BMJ Research Methods and Reporting: General topics & statistics volume 1

Sample size calculations: should the emperor's clothes be off the peg or made to measure? Implementation research: what it is and how to do it How to obtain the confidence interval from a P value The Cochrane Collaboration's tool for assessing risk of bias in randomised trials Clinical prediction rules Prognosis research strategy (PROGRESS) 1: A framework for researching clinical outcomes Out of sight but not out of mind: how to search for unpublished clinical trial evidence Interpreting diagnostic accuracy studies for patient care Demystifying trial networks and network meta-analysis Interpreting and reporting clinical trials with results of borderline significance Assessing equity in systematic reviews: realising the recommendations of the Commission on Social Determinants of Health Assessing the value of diagnostic tests: a framework for designing and evaluating trials Three techniques for integrating data in mixed methods studies Dangers of non-specific composite outcome measures in clinical trials Cascade diagrams for depicting complex interventions in randomised trials Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials Brackets (parentheses) in formulas Comparisons within randomised groups can be very misleading Uncertainties in baseline risk estimates and confidence in treatment effects How to obtain the P value from a confidence interval Diagnostic accuracy studies: how to report and analyse inconclusive test results Making inferences on treatment effects from real world data: propensity scores, confounding by indication, and other perils for the unwary in observational research The impact of outcome reporting bias in randomised controlled trials on a cohort of systematic reviews Prognosis research strategy (PROGRESS) 4: Stratified medicine research Value of composite reference standards in diagnostic research Statistics Notes: Missing outcomes in randomised trials Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls