BMC Medical Research Methodology · 2019 · 204 citations · 31 references
Public HealthBiostatisticsEpidemiologic MethodsExperimental DesignMeta-analysisClinical TrialsEffect Size EstimationMultiple Comparison AdjustmentMultiple Testing CorrectionPower AnalysisRandomized Controlled TrialsFamilywise Error RateHochberg's ProcedureMultiple Comparisons CorrectionSample Size CalculationSample Size EstimationSample Size ReductionSimulation Studies
BACKGROUND: Multiple primary outcomes may be specified in randomised controlled trials (RCTs). When analysing multiple outcomes it's important to control the family wise error rate (FWER). A popular approach to do this is to adjust the p-values corresponding to each statistical test used to investigate the intervention effects by using the Bonferroni correction. It's also important to consider the power of the trial to detect true intervention effects. In the context of multiple outcomes, depending on the clinical objective, the power can be defined as: 'disjunctive power', the probability of detecting at least one true intervention effect across all the outcomes or 'marginal power' the probability of finding a true intervention effect on a nominated outcome. We provide practical recommendations on which method may be used to adjust for multiple comparisons in the sample size calculation and the analysis of RCTs with multiple primary outcomes. We also discuss the implications on the sample size for obtaining 90% disjunctive power and 90% marginal power. METHODS: We use simulation studies to investigate the disjunctive power, marginal power and FWER obtained after applying Bonferroni, Holm, Hochberg, Dubey/Armitage-Parmar and Stepdown-minP adjustment methods. Different simulation scenarios were constructed by varying the number of outcomes, degree of correlation between the outcomes, intervention effect sizes and proportion of missing data. RESULTS: The Bonferroni and Holm methods provide the same disjunctive power. The Hochberg and Hommel methods provide power gains for the analysis, albeit small, in comparison to the Bonferroni method. The Stepdown-minP procedure performs well for complete data. However, it removes participants with missing values prior to the analysis resulting in a loss of power when there are missing data. The sample size requirement to achieve the desired disjunctive power may be smaller than that required to achieve the desired marginal power. The choice between whether to specify a disjunctive or marginal power should depend on the clincial objective.
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A Simple Sequentially Rejective Multiple Test Procedure
Sture Holm · Scandinavian Journal of Statistics · 1979 · 21.8K citations
A sharper Bonferroni procedure for multiple tests of significance
Yosef Hochberg · Biometrika · 1988 · 4.7K citations
The prevention and handling of the missing data
Hyun Kang · Korean journal of anesthesiology · 2013 · 1.7K citations · Full text