanical ventilation from the BreathDx study. Samples were analyzed by thermal desorption-gas chromatography-mass spectrometry., Results: Pathogens from bronchoalveolar lavage (BAL) cultures were identified in 45 of 89 patients and Staphylococcus aureus was the most commonly identified pathogen (n = 15). Of 19 mVOCs detected in the in vitro culture headspace of 4 common respiratory pathogens (S. aureus, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Escherichia coli), 14 were found in exhaled breath samples. Higher concentrations of 2 mVOCs were found in the exhaled breath of patients infected with S. aureus compared to those without (3-methylbutanal: P < .01, area under the receiver operating characteristic curve [AUROC] = 0.81-0.87; and 3-methylbutanoic acid: P = .01, AUROC = 0.79-0.80). In addition, bacteria identified from BAL cultures that are known to metabolize tryptophan (E. coli, Klebsiella oxytoca, and Haemophilus influenzae) were grouped and found to produce higher concentrations of indole compared to breath samples with culture-negative (P = .034) and other pathogen-positive (P = .049) samples., Conclusions: This study demonstrates the capability of using mVOCs to detect the presence of specific pathogen groups with potential to support clinical diagnosis. Although not all mVOCs were found in patient samples within this small pilot study, further targeted and qualitative investigation is warranted using multicenter clinical studies., Competing Interests: Potential conflicts of interest. P. D. is Deputy Medical Director, NIHR National Research Network (payment to institution), and NIHR Senior Investigator (payment to employing institution). B. D. reports travel accommodation from Markes International for the Breath Summit conference 2022. L. D. J. B. reports consulting fees from Scailyte and Santhera (paid to institution) and participation on advisory board for Sobi, Excastat, Pfizer, and AstraZeneca. P. B. reports grants for development of chemical sensor–driven technology from Amsterdam University Medical Center (Innovatie Impuls 2020), Vertex (Vertex Innovation Award 2022), Stichting Astma Bestrijding, and Boehringer Ingelheim; a public-private partnership grant from Eurostars; and a grant for Disaster-Resilient Society 2021 (HORIZON-CL3-2021-DRS-01) from the Horizon Europe Framework Programme. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2022. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Importance: The common use of isothiazolinones as preservatives is a global cause of allergic contact dermatitis. Differences in allowable concentrations of methylisothiazolinone (MI) exist in Europe, Canada, and the US., Objective: To compare the prevalence of positive patch test reactions to the methylchloroisothiazolinone/methylisothiazolinone (MCI/MI) combination and MI alone in North America and Europe from 2009 to 2018., Design, Setting, and Participants: This retrospective analysis of North American Contact Dermatitis Group, European Surveillance System on Contact Allergies (ESSCA), and the Information Network of Departments of Dermatology (IVDK) databases included data from patients presenting for patch testing at referral patch test clinics in North America and Europe., Exposures: Patch tests to MCI/MI and MI., Main Outcomes and Measures: Prevalence of allergic contact dermatitis to MCI/MI and MI., Results: From 2009 to 2018, participating sites in North America and Europe patch tested a total of 226 161 individuals to MCI/MI and 118 779 to MI. In Europe, positivity to MCI/MI peaked during 2013 and 2014 at 7.6% (ESSCA) and 5.4% (IVDK) before decreasing to 4.4% (ESSCA) and 3.2% (IVDK) during 2017 and 2018. Positive reactions to MI were 5.5% (ESSCA) and 3.4% (IVDK) during 2017 and 2018. In North America, the frequency of positivity to MCI/MI increased steadily through the study period, reaching 10.8% for MCI/MI during 2017 and 2018. Positive reactions to MI were 15.0% during 2017 and 2018., Conclusions and Relevance: The study results suggest that in contrast to the continued increase in North America, isothiazolinone allergy is decreasing in Europe. This trend may coincide with earlier and more stringent government regulation of MI in Europe.

We describe the command artbin, which offers various new facilities for the calculation of sample size for binary outcome variables that are not otherwise available in Stata. While artbin has been available since 2004, it has not been previously described in the Stata Journal . artbin has been recently updated to include new options for different statistical tests, methods and study designs, improved syntax, and better handling of noninferiority trials. In this article, we describe the updated version of artbin and detail the various formulas used within artbin in different settings.

We describe a new command, artcat, that calculates sample size or power for a randomized controlled trial or similar experiment with an ordered categorical outcome, where analysis is by the proportional-odds model. artcat implements the method of Whitehead (1993, Statistics in Medicine 12: 2257-2271). We also propose and implement a new method that 1) allows the user to specify a treatment effect that does not obey the proportional-odds assumption, 2) offers greater accuracy for large treatment effects, and 3) allows for noninferiority trials. We illustrate the command and explore the value of an ordered categorical outcome over a binary outcome in various settings. We show by simulation that the methods perform well and that the new method is more accurate than Whitehead's method.

Background: MND-SMART is a platform, multi-arm, multi-stage, multi-centre, randomised controlled trial recruiting people with motor neuron disease. Initially, the treatments memantine and trazodone will each be compared against placebo, but other investigational treatments will be introduced into the trial later. The co-primary outcomes are the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (ALS-FRS-R) functional outcome, which is assessed longitudinally, and overall survival., Methods: Initially in MND-SMART, participants are randomised 1:1:1 via a minimisation algorithm to receive placebo or one of the two investigational treatments with up to 531 to be randomised in total. The comparisons between each research arm and placebo will be conducted in four stages, with the opportunity to cease further randomisations to poorly performing research arms at the end of stages 1 or 2. The final ALS-FRS-R analysis will be at the end of stage 3 and final survival analysis at the end of stage 4. The estimands for the co-primary outcomes are described in detail. The primary analysis of ALS-FRS-R at the end of stages 1 to 3 will involve fitting a normal linear mixed model to the data to calculate a mean difference in rate of ALS-FRS-R change between each research treatment and placebo. The pairwise type 1 error rate will be controlled, because each treatment comparison will generate its own distinct and separate interpretation. This publication is based on a formal statistical analysis plan document that was finalised and signed on 18 May 2022., Discussion: In developing the statistical analysis plan, we had to carefully consider several issues such as multiple testing, estimand specification, interim analyses, and statistical analysis of the repeated measurements of ALS-FRS-R. This analysis plan attempts to balance multiple factors, including minimisation of bias, maximising power and precision, and deriving clinically interpretable summaries of treatment effects., Trial Registration: EudraCT Number, 2019-000099-41. Registered 2 October 2019, https://www.clinicaltrialsregister.eu/ctr-search/search?query=mnd-smart ClinicalTrials.gov, NCT04302870 . Registered 10 March 2020., (© 2023. The Author(s).)

The paper by Lee and Beretvas (doi:10.1002/jrsm.1585) described a well-executed simulation study comparing 'modern' with 'ad hoc' methods for performing meta-regression when some covariates are incomplete. However, they drew practical conclusions after simulating data under a single missing data mechanism which favoured the 'modern' methods, while other missing data mechanisms would have favoured the 'ad hoc' methods. Broad recommendations about methods to use in practice should instead be based on simulation studies using a range of plausible data-generating mechanisms. This range must represent what is believed likely to occur in practice, and not what is convenient for statistical analysis., (© 2022 John Wiley & Sons Ltd.)

Estimation of within-trial interactions in meta-analysis is crucial for reliable assessment of how treatment effects vary across participant subgroups. However, current methods have various limitations. Patients, clinicians and policy-makers need reliable estimates of treatment effects within specific covariate subgroups, on relative and absolute scales, in order to target treatments appropriately-which estimation of an interaction effect does not in itself provide. Also, the focus has been on covariates with only two subgroups, and may exclude relevant data if only a single subgroup is reported. Therefore, in this article we further develop the "within-trial" framework by providing practical methods to (1) estimate within-trial interactions across two or more subgroups; (2) estimate subgroup-specific ("floating") treatment effects that are compatible with the within-trial interactions and make maximum use of available data; and (3) clearly present this data using novel implementation of forest plots. We described the steps involved and apply the methods to two examples taken from previously published meta-analyses, and demonstrate a straightforward implementation in Stata based upon existing code for multivariate meta-analysis. We discuss how the within-trial framework and plots can be utilised with aggregate (or "published") source data, as well as with individual participant data, to effectively demonstrate how treatment effects differ across participant subgroups., (© 2022 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.)

Background: Research overlap and duplication is a recognised problem in the context of both pairwise and network systematic reviews and meta-analyses. As a case study, we carried out a scoping review to identify and examine duplicated network meta-analyses (NMAs) in a specific disease setting where several novel therapies have recently emerged: hormone-sensitive metastatic prostate cancer (mHSPC)., Methods: MEDLINE and EMBASE were systematically searched, in January 2020, for indirect or mixed treatment comparisons or network meta-analyses of the systemic treatments docetaxel and abiraterone acetate in the mHSPC setting, with a time-to-event outcome reported on the hazard-ratio scale. Eligibility decisions were made, and data extraction performed, by two independent reviewers., Results: A total of 13 eligible reviews were identified, analysing between 3 and 8 randomised comparisons, and comprising between 1773 and 7844 individual patients. Although the included trials and treatments showed a high degree of overlap, we observed considerable variation between identified reviews in terms of review aims, eligibility criteria and included data, statistical methodology, reporting and inference. Furthermore, crucial methodological details and specific source data were often unclear., Conclusions and Recommendations: Variation across duplicated NMAs, together with reporting inadequacies, may compromise identification of best-performing treatments. Particularly in fast-moving fields, review authors should be aware of all relevant studies, and of other reviews with potential for overlap or duplication. We recommend that review protocols be published in advance, with greater clarity regarding the specific aims or scope of the project, and that reports include information on how the work builds upon existing knowledge. Source data and results should be clearly and completely presented to allow unbiased interpretation., (© 2022. The Author(s).)