Simulation studies are commonly used in methodological research for the empirical evaluation of data analysis methods. They generate artificial datasets under specified mechanisms and compare the p...

Background The aim of equality, equity, diversity, and inclusion (EDI) is to ensure fair treatment, equal opportunities, equitable outcomes, and representation. The NIHR Research Design Service (RDS) ...

Background Well designed and properly executed randomised trials are considered the most reliable evidence on the benefits of healthcare interventions. However, there is overwhelming evidence that the...

This open access book explores how expert consensus shapes science, guiding truth, policy, and practice, while proposing improvements.

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Mediation analysis has gotten a lot of flak, including classic titles such as “Yes, but what’s the mechanism? (Don’t expect an easy answer)” (Bullock et al., 2010), “What mediation analysis can (not) ...

This is a guide to protect ourselves and to preserve what is good about turning data into visual things.

Reference-based multiple imputation provides a practical information anchored tool for inferences about the treatment effect for a treatment policy estimand with a longitudinal binary outcome. The latent multivariate normal model is the preferred implementation.

Network meta-analysis (NMA), a statistical technique that allows systematic reviewers to simultaneously compare more than two alternatives, makes use of indirect evidence from studies comparing interv...

This report presents the explanation and elaboration paper for the CONSORT (Consolidated Standards of Reporting Trials) 2010 and SPIRIT (Standard Protocol Items: Recommendations for Interventional Tri...

Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem t…

Lukas Lengersdorff and Claus Lamm discuss three misconceptions that stand in the way of an informed discussion.

Longitudinal studies are frequently used in medical research and involve collecting repeated measures on individuals over time. Observations from the same individual are invariably correlated and thu....

As medical statisticians, we welcome revisions to the Declaration of Helsinki emphasising the importance of appropriate methods to reduce research waste.1 The declaration now states that rigorous stu...

Practices that introduce systematic bias are common in most scientific disciplines, including toxicology. Selective reporting of results and publication bias are two of the most prevalent sources o...

Systematic reviews are a type of evidence synthesis in which authors develop explicit eligibility criteria, collect all the available studies that meet these criteria, and summarize results using repr...

AbstractBackground. Immortal time is a period of follow-up during which death or the study outcome cannot occur by design. Bias from immortal time has been

This article presents the CONSORT (consolidated standards of reporting trials) extension for cluster randomised crossover trials. A cluster randomised crossover trial involves randomisation of groups ...

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Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biological Products

The current Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system instructs appraisers to evaluate whether individual observational studies have sufficiently adjusted for c...

Multiple tools exist for assessing the methodological quality of diagnosis and prognosis research. It can be challenging to decide on when to use which tool. We aimed to provide an overview of existin...

Using Bayesian hierarchical models, CDER statisticians are improving our understanding of how drugs affect different groups of patients

Code review is the process of either informally (as part of a group, as colleagues) or formally (as part of the peer review process) checking and evaluating each other's code and is a critical method...

If you can’t find an answer to a question in one of the other tabs, click on the “Discussions” tab, which will navigate you to the GitHub discussions forum for all Q&A. If you post a question, someone from our team will get back to you.

Miguel A. Hernán, MD, DrPH, professor of epidemiology, Harvard T.H. Chan School of Public Health, discusses Target Trial Emulation: A Framework for Causal Inference From Observational Data with JAMA Statistical Editor Roger J. Lewis, MD, PhD.