JAMA Guide to Statistics and Methods

This JAMA Guide to Statistics and Methods explains the use of historical controls—persons who had received a specific control treatment in a previous study—when randomizing participants to that control treatment in a subsequent trial may not be practical or ethical.

This JAMA Guide to Statistics and Methods discusses the early stopping of clinical trials for futility due to lack of evidence supporting the desired benefit, evidence of harm, or practical issues that make successful completion unlikely.

This JAMA Guide to Statistics and Methods article examines conditional power, calculated while a trial is ongoing and based on both the currently observed data and an assumed treatment effect for future patients.

This JAMA Guide to Statistics and Methods explains sequential, multiple assignment, randomized trial (SMART) study designs, in which some or all participants are randomized at 2 or more decision points depending on the participant’s response to prior treatment.

This Guide to Statistics and Methods describes the use of target trial emulation to design an observational study so it preserves the advantages of a randomized clinical trial, points out the limitations of the method, and provides an example of its use.

This Guide to Statistics and Methods provides an overview of the use of adjustment for baseline characteristics in the analysis of randomized clinical trials and emphasizes several important considerations.

This Guide to Statistics and Methods provides an overview of regression models for ordinal outcomes, including an explanation of why they are used and their limitations.

This Guide to Statistics and Methods provides an overview of patient-reported outcome measures for clinical research, emphasizes several important considerations when using them, and points out their limitations.

This JAMA Guide to Statistics and Methods describes collider bias, illustrates examples in directed acyclic graphs, and explains how it can threaten the internal validity of a study and the accurate estimation of causal relationships in randomized clinical trials and observational studies.

This JAMA Guide to Statistics and Methods discusses instrumental variable analysis, a method designed to reduce or eliminate unobserved confounding in observational studies, with the goal of achieving unbiased estimation of treatment effects.

This JAMA Guide to Statistics and Methods discusses the basics of causal directed acyclic graphs, which are useful tools for communicating researchers’ understanding of the potential interplay among variables and are commonly used for mediation analysis.

This JAMA Guide to Statistics and Methods discusses the CONSERVE guidelines, which address how to report extenuating circumstances that lead to a modification in trial design, conduct, or analysis.

This JAMA Guide to Statistics and Methods discusses cardinality matching, a method for finding the largest possible number of matched pairs in an observational data set, with the goal of balanced and representative samples of study participants between groups.

This Guide to Statistics and Methods discusses the various approaches to estimating variability in treatment effects, including heterogeneity of treatment effect, which was used to assess the association between surgery to close patent foramen ovale and risk of recurrent stroke in patients who presented with a stroke in a related JAMA article.

This Guide to Statistics and Methods describes how confidence intervals can be used to help in the interpretation of nonsignificant findings across all study designs.

This JAMA Guide to Statistics and Methods describes why interim analyses are performed during group sequential trials, provides examples of the limitations of interim analyses, and provides guidance on interpreting the results of interim analyses performed during group sequential trials.

This JAMA Guide to Statistics and Methods takes a look at estimands, estimators, and estimates in the context of randomized clinical trials and suggests several qualities that make for good estimands, including their scope, ability to summarize treatment effects, external validity, and ability to provide good estimates.

This JAMA Guide to Statistics and Methods describes how ACC/AHA guidelines are formatted to rate class (denoting strength of a recommendation) and level (indicating the level of evidence on which a recommendation is based) and summarizes the strengths and benefits of this rating system in comparison with other commonly used ones.

This JAMA Guide to Statistics and Methods describes how intention-to-treat, per-protocol, and as-treated approaches to analysis differ with regard to the patient population and treatment assignments and their implications for interpretation of treatment effects in clinical trials.

This JAMA Guide to Statistics and Methods describes various methods to handle trial participant nonadherence to study interventions.