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Background: Clinical trials play a crucial role in biomedical research, and it is important to register them in public registries to ensure transparency and prevent research waste. In this study, we wished to determine what steps need to be taken to identify every clinical trial run in India that has been registered in any of the (non-Indian) World Health Organization-recognised primary registries. Of the 16 registries, we studied all except that of the European Union, which will be studied separately., Methods: Two methodologies were employed for each registry, except for four that did not facilitate one or the other method. Methodology A involved downloading all the records in a registry and querying them. Methodology B involved conducting a search via the registry website., Results: Only four registries provided consistent results with both methodologies. Seven registries had different results from the two methodologies. Of these, in four cases, in Methodology A one field indicated that the study ran in India, while another indicated otherwise., Conclusions: The above-mentioned ambiguities should be addressed by the concerned registries. Overall, this study reinforces the need for improved data accuracy and transparency in clinical trial registries and emphasizes the importance of resolving complications faced by users while navigating the registries. Ensuring accurate and comprehensive registration of clinical trials is essential for meta-research and the use of such data by a variety of stakeholders., (© 2024. The Author(s).)

Background and Objectives: Comprehending the research dataset is crucial for obtaining reliable and valid outcomes. Health analysts must have a deep comprehension of the data being analyzed. This comprehension allows them to suggest practical solutions for handling missing data, in a clinical data source. Accurate handling of missing values is critical for producing precise estimates and making informed decisions, especially in crucial areas like clinical research. With data's increasing diversity and complexity, numerous scholars have developed a range of imputation techniques. To address this, we conducted a systematic review to introduce various imputation techniques based on tabular dataset characteristics, including the mechanism, pattern, and ratio of missingness, to identify the most appropriate imputation methods in the healthcare field., Materials and Methods: We searched four information databases namely PubMed, Web of Science, Scopus, and IEEE Xplore, for articles published up to September 20, 2023, that discussed imputation methods for addressing missing values in a clinically structured dataset. Our investigation of selected articles focused on four key aspects: the mechanism, pattern, ratio of missingness, and various imputation strategies. By synthesizing insights from these perspectives, we constructed an evidence map to recommend suitable imputation methods for handling missing values in a tabular dataset., Results: Out of 2955 articles, 58 were included in the analysis. The findings from the development of the evidence map, based on the structure of the missing values and the types of imputation methods used in the extracted items from these studies, revealed that 45% of the studies employed conventional statistical methods, 31% utilized machine learning and deep learning methods, and 24% applied hybrid imputation techniques for handling missing values., Conclusion: Considering the structure and characteristics of missing values in a clinical dataset is essential for choosing the most appropriate data imputation technique, especially within conventional statistical methods. Accurately estimating missing values to reflect reality enhances the likelihood of obtaining high-quality and reusable data, contributing significantly to precise medical decision-making processes. Performing this review study creates a guideline for choosing the most appropriate imputation methods in data preprocessing stages to perform analytical processes on structured clinical datasets., (© 2024. The Author(s).)

Background: Lack of diversity in participants throughout the research process limits the generalizability of findings and may contribute to health disparities. There are unique challenges to recruitment of families to pediatric cancer research studies, especially for those from disadvantaged backgrounds. Thus, there is a need to evaluate the most effective recruitment and retention strategies to optimize equitable recruitment of diverse participants., Methods: The present study adapted and implemented methods outlined previously in the literature. These previous efforts were developed to address barriers to pediatric research, behavioral health intervention research and research with Black adolescents. Recruitment and retention strategies are described across four different time points: pre-approach, initial connection, building connection and follow-up. Eligible families of children with a pediatric cancer diagnosis were approached during a routine oncology visit. Once consented, enrollment and retention rates over three timepoints of data collection were recorded and evaluated., Results: Results indicated high rates of enrollment (86%) and retention (95%) for eligible participants. There were no trends in heightened attrition for any specific subgroup., Conclusions: The findings of this study are promising and suggest these recruitment and retention strategies may be useful in recruiting individuals from disadvantaged backgrounds., (© 2024. The Author(s).)

Background: Since the start of the COVID-19 outbreak, a large number of COVID-19-related papers have been published. However, concerns about the risk of expedited science have been raised. We aimed at reviewing and categorizing COVID-19-related medical research and to critically appraise peer-reviewed original articles., Methods: The data sources were Pubmed, Cochrane COVID-19 register study, arXiv, medRxiv and bioRxiv, from 01/11/2019 to 01/05/2020. Peer-reviewed and preprints publications related to COVID-19 were included, written in English or Chinese. No limitations were placed on study design. Reviewers screened and categorized studies according to i) publication type, ii) country of publication, and iii) topics covered. Original articles were critically appraised using validated quality assessment tools., Results: Among the 11,452 publications identified, 10,516 met the inclusion criteria, among which 7468 (71.0%) were peer-reviewed articles. Among these, 4190 publications (56.1%) did not include any data or analytics (comprising expert opinion pieces). Overall, the most represented topics were infectious disease (n = 2326, 22.1%), epidemiology (n = 1802, 17.1%), and global health (n = 1602, 15.2%). The top five publishing countries were China (25.8%), United States (22.3%), United Kingdom (8.8%), Italy (8.1%) and India (3.4%). The dynamic of publication showed that the exponential growth of COVID-19 peer-reviewed articles was mainly driven by publications without original data (mean 261.5 articles ± 51.1 per week) as compared with original articles (mean of 69.3 ± 22.3 articles per week). Original articles including patient data accounted for 713 (9.5%) of peer-reviewed studies. A total of 576 original articles (80.8%) showed intermediate to high risk of bias. Last, except for simulation studies that mainly used large-scale open data, the median number of patients enrolled was of 102 (IQR = 37-337)., Conclusions: Since the beginning of the COVID-19 pandemic, the majority of research is composed by publications without original data. Peer-reviewed original articles with data showed a high risk of bias and included a limited number of patients. Together, these findings underscore the urgent need to strike a balance between the velocity and quality of research, and to cautiously consider medical information and clinical applicability in a pressing, pandemic context. SYSTEMATIC REVIEW REGISTRATION: https://osf.io/5zjyx/.

Background: Percentiles are widely used in scientific research for determining the comparative magnitude and reference limit of quantitative measurements. The investigations for point and interval estimation of normal percentiles are well documented in the literature. However, the corresponding statistical tests of hypothesis have received relatively little attention., Methods: To facilitate data analysis and design planning of percentile study, this paper aims to present hypothesis testing procedures and associated power functions for assessing the difference, noninferiority, and equivalence of normal percentiles., Results: Numerical illustrations about drug dissolution are provided to demonstrate the usefulness of the suggested exact approaches and the deficiency of approximate methods., Conclusions: The exact approaches are superior to the approximate methods on the basis of control of Type I errors. Computer algorithms are constructed to implement the recommended test procedures and sample size calculations for percentile analysis.

Background: Patient and public involvement (PPI) has become enshrined as an important pillar of health services empirical research, including PPI roles during stages of research development and analysis and co-design approaches. Whilst user participation has been central to qualitative evidence synthesis (QES) for decades, as seen in the Cochrane consumer network and guidelines, meta-ethnography has been slow to incorporate user participation and published examples of this occurring within meta-ethnography are sparse. In this paper, drawing upon our own experience of conducting a meta-ethnography, we focus on what it means in practice to 'express a synthesis' (stage 7). We suggest the methodological importance of 'expression' in Noblit and Hare's seven stage process (Noblit, GW and Hare, RD. Meta-ethnography: synthesizing qualitative studies, 1988) has been overlooked, and in particular, opportunities for PPI user participation within it., Methods: Meta-ethnography comprises a seven-stage process of evidence synthesis. Noblit and Hare describe the final 7th stage of the meta-ethnography process as 'expression of synthesis', emphasizing co-construction of findings with the audience. In a previous study we conducted a meta-ethnography exploring patient and student experience of medical education within primary care contexts. We subsequently presented and discussed initial meta-ethnography findings with PPI (students and patients) in focus groups and interviews. We transcribed patient and student PPI interpretations of synthesis findings. As a research team, we then translated these into our existing meta-ethnography findings., Results: We describe, with examples, the process of involving PPI in stage 7 of meta-ethnography and discuss three methodological implications of incorporating PPI within an interpretative approach to QES: (1) we reflect on the construct hierarchy of user participants' interpretations and consider whether incorporating these additional 1st order, 2nd level constructs implies an additional logic of 3rd order 2nd level constructs of the QES team; (2) we discuss the link between PPI user participation and what Noblit and Hare may have meant by ideas of 'expression' and 'audience' as integral to stage 7; and (3) we link PPI user participation to Noblit and Hare's underlying theory of social explanation, i.e. how expression of the synthesis is underpinned by ideas of translation and that the synthesis must be 'translated in the audience's (user participants) particular language'., Conclusions: The paper aims to complement recent attempts in the literature to refine and improve guidance on conducting a meta-ethnography, highlighting opportunities for PPI user participation in the processes of interpretation, translation and expression. We discuss the implications of user participation in meta-ethnography on ideas of 'generalisability'.

The quality of medical research importantly depends, among other aspects, on a valid statistical planning of the study, analysis of the data, and reporting of the results, which is usually guaranteed by a biostatistician. However, there are several related professions next to the biostatistician, for example epidemiologists, medical informaticians and bioinformaticians. For medical experts, it is often not clear what the differences between these professions are and how the specific role of a biostatistician can be described. For physicians involved in medical research, this is problematic because false expectations often lead to frustration on both sides. Therefore, the aim of this article is to outline the tasks and responsibilities of biostatisticians in clinical trials as well as in other fields of application in medical research.

Background: This article describes a bibliometric review of the scientific production, geographical distribution, collaboration, impact, and subject area focus of pneumonia research indexed on the Web of Science over a 15-year period., Methods: We searched the Web of Science database using the Medical Subject Heading (MeSH) of "Pneumonia" from January 1, 2001 to December 31, 2015. The only document types we studied were original articles and reviews, analyzing descriptive indicators by five-year periods and the scientific production by country, adjusting for population, economic, and research-related parameters., Results: A total of 22,694 references were retrieved. The number of publications increased steadily over time, from 981 publications in 2001 to 1977 in 2015 (R 2  = 0.956). The most productive country was the USA (38.49%), followed by the UK (7.18%) and Japan (5.46%). Research production from China increased by more than 1000%. By geographical area, North America (42.08%) and Europe (40.79%) were most dominant. Scientific production in low- and middle-income countries more than tripled, although their overall contribution to the field remained limited (< 15%). Overall, 18.8% of papers were the result of an international collaboration, although this proportion was much higher in sub-Saharan Africa (46.08%) and South Asia (23.43%). According to the specific MeSH terms used, articles focused mainly on "Pneumonia, Bacterial" (19.99%), followed by "Pneumonia, Pneumococcal" (7.02%) and "Pneumonia, Ventilator-Associated" (6.79%)., Conclusions: Pneumonia research increased steadily over the 15-year study period, with Europe and North America leading scientific production. About a fifth of all papers reflected international collaborations, and these were most evident in papers from sub-Saharan Africa and South Asia.

Background: The paper opens with a brief history of two of the major intellectual components of the recent utilitarian turn in clinical research, namely 'pragmatic trials' and 'implementation science'. The two schools of thought developed independently and the paper scrutinises their mutual compatibilities and incompatibilities, asking: i) what do the leading advocates of pragmatic trials assume about the transfer of research findings to real-world practice and ii) what role pragmatic trials can and should play in the evaluation of implementation science strategies., Methods: The paper utilises 'explication de texte': i) providing a close reading of the inferential logics contained in major published expositions of the two paradigms, and ii) interrogating the conclusions of a pragmatic trial of an intervention providing guidelines on retinal screening aimed at family practitioners., Results: The paper is in two parts. Part 1 unearths some significant incommensurability - the pragmatic trial literature retains an antiquated view of knowledge transfer and is overly optimistic about the wide applicability the findings of pragmatic trials to 'real world' conditions. Part 2 of the paper outlines an empirical strategy to better penetrate the mechanisms of knowledge transfer and to tackle the issue of the generalisabilty of research findings in implementation science., Conclusions: Pragmatism, classically, is about problem solving and the melding of perspectives. The core research requirement in implementation science is a fundamental shift from the narrow shoulders of pragmatic trials to a model of explanation building based upon a multi-case, multi-method body of evidence.

Background: Use of big data is becoming increasingly popular in medical research. Since big data-based projects differ notably from classical research studies, both in terms of scope and quality, a debate is apt as to whether big data require new approaches to scientific reasoning different from those established in statistics and philosophy of science., Main Text: The progressing digitalization of our societies generates vast amounts of data that also become available for medical research. Here, the big promise of big data is to facilitate major improvements in the treatment, diagnosis and prevention of diseases. An ongoing examination of the idiosyncrasies of big data is therefore essential to ensure that the field stays congruent with the principles of evidence-based medicine. We discuss the inherent challenges and opportunities of big data in medicine from a methodological point of view, particularly highlighting the relative importance of causality and correlation in commercial and medical research settings. We make a strong case for upholding the distinction between exploratory data analysis facilitating hypothesis generation and confirmatory approaches involving hypothesis validation. An independent verification of research results will be ever more important in the context of big data, where data quality is often hampered by a lack of standardization and structuring., Conclusions: We argue that it would be both unnecessary and dangerous to discard long-established principles of data generation, analysis and interpretation in the age of big data. While many medical research areas may reasonably benefit from big data analyses, they should nevertheless be complemented by carefully designed (prospective) studies.

Background: Evaluation of complex interventions (CI) is challenging for health researchers and requires innovative approaches. The objective of this work is to present the main methods used to evaluate CI., Methods: A systematic review of the scientific literature was conducted to identify methods used for the evaluation of CI. We searched MEDLINE via PubMed databases for articles including an evaluation or a pilot study of a complex intervention, published in a ten-year period. Key-words of this research were ("complex intervention*" AND "evaluation")., Results: Among 445 identified articles, 100 research results or protocols were included. Among them, 5 presented 2 different types of design in the same publication, thus our work included 105 designs. Individual randomized controlled trials (IRCT) represented 21.9% (n = 23) of evaluation designs, randomized clinical trials adaptations 44.8% (n = 47), quasi -experimental designs and cohort study 19.0% (n = 20), realist evaluation 6.7% (n = 7) and other cases studies and other approaches 8.6% (n = 9). A process/mechanisms analysis was included in 80% (n = 84) of these designs., Conclusion: A range of methods can be used successively or combined at various steps of the evaluation approach. A framework is proposed to situate each of the designs with respect to evaluation questions. The growing interest of researchers in alternative methods and the development of their use must be accompanied by conceptual and methodological research in order to more clearly define their principles of use.

Background: Linear regression analysis is a widely used statistical technique in practical applications. For planning and appraising validation studies of simple linear regression, an approximate sample size formula has been proposed for the joint test of intercept and slope coefficients., Methods: The purpose of this article is to reveal the potential drawback of the existing approximation and to provide an alternative and exact solution of power and sample size calculations for model validation in linear regression analysis., Results: A fetal weight example is included to illustrate the underlying discrepancy between the exact and approximate methods. Moreover, extensive numerical assessments were conducted to examine the relative performance of the two distinct procedures., Conclusions: The results show that the exact approach has a distinct advantage over the current method with greater accuracy and high robustness.

Background: The availability of clinical and therapeutic data drawn from medical records and administrative databases has entailed new opportunities for clinical and epidemiologic research. However, these databases present inherent limitations which may render them prone to new biases. We aimed to conduct a structured review of biases specific to observational clinical studies based on secondary databases, and to propose strategies for the mitigation of those biases., Methods: Scoping review of the scientific literature published during the period 2000-2018 through an automated search of MEDLINE, EMBASE and Web of Science, supplemented with manually cross-checking of reference lists. We included opinion essays, methodological reviews, analyses or simulation studies, as well as letters to the editor or retractions, the principal objective of which was to highlight the existence of some type of bias in pharmacoepidemiologic studies using secondary databases., Results: A total of 117 articles were included. An increasing trend in the number of publications concerning the potential limitations of secondary databases was observed over time and across medical research disciplines. Confounding was the most reported category of bias (63.2% of articles), followed by selection and measurement biases (47.0% and 46.2% respectively). Confounding by indication (32.5%), unmeasured/residual confounding (28.2%), outcome misclassification (28.2%) and "immortal time" bias (25.6%) were the subcategories most frequently mentioned., Conclusions: Suboptimal use of secondary databases in pharmacoepidemiologic studies has introduced biases in the studies, which may have led to erroneous conclusions. Methods to mitigate biases are available and must be considered in the design, analysis and interpretation phases of studies using these data sources.

Background: The concept of "mechanism" is central to realist approaches to research, yet research teams struggle to operationalize and apply the concept in empirical research. Our large, interdisciplinary research team has also experienced challenges in making the concept useful in our study of the implementation of models of integrated community-based primary health care (ICBPHC) in three international jurisdictions (Ontario and Quebec in Canada, and in New Zealand)., Methods: In this paper we summarize definitions of mechanism found in realist methodological literature, and report an empirical example of a realist analysis of the implementation ICBPHC., Results: We use our empirical example to illustrate two points. First, the distinction between contexts and mechanisms might ultimately be arbitrary, with more distally located mechanisms becoming contexts as research teams focus their analytic attention more proximally to the outcome of interest. Second, the relationships between mechanisms, human reasoning, and human agency need to be considered in greater detail to inform realist-informed analysis; understanding these relationships is fundamental to understanding the ways in which mechanisms operate through individuals and groups to effect the outcomes of complex health interventions., Conclusions: We conclude our paper with reflections on human agency and outline the implications of our analysis for realist research and realist evaluation.

Background: Pelvic Girdle Pain (PGP) is an important cause of disability and economic cost worldwide. There is a need for effective preventative and management strategies. Emerging studies measure a variety of outcomes rendering synthesis and translation to clinical practice difficult. A Core Outcome Set (COS) can address this problem by ensuring that data are relevant, useful and usable for making well-informed healthcare choices. The aim of this study is to develop a consensus-based PGP-COS, including agreement on methods (e.g. instruments) for measuring the construct outcomes in the COS for use in research and clinical practice. Furthermore, as there is uncertainty as to whether incorporating stakeholder interviews in addition to conducting a systematic review to determine an initial list of outcomes for the Delphi survey, or, whether using different rating scales in a Delphi survey impacts on the final COS, we propose to embed two methodological studies within the PGP-COS development process to address these questions., Methods: The PGP-COS study will include five phases: (1) A systematic review of the literature and semi-structured interviews with 15 patients (three countries) to form the initial list of outcomes for the Delphi survey; (2) A 3-round Delphi including patients, clinicians, researchers and service providers; (3) A systematic review of methods for measuring the outcomes in the preliminary PGP-COS identified in the Delphi survey; (4) A face-to-face consensus meeting to agree on the final PGP-COS and methods for measuring the COS; (5) Global dissemination. To address the methodological questions, we will assess the number and type of outcomes, in the final PGP-COS, that were exclusively derived from the interviews. Secondly, we will randomise Delphi survey participants to either a 5-point or 9-point importance rating scale, and examine potential differences in 'important' ratings between the groups., Discussion: There is currently no COS for measuring/monitoring PGP in trials and clinical practice. A PGP-COS will ensure that relevant outcomes are measured using appropriate measurement instruments for patients with PGP globally., Core Outcome Set Registration: This PGP-COS was registered with COMET (Core Outcome Measures for Effectiveness Trials) in January 2017 ( http://www.comet-initiative.org/studies/details/958 ).

Background: We demonstrate an application of Group-Based Trajectory Modeling (GBTM) based on the beta distribution. It is offered as an alternative to the normal distribution for modeling continuous longitudinal data that are poorly fit by the normal distribution even with censoring. The primary advantage of the beta distribution is the flexibility of the shape of the density function., Methods: GBTM is a specialized application of finite mixture modeling designed to identify clusters of individuals who follow similar trajectories. Like all finite mixture models, GBTM requires that the distribution of the data composing the mixture be specified. To our knowledge this is the first demonstration of the use of the beta distribution in GBTM. A case study of a beta-based GBTM analyzes data on the neurological activity of comatose cardiac arrest patients., Results: The case study shows that the summary measure of neurological activity, the suppression ratio, is not well fit by the normal distribution but due to the flexibility of the shape of the beta density function, the distribution of the suppression ratio by trajectory appears to be well matched by the estimated beta distribution by group., Conclusions: The addition of the beta distribution to the already available distributional alternatives in software for estimating GBTM is a valuable augmentation to extant distributional alternatives.

Background: Biomarker-based analyses are commonly reported in observational epidemiological studies; however currently there are no specific study quality assessment tools to assist evaluation of conducted research. Accounting for study design and biomarker measurement would be important for deriving valid conclusions when conducting systematic data evaluation., Methods: We developed a study quality assessment tool designed specifically to assess biomarker-based cross-sectional studies (BIOCROSS) and evaluated its inter-rater reliability. The tool includes 10-items covering 5 domains: 'Study rational', 'Design/Methods', 'Data analysis', 'Data interpretation' and 'Biomarker measurement', aiming to assess different quality features of biomarker cross-sectional studies. To evaluate the inter-rater reliability, 30 studies were distributed among 5 raters and intraclass correlation coefficients (ICC-s) were derived from respective ratings., Results: The estimated overall ICC between the 5 raters was 0.57 (95% Confidence Interval (CI): 0.38-0.74) indicating a good inter-rater reliability. The ICC-s ranged from 0.11 (95% CI: 0.01-0.27) for the domain 'Study rational' to 0.56 (95% CI: 0.40-0.72) for the domain 'Data interpretation'., Conclusion: BIOCROSS is a new study quality assessment tool suitable for evaluation of reporting quality from cross-sectional epidemiological studies employing biomarker data. The tool proved to be reliable for use by biomedical scientists with diverse backgrounds and could facilitate comprehensive review of biomarker studies in human research.

Background: Consumers, clinicians, policymakers and researchers require high quality evidence to guide decision-making in child health. Though Cochrane systematic reviews (SRs) are a well-established source of evidence, little is known about the characteristics of non-Cochrane child-relevant SRs. To complement published descriptions of Cochrane SRs, we aimed to characterize the epidemiologic, methodological, and reporting qualities of non-Cochrane child-relevant SRs published in 2014., Methods: English-language child-relevant SRs of quantitative primary research published outside the Cochrane Library in 2014 were eligible for this descriptive analysis. A research librarian searched MEDLINE, CINAHL, Web of Science, and PubMed in August 2015. A single reviewer screened articles for inclusion; a second verified the excluded studies. Reviewers extracted: general characteristics of the review; included study characteristics; methodological approaches. We performed univariate analyses and presented the findings narratively., Results: We identified 1598 child-relevant SRs containing a median (IQR) 19 (11, 33) studies. These originated primarily from high-income countries (n = 1247, 78.0%) and spanned 47 of the 53 Cochrane Review Groups. Most synthesized therapeutic (n = 753, 47.1%) or epidemiologic (n = 701, 43.8%) evidence. Though 39.3% (n = 628) of SRs included evidence related to children only, few were published in pediatric-specific journals (n = 283, 17.7%). Reporting quality seemed poor based on the items we assessed; few reviews mentioned an a-priori protocol (n = 246, 15.4%) or registration (n = 111, 6.9%), and only 23.4% (n = 374) specified a primary outcome. Many SRs relied solely on evidence from non-RCTs (n = 796, 49.8%). Less than two-thirds (n = 953, 59.6%) appraised the quality of included studies and assessments of the certainty of the body of evidence were rare (n = 102, 6.4%)., Conclusions: Child-relevant Cochrane SRs are a known source of high quality evidence in pediatrics. There exists, however, an abundance of evidence from non-Cochrane SRs that may be complementary. Our findings show that high-quality non-Cochrane SRs may not be practical nor easy for knowledge users to find. Improvements are needed to ensure that evidence syntheses published outside of the Cochrane Library adhere to the high standard of conduct and reporting characteristic of Cochrane SRs.

Background: Multilevel models for non-normal outcomes are widely used in medical and health sciences research. While methods for interpreting fixed effects are well-developed, methods to quantify and interpret random cluster variation and compare it with other sources of variation are less established. Random cluster variation, sometimes referred to as general contextual effects (GCE), may be the main focus of a study; therefore, easily interpretable methods are needed to quantify GCE. We propose a Reference Effect Measure (REM) approach to 1) quantify GCE and compare it to individual subject and cluster covariate effects, and 2) quantify relative magnitudes of GCE and variation from sets of measured factors., Methods: To illustrate REM, we consider a two-level mixed logistic model with patients clustered within hospitals and a random intercept for hospitals. We compare patients at hospitals at given percentiles of the estimated random effect distribution to patients at a median or 'reference' hospital. These estimates are then compared numerically and graphically to individual fixed effects to quantify GCE in the context of effects of other measured variables (aim 1). We then extend this approach by comparing variation from the random effect distribution to variation from sets of fixed effects to understand their magnitudes relative to overall outcome variation (aim 2)., Results: Using an example of initiation of rhythm control treatment in atrial fibrillation (AF) patients within the Veterans Affairs (VA), we use REM to demonstrate that random variation across hospitals (GCE) in initiation of treatment is substantially greater than that due to most individual patient factors, and explains at least as much variation in treatment initiation as do all patient factors combined. These results are contrasted with a relatively small GCE compared with patient factors in 1 year mortality following hospitalization for AF patients., Conclusions: REM provides a means of quantifying random effect variation (GCE) with multilevel data and can be used to explore drivers of outcome variation. This method is easily interpretable and can be presented visually. REM offers a simple, interpretable approach for evaluating questions of growing importance in the study of health care systems.

Background: Limited translational success in critical care medicine is thought to be in part due to inadequate methodology, study design, and reporting in preclinical studies. The purpose of this study was to compare reporting of core features of experimental rigor: blinding, randomization, and power calculations in critical care medicine animal experimental research. We hypothesized that these study design characteristics were more frequently reported in 2015 versus 2005., Methods: We performed an observational bibliometric study to grade manuscripts on blinding, randomization, and power calculations. Chi-square tests and logistic regression were used for analysis. Inter-rater agreement was assessed using kappa and Gwet's AC1., Results: A total of 825 articles from seven journals were included. In 2005, power estimations were reported in 2%, randomization in 35%, and blinding in 20% (n = 482). In 2015, these metrics were included in 9, 47, and 36% of articles (n = 343). The increase in proportion for the metrics tested was statistically significant (p < 0.001, p = 0.002, and p < 0.001)., Conclusions: Only a minority of published manuscripts in critical care medicine journals reported on recommended study design steps to increase rigor. Routine justification for the presence or absence of blinding, randomization, and power calculations should be considered to better enable readers to assess potential sources of bias.

Background: Percentiles are widely used as reference limits for determining the relative magnitude and substantial importance of quantitative measurements. An important application is the advocated Bland-Altman limits of agreement., Methods: To contribute to the data analysis and design planning of reference limit or percentile research, the purpose of this paper is twofold. The first is to clarify the statistical features of interval estimation procedures for normal percentiles. The second goal is to provide sample size procedures for precise interval estimation of normal percentiles., Results: The delineation demonstrates the theoretical connections between different pivotal quantities for obtaining exact confidence intervals. Moreover, the seemingly accurate approximate methods with equidistant from the principal estimators are shown to have undesirable confidence limits. It is found that the optimal sample size has a minimum for median or mean, and increases as the percentile approaches the extremes., Conclusions: The exact interval procedure should be used in preference to the approximate methods. Computer algorithms are presented to implement the suggested interval precision and sample size calculations for planning percentile research.

Background: Interpreting graphs of continuous safety variables can be complicated because differences in age, gender, and testing site methodologies data may give rise to multiple reference limits. Furthermore, data below the lower limit of normal are compressed relative to those points above the upper limit of normal. The objective of this study is to develop a graphing technique that addresses these issues and is visually intuitive., Methods: A mock dataset with multiple reference ranges is initially used to develop the graphing technique. Formulas are developed for conditions where data are above the upper limit of normal, normal, below the lower limit of normal, and below the lower limit of normal when the data value equals zero. After the formulae are developed, an anonymized dataset from an actual set of trials for an approved drug is evaluated comparing the technique developed in this study to standard graphical methods., Results: Formulas are derived for the novel graphing method based on multiples of the normal limits. The formula for values scaled between the upper and lower limits of normal is a novel application of a readily available scaling formula. The formula for the lower limit of normal is novel and addresses the issue of this value potentially being indeterminate when the result to be scaled as a multiple is zero., Conclusions: The formulae and graphing method described in this study provides a visually intuitive method to graph continuous safety data including laboratory values, vital sign data.

Background: When discussing results medical research articles often tear substantive and statistical (methodical) contributions apart, just as if both were independent. Consequently, reasoning on bias tends to be vague, unclear and superficial. This can lead to over-generalized, too narrow and misleading conclusions, especially for causal research questions., Main Body: To get the best possible conclusion, substantive and statistical expertise have to be integrated on the basis of reasonable assumptions. While statistics should raise questions on the mechanisms that have presumably created the data, substantive knowledge should answer them. Building on the related principle of Bayesian thinking, we make seven specific and four general proposals on writing a discussion section., Conclusion: Misinterpretation could be reduced if authors explicitly discussed what can be concluded under which assumptions. Informed on the resulting conditional conclusions other researchers may, according to their knowledge and beliefs, follow a particular conclusion or, based on other conditions, arrive at another one. This could foster both an improved debate and a better understanding of the mechanisms behind the data and should therefore enable researchers to better address bias in future studies.

Background: Prospective study protocols and registrations can play a significant role in reducing incomplete or selective reporting of primary biomedical research, because they are pre-specified blueprints which are available for the evaluation of, and comparison with, full reports. However, inconsistencies between protocols or registrations and full reports have been frequently documented. In this systematic review, which forms part of our series on the state of reporting of primary biomedical, we aimed to survey the existing evidence of inconsistencies between protocols or registrations (i.e., what was planned to be done and/or what was actually done) and full reports (i.e., what was reported in the literature); this was based on findings from systematic reviews and surveys in the literature., Methods: Electronic databases, including CINAHL, MEDLINE, Web of Science, and EMBASE, were searched to identify eligible surveys and systematic reviews. Our primary outcome was the level of inconsistency (expressed as a percentage, with higher percentages indicating greater inconsistency) between protocols or registration and full reports. We summarized the findings from the included systematic reviews and surveys qualitatively., Results: There were 37 studies (33 surveys and 4 systematic reviews) included in our analyses. Most studies (n = 36) compared protocols or registrations with full reports in clinical trials, while a single survey focused on primary studies of clinical trials and observational research. High inconsistency levels were found in outcome reporting (ranging from 14% to 100%), subgroup reporting (from 12% to 100%), statistical analyses (from 9% to 47%), and other measure comparisons. Some factors, such as outcomes with significant results, sponsorship, type of outcome and disease speciality were reported to be significantly related to inconsistent reporting., Conclusions: We found that inconsistent reporting between protocols or registrations and full reports of primary biomedical research is frequent, prevalent and suboptimal. We also identified methodological issues such as the need for consensus on measuring inconsistency across sources for trial reports, and more studies evaluating transparency and reproducibility in reporting all aspects of study design and analysis. A joint effort involving authors, journals, sponsors, regulators and research ethics committees is required to solve this problem.

Background: Different confounder adjustment strategies were used to estimate odds ratios (ORs) in case-control study, i.e. how many confounders original studies adjusted and what the variables are. This secondary data analysis is aimed to detect whether there are potential biases caused by difference of confounding factor adjustment strategies in case-control study, and whether such bias would impact the summary effect size of meta-analysis., Methods: We included all meta-analyses that focused on the association between breast cancer and passive smoking among non-smoking women, as well as each original case-control studies included in these meta-analyses. The relative deviations (RDs) of each original study were calculated to detect how magnitude the adjustment would impact the estimation of ORs, compared with crude ORs. At the same time, a scatter diagram was sketched to describe the distribution of adjusted ORs with different number of adjusted confounders., Results: Substantial inconsistency existed in meta-analysis of case-control studies, which would influence the precision of the summary effect size. First, mixed unadjusted and adjusted ORs were used to combine individual OR in majority of meta-analysis. Second, original studies with different adjustment strategies of confounders were combined, i.e. the number of adjusted confounders and different factors being adjusted in each original study. Third, adjustment did not make the effect size of original studies trend to constringency, which suggested that model fitting might have failed to correct the systematic error caused by confounding., Conclusions: The heterogeneity of confounder adjustment strategies in case-control studies may lead to further bias for summary effect size in meta-analyses, especially for weak or medium associations so that the direction of causal inference would be even reversed. Therefore, further methodological researches are needed, referring to the assessment of confounder adjustment strategies, as well as how to take this kind of bias into consideration when drawing conclusion based on summary estimation of meta-analyses.

Background: Prediction models and prognostic scores have been increasingly popular in both clinical practice and clinical research settings, for example to aid in risk-based decision making or control for confounding. In many medical fields, a large number of prognostic scores are available, but practitioners may find it difficult to choose between them due to lack of external validation as well as lack of comparisons between them., Methods: Borrowing methodology from network meta-analysis, we describe an approach to Multiple Score Comparison meta-analysis (MSC) which permits concurrent external validation and comparisons of prognostic scores using individual patient data (IPD) arising from a large-scale international collaboration. We describe the challenges in adapting network meta-analysis to the MSC setting, for instance the need to explicitly include correlations between the scores on a cohort level, and how to deal with many multi-score studies. We propose first using IPD to make cohort-level aggregate discrimination or calibration scores, comparing all to a common comparator. Then, standard network meta-analysis techniques can be applied, taking care to consider correlation structures in cohorts with multiple scores. Transitivity, consistency and heterogeneity are also examined., Results: We provide a clinical application, comparing prognostic scores for 3-year mortality in patients with chronic obstructive pulmonary disease using data from a large-scale collaborative initiative. We focus on the discriminative properties of the prognostic scores. Our results show clear differences in performance, with ADO and eBODE showing higher discrimination with respect to mortality than other considered scores. The assumptions of transitivity and local and global consistency were not violated. Heterogeneity was small., Conclusions: We applied a network meta-analytic methodology to externally validate and concurrently compare the prognostic properties of clinical scores. Our large-scale external validation indicates that the scores with the best discriminative properties to predict 3 year mortality in patients with COPD are ADO and eBODE.

Background: Within epidemiological and clinical research, missing data are a common issue and often over looked in publications. When the issue of missing observations is addressed it is usually assumed that the missing data are 'missing at random' (MAR). This assumption should be checked for plausibility, however it is untestable, thus inferences should be assessed for robustness to departures from missing at random., Methods: We highlight the method of pattern mixture sensitivity analysis after multiple imputation using colorectal cancer data as an example. We focus on the Dukes' stage variable which has the highest proportion of missing observations. First, we find the probability of being in each Dukes' stage given the MAR imputed dataset. We use these probabilities in a questionnaire to elicit prior beliefs from experts on what they believe the probability would be in the missing data. The questionnaire responses are then used in a Dirichlet draw to create a Bayesian 'missing not at random' (MNAR) prior to impute the missing observations. The model of interest is applied and inferences are compared to those from the MAR imputed data., Results: The inferences were largely insensitive to departure from MAR. Inferences under MNAR suggested a smaller association between Dukes' stage and death, though the association remained positive and with similarly low p values., Conclusions: We conclude by discussing the positives and negatives of our method and highlight the importance of making people aware of the need to test the MAR assumption.

Background: Data capture is one of the most expensive phases during the conduct of a clinical trial and the increasing use of electronic health records (EHR) offers significant savings to clinical research. To facilitate these secondary uses of routinely collected patient data, it is beneficial to know what data elements are captured in clinical trials. Therefore our aim here is to determine the most commonly used data elements in clinical trials and their availability in hospital EHR systems., Methods: Case report forms for 23 clinical trials in differing disease areas were analyzed. Through an iterative and consensus-based process of medical informatics professionals from academia and trial experts from the European pharmaceutical industry, data elements were compiled for all disease areas and with special focus on the reporting of adverse events. Afterwards, data elements were identified and statistics acquired from hospital sites providing data to the EHR4CR project., Results: The analysis identified 133 unique data elements. Fifty elements were congruent with a published data inventory for patient recruitment and 83 new elements were identified for clinical trial execution, including adverse event reporting. Demographic and laboratory elements lead the list of available elements in hospitals EHR systems. For the reporting of serious adverse events only very few elements could be identified in the patient records., Conclusions: Common data elements in clinical trials have been identified and their availability in hospital systems elucidated. Several elements, often those related to reimbursement, are frequently available whereas more specialized elements are ranked at the bottom of the data inventory list. Hospitals that want to obtain the benefits of reusing data for research from their EHR are now able to prioritize their efforts based on this common data element list.

Background: Many health researchers are clinicians. Dual-role experiences are common for clinician-researchers in research involving patient-participants, even if not their own patients. To extend the existing body of literature on why dual-role is experienced, we aimed to develop a typology of common catalysts for dual-role experiences to help clinician-researchers plan and implement methodologically and ethically sound research., Methods: Systematic searching of Medline, CINAHL, PsycINFO, Embase and Scopus (inception to 28.07.2014) for primary studies or first-person reflexive reports of clinician-researchers' dual-role experiences, supplemented by reference list checking and Google Scholar scoping searches. Included articles were loaded in NVivo for analysis. The coding was focused on how dual-role was evidenced for the clinician-researchers in research involving patients. Procedures were completed by one researcher (MB) and independently cross-checked by another (JHS). All authors contributed to extensive discussions to resolve all disagreements about initial coding and verify the final themes., Results: Database searching located 7135 records, resulting in 29 included studies, with the addition of 7 studies through reference checks and scoping searches. Two overarching themes described the most common catalysts for dual-role experiences - ways a research role can involve patterns of behaviour typical of a clinical role, and the developing connection that starts to resemble a clinician-patient relationship. Five subthemes encapsulated the clinical patterns commonly repeated in research settings (clinical queries, perceived agenda, helping hands, uninvited clinical expert, and research or therapy) and five subthemes described concerns about the researcher-participant relationship (clinical assumptions, suspicion and holding back, revelations, over-identification, and manipulation). Clinician-researchers use their clinical skills in health research in ways that set up a relationship resembling that of clinician-patient. Clinicians' ingrained orientation to patients' needs can be in tension with their research role, and can set up ethical and methodological challenges., Conclusion: The typology we developed outlines the common ways dual-role is experienced in research involving clinician-researchers and patient-participants, and perhaps the inevitability of the experience given the primacy accorded to patient well-being. The typology offers clinician-researchers a framework for grappling with the ethical and methodological implications of dual-role throughout the research process, including planning, implementation, monitoring and reporting.

Background: The ethical, methodological, and technical aspects of pediatric research, often results in complications and delays in implementation. Our objective was to identify factors associated with the implementation duration of hospital-based pediatric studies., Methods: All hospital-based pediatric studies sponsored by AP-HP between 2002 and 2008 were retrospectively identified. Association of the funding mechanism and methodological factors with the implementation duration was assessed using a multivariable mixed linear model. Pharmaceutical factors were explored as part of a subgroup analysis restricted to the studies involving drug therapy. Given that we took an exploratory approach, factors associated with implementation duration with p < 0.10 were kept in the final models., Results: A total of 139 studies were evaluated. The median implementation duration was 17.1 months (range: 0.9-55.3 months), and tended to increase over time (from 14.9 [25(th) percentile-75(th) percentile: 11.5-19.9] months in 2002 to 23.7 [15.2-31.0] months in 2008, p = 0.01). External (coefficient [95 % confidence interval]: -7.7 [-11.9;-3.5] months, p < 0.001) and internal funding (-5.3 95 % CI [-9.8;-0.8], p = 0.02) compared to governmental funding and number of centers (-0.1 95 % CI[-0.2;0.02] months for 1 center increase, p = 0.07) were associated with reduced duration, whereas interventional study (either involving drug therapy (6.0 95 % CI[0.7;11.3] months, p = 0.03 or not (3.5 95 % CI[-0.3;7.3] months, p = 0.06) was associated with increased duration compared to observational study. Regarding the 35 studies involving drug therapy, external funding decreased duration (-6.7 95 % CI[-13.2;-0.2] months, p = 0.05), whereas studies involving solely a pediatric population (7.8 95 % CI[1.1;14.5] months, p = 0.01) (compared to mixed adult-pediatric population), a placebo-controlled design (6.6 95 % CI[0.9;12.3] months, p = 0.01), and inappropriate drug formulation for at least one drug used in the study (6.9 95 % CI[-0.2;14.0] months, p = 0.06) were associated with increased duration., Conclusion: Implementation of hospital-based pediatric studies primarily faced delays when they were interventional and, in particular, when they involved drug therapy. Regarding the latter, difficulties that resulted in delayed studies arose with respect to the supply of drugs and placebo in age-appropriate dosages and route of administration. Therefore, difficulties related to the use of pharmaceuticals need to be anticipated earlier in order to avoid implementation delays.

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.

Background: Biomarkers of exposures such as infection or environmental chemicals can be measured in small volumes of blood extracted from newborn dried blood spots (DBS) underscoring their potential utility for population-based research. However, few studies have evaluated the feasibility and utility of this resource; particularly the factors associated with parental consent, and the ability to retrieve banked samples with sufficient remaining volume for epidemiologic research., Methods: At 8 months postpartum, 5,034 mothers of infants born (2008-2010) in New York (57 counties excluding New York City) were asked to consent for the use of residual DBS for the quantification of cytokines and environmental chemicals. Mothers were part of the Upstate KIDS study, a longitudinal birth cohort designed to evaluate child development through 3 years of age. Information on parental and infant characteristics was obtained from birth certificates and maternal report at 4 months postpartum. Multivariate logistic regression was used to identify factors associated with parental consent and with successful retrieval of DBS., Results: Sixty-two percent (n = 3125) of parents consented. Factors significantly associated with consent included non-Hispanic ethnicity (odds ratio 2.04; 95% CI: 1.43-2.94), parity (1.29; 1.05-1.57), maternal obesity (1.42; 1.11-1.80) and reported alcohol use during pregnancy (1.51; 1.12-2.06). However, these associations corresponded to small absolute differences in proportions (4 to 8%), suggesting that the two groups remained comparable. Infant characteristics such as preterm delivery did not significantly differ by consent status among singletons and only ventilator use (OR 2.39; 95% CI: 1.06-5.41) remained borderline significant among twins in adjusted analyses. Among consented infants, 99% had at least one 3.2 mm punch successfully retrieved for biomarker analyses and 84% had a full DBS circle available., Conclusion: Parental characteristics varied slightly by consent, and the availability of samples for research purposes was high, demonstrating the feasibility of this resource for population based research.

Background: In many studies, some information might not be available for the whole cohort, some covariates, or even the outcome, might be ascertained in selected subsamples. These studies are part of a broad category termed two-phase studies. Common examples include the nested case-control and the case-cohort designs. For two-phase studies, appropriate weighted survival estimates have been derived; however, no estimator of cumulative incidence accounting for competing events has been proposed. This is relevant in the presence of multiple types of events, where estimation of event type specific quantities are needed for evaluating outcome., Methods: We develop a non parametric estimator of the cumulative incidence function of events accounting for possible competing events. It handles a general sampling design by weights derived from the sampling probabilities. The variance is derived from the influence function of the subdistribution hazard., Results: The proposed method shows good performance in simulations. It is applied to estimate the crude incidence of relapse in childhood acute lymphoblastic leukemia in groups defined by a genotype not available for everyone in a cohort of nearly 2000 patients, where death due to toxicity acted as a competing event. In a second example the aim was to estimate engagement in care of a cohort of HIV patients in resource limited setting, where for some patients the outcome itself was missing due to lost to follow-up. A sampling based approach was used to identify outcome in a subsample of lost patients and to obtain a valid estimate of connection to care., Conclusions: A valid estimator for cumulative incidence of events accounting for competing risks under a general sampling design from an infinite target population is derived.

Background: A public-private (51/49 %) partnership was initiated in 2007 in France to improve the attractiveness of French sites in industry-sponsored international clinical trials. This initiative developed and implemented a combination of structuring actions and support actions. Here we report the assessment of the impact after 6 years on participation of French study sites in industry-sponsored clinical trials., Methods: We performed a prospective before-after study of clinical research activities in French public hospitals to assess the impact of actions developed and implemented by CeNGEPS. The programme involved a combination of structuring actions (establishment of sites of excellence, national networks and dedicated clinical research assistants (CRAs)), support actions (tools, templates and training) and competitive budget allocation for sites or networks based on performance. The impact was assessed using the following performance criteria: 1) reduction of the delay to contract signature to ≤ 60 days for 80 % of the trial sites; 2) inclusion of ≥80 % of the planned number of patients by at least 80 % of trial sites; 3) closure of <15 % of trials sites without patients enrolled., Results: In 2013, the median delay to contract signature was: 55 days, compared with 76 days in 2008 (27.6 % reduction), 50.5 % of all sites and 58 % of sites with a dedicated CRA included ≥80 % of the planned number of patients compared with 44.8 % in 2008 (12.7 % increase) and 21.3 % of all sites and 9 % of sites with a dedicated CRA closed with no patients included, compared with 26.4 % in 2008 (19.3 and 65.9 %, respectively)., Conclusions: These results provide evidence that it is possible to improve a country's attractiveness for industry-sponsored clinical research. The two main actions, i.e. establishing sites of excellence throughout the country with well-trained, dedicated staff and establishing a national network of clinical investigators, could be adapted to other countries in Western Europe to improve Europe's attractiveness to industry-funded trials.

Background: Multiple interventions meta-analysis has been recommended in the methodological literature as a tool for evidence synthesis when a heterogeneous set of interventions is included in the same review—and, more recently, when a heterogeneous set of complex interventions is included. However, there is little guidance on the use of this method with complex interventions. This article suggests two approaches to model complexity and heterogeneity through this method., Discussion: 'Clinically meaningful units' groups interventions by modality or similar theory of change, whereas 'components and dismantling' separates out interventions into combinations of components and either groups interventions by the combination of components they demonstrate or extracts effects for each identified component and, possibly, interactions between components. Future work in systematic review methodology should aim to understand how to develop taxonomies of components or theories of change that are internally relevant to the studies in these multiple interventions meta-analyses., Summary: Despite little meaningful prior guidance to its use in this context, multiple interventions meta-analysis has the potential to be a useful tool for synthesising heterogeneous sets of complex interventions. Researchers should choose an approach in accordance with their specific aims in their systematic review.

Background: With the increase of clinical trial costs during the last decades, the design of feasibility studies has become an essential process to reduce avoidable and costly protocol amendments. This design includes timelines, targeted sites and budget, together with a list of eligibility criteria that potential participants need to match. The present work was designed to assess the value of obtaining potential study participant counts using an automated patient count cohort system for large multi-country and multi-site trials: the Electronic Health Records for Clinical Research (EHR4CR) system., Methods: The evaluation focuses on the accuracy of the patient counts and the time invested to obtain these using the EHR4CR platform compared to the current questionnaire based process. This evaluation will assess the patient counts from ten clinical trials at two different sites. In order to assess the accuracy of the results, the numbers obtained following the two processes need to be compared to a baseline number, the "alloyed" gold standard, which was produced by a manual check of patient records., Results: The patient counts obtained using the EHR4CR system were in three evaluated trials more accurate than the ones obtained following the current process whereas in six other trials the current process counts were more accurate. In two of the trials both of the processes had counts within the gold standard's confidence interval. In terms of efficiency the EHR4CR protocol feasibility system proved to save approximately seven calendar days in the process of obtaining patient counts compared to the current manual process., Conclusions: At the current stage, electronic health record data sources need to be enhanced with better structured data so that these can be re-used for research purposes. With this kind of data, systems such as the EHR4CR are able to provide accurate objective patient counts in a more efficient way than the current methods. Additional research using both structured and unstructured data search technology is needed to assess the value of unstructured data and to compare the amount of efforts needed for data preparation.

Background: Missing data are common in medical research, which can lead to a loss in statistical power and potentially biased results if not handled appropriately. Multiple imputation (MI) is a statistical method, widely adopted in practice, for dealing with missing data. Many academic journals now emphasise the importance of reporting information regarding missing data and proposed guidelines for documenting the application of MI have been published. This review evaluated the reporting of missing data, the application of MI including the details provided regarding the imputation model, and the frequency of sensitivity analyses within the MI framework in medical research articles., Methods: A systematic review of articles published in the Lancet and New England Journal of Medicine between January 2008 and December 2013 in which MI was implemented was carried out., Results: We identified 103 papers that used MI, with the number of papers increasing from 11 in 2008 to 26 in 2013. Nearly half of the papers specified the proportion of complete cases or the proportion with missing data by each variable. In the majority of the articles (86%) the imputed variables were specified. Of the 38 papers (37%) that stated the method of imputation, 20 used chained equations, 8 used multivariate normal imputation, and 10 used alternative methods. Very few articles (9%) detailed how they handled non-normally distributed variables during imputation. Thirty-nine papers (38%) stated the variables included in the imputation model. Less than half of the papers (46%) reported the number of imputations, and only two papers compared the distribution of imputed and observed data. Sixty-six papers presented the results from MI as a secondary analysis. Only three articles carried out a sensitivity analysis following MI to assess departures from the missing at random assumption, with details of the sensitivity analyses only provided by one article., Conclusions: This review outlined deficiencies in the documenting of missing data and the details provided about imputation. Furthermore, only a few articles performed sensitivity analyses following MI even though this is strongly recommended in guidelines. Authors are encouraged to follow the available guidelines and provide information on missing data and the imputation process.

Background: In systematic reviews and meta-analysis, researchers often pool the results of the sample mean and standard deviation from a set of similar clinical trials. A number of the trials, however, reported the study using the median, the minimum and maximum values, and/or the first and third quartiles. Hence, in order to combine results, one may have to estimate the sample mean and standard deviation for such trials., Methods: In this paper, we propose to improve the existing literature in several directions. First, we show that the sample standard deviation estimation in Hozo et al.'s method (BMC Med Res Methodol 5:13, 2005) has some serious limitations and is always less satisfactory in practice. Inspired by this, we propose a new estimation method by incorporating the sample size. Second, we systematically study the sample mean and standard deviation estimation problem under several other interesting settings where the interquartile range is also available for the trials., Results: We demonstrate the performance of the proposed methods through simulation studies for the three frequently encountered scenarios, respectively. For the first two scenarios, our method greatly improves existing methods and provides a nearly unbiased estimate of the true sample standard deviation for normal data and a slightly biased estimate for skewed data. For the third scenario, our method still performs very well for both normal data and skewed data. Furthermore, we compare the estimators of the sample mean and standard deviation under all three scenarios and present some suggestions on which scenario is preferred in real-world applications., Conclusions: In this paper, we discuss different approximation methods in the estimation of the sample mean and standard deviation and propose some new estimation methods to improve the existing literature. We conclude our work with a summary table (an Excel spread sheet including all formulas) that serves as a comprehensive guidance for performing meta-analysis in different situations.

Background: Multiple imputation is a reliable tool to deal with missing data and is becoming increasingly popular in biostatistics. However, building a model with interactions that are not specified a priori, in the presence of missing data, presents a challenge. On the one hand, the interactions are needed to impute the data, while on the other hand, the data is needed to identify the interactions. The objective of this study was to present a way in which this challenge can be addressed., Methods: This paper investigates two strategies in which model development with interactions is achieved using a single data set generated from the Expectation Maximization (EM) algorithm. Imputation using both the fully conditional specification approach and the multivariate normal approach is carried out and results are compared. The strategies are illustrated with data from a study of ambient pollution and childhood asthma in Durban, South Africa., Results: The different approaches to model building and imputation yielded similar results despite the data being mainly categorical. Both strategies investigated for building the model using the multivariate normal imputed data resulted in the identical set of variables and interactions being identified; while models built using data imputed by fully conditional specification were marginally different for the two strategies. It was found that, for both imputation approaches, model building with backward elimination applied to the initial EM data set was easier to implement, and produced good results, compared to those from a complete case analysis., Conclusions: Developing a predictive model including interactions with data that suffers from missingness is easily done by identifying significant interactions and then applying backward elimination to a single data set imputed from the EM algorithm. It is hoped that this idea can be further developed and, by addressing this practical dilemma, there will be increased adoption of multiple imputation in medical research when data suffers from missingness.

Background: Medical researchers often use longitudinal observational studies to examine how risk factors predict change in health over time. Selective attrition and inappropriate modeling of regression toward the mean (RTM) are two potential sources of bias in such studies., Method: The current study used Monte Carlo simulations to examine bias related to selective attrition and inappropriate modeling of RTM in the study of prediction of change. This was done for multiple regression (MR) and change score analysis., Results: MR provided biased results when attrition was dependent on follow-up and baseline variables to quite substantial degrees, while results from change score analysis were biased when attrition was more strongly dependent on variables at one time point than the other. A positive association between the predictor and change in the health variable was underestimated in MR and overestimated in change score analysis due to selective attrition. Inappropriate modeling of RTM, on the other hand, lead to overestimation of this association in MR and underestimation in change score analysis. Hence, selective attrition and inappropriate modeling of RTM biased the results in opposite directions., Conclusion: MR and change score analysis are both quite robust against selective attrition. The interplay between selective attrition and inappropriate modeling of RTM emphasizes that it is not an easy task to assess the degree to which obtained results from empirical studies are over- versus underestimated due to attrition or RTM. Researchers should therefore use modern techniques for handling missing data and be careful to model RTM appropriately.

Background: Recruitment is a challenge in developing population-representative pregnancy and birth cohorts., Methods: We developed a collaborative recruitment infrastructure (CRI) to recruit pregnant women for 4 pregnancy cohorts using: faxes from obstetrical offices, in-clinic recruiters, university and funder-driven free-media events, paid-media, and attendance at relevant tradeshows. Recruitment rates and demographic differences were compared between recruitment methods., Results: We received 5008 referrals over 40 months. Compared to fax, free-media referrals were 13 times more likely to be recruited (OR 13.0, 95% CI 4.2, 40.4: p < 0.001) and paid-media referrals were 4 times more likely to be recruited (OR 4.6, 95% CI 2.1, 10.3: p < 0.001). Among paid-media advertisements, free-to-read print (e.g. Metro) was the most effective (OR 3.3, 95% CI 2.3, 4.5: p < 0.05). Several demographic differences were identified between recruitment methods and against a reference population. Between recruitment methods, media recruits had a similar proportion families with incomes ≥ $40,000 (paid-media: 94.4%; free-media: 93.3%) compared to fax recruits (95.7%), while in-clinic recruits were less likely to have family incomes ≥ $40,000 (88.8%, p < 0.05). Maternal recruits from fax and in-clinic were more likely to attend university (Fax: 92.6%, in-clinic 89.8%) versus the reference population (52.0%; p < 0.05 for both) and both were less likely to smoke (Fax: 6.8%, in-clinic 4.2%) versus reference (18.6%; p < 0.05 for both). However, while fax referrals were more likely to be Caucasian (85.9% versus reference 77.5%; p < 0.05), in-clinic referrals were not significantly different (78.2%; P > 0.05)., Conclusion: Recruitment methods result in different recruitment rates and participant demographics. A variety of methods are required to recruit a generalizable sample.

Background: Network meta-analysis can be used to combine results from several randomized trials involving more than two treatments. Potential inconsistency among different types of trial (designs) differing in the set of treatments tested is a major challenge, and application of procedures for detecting and locating inconsistency in trial networks is a key step in the conduct of such analyses., Methods: Network meta-analysis can be very conveniently performed using factorial analysis-of-variance methods. Inconsistency can be scrutinized by inspecting the design × treatment interaction. This approach is in many ways simpler to implement than the more common approach of using treatment-versus-control contrasts., Results: We show that standard regression diagnostics available in common linear mixed model packages can be used to detect and locate inconsistency in trial networks. Moreover, a suitable definition of factors and effects allows devising significance tests for inconsistency., Conclusion: Factorial analysis of variance provides a convenient framework for conducting network meta-analysis, including diagnostic checks for inconsistency.

Background: In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models., Methods: In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided., Results: If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the response data are generated from a discrete multinomial distribution with support on (0,1)., Conclusions: The linear regression model, the variable-dispersion beta regression model and the fractional logit regression model all perform well across the simulation experiments under consideration. When employing beta regression to estimate covariate effects on (0,1) response data, researchers should ensure their dispersion sub-model is properly specified, else inferential errors could arise.

Background: Analysis of biomedical research and publications in a country or group of countries is used to monitor research progress and trends. This study aims to assess the performance of biomedical research in the Arab world during 2001-2005 and to compare it with other Middle Eastern non-Arab countries., Methods: PubMed and Science Citation Index Expanded (SCI-expanded) were searched systematically for the original biomedical research publications and their citation frequencies of 16 Arab nations and three non-Arab Middle Eastern countries (Iran, Israel and Turkey), all of which are classified as middle or high income countries., Results: The 16 Arab countries together have 5775 and 14,374 original research articles listed by PubMed and SCI-expanded, respectively, significantly less (p < 0.001) than the other three Middle Eastern countries (25,643 and 49,110). The Arab countries also scored less when the data were normalized to population, gross domestic product (GDP), and GDP/capita. The publications from the Arab countries also have a significantly lower (p < 0.001) citation frequency., Conclusion: The Arab world is producing fewer biomedical publications of lower quality than other Middle Eastern countries. Studies are needed to clarify the causes and to propose strategies to improve the biomedical research status in Arab countries.

Background: Citation counts are often regarded as a measure of the utilization and contribution of published articles. The objective of this study is to assess whether statistical reporting and statistical errors in the analysis of the primary outcome are associated with the number of citations received., Methods: We evaluated all original research articles published in 1996 in four psychiatric journals. The statistical and reporting quality of each paper was assessed and the number of citations received up to 2005 was obtained from the Web of Science database. We then examined whether the number of citations was associated with the quality of the statistical analysis and reporting., Results: A total of 448 research papers were included in the citation analysis. Unclear or inadequate reporting of the research question and primary outcome were not statistically significantly associated with the citation counts. After adjusting for journal, extended description of statistical procedures had a positive effect on the number of citations received. Inappropriate statistical analysis did not affect the number of citations received. Adequate reporting of the primary research question, statistical methods and primary findings were all associated with the journal visibility and prestige., Conclusion: In this cohort of published research, measures of reporting quality and appropriate statistical analysis were not associated with the number of citations. The journal in which a study is published appears to be as important as the statistical reporting quality in ensuring dissemination of published medical science.