Evidence map›Paper›PMID 31226934›Full record

ArticleBMC medical research methodology2019

Methods to adjust for multiple comparisons in the analysis and sample size calculation of randomised controlled trials with multiple primary outcomes.

Victoria Vickerstaff, Rumana Z Omar, Gareth Ambler

Erratum issuedAbstract read
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In one paragraph

Article in BMC medical research methodology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 107 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
107citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

107 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  18. Antidepressant medications have differential effects on REM sleep without atonia quantified by chin and upper extremity EMG.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2026
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47 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Victoria VickerstaffMarie Curie Palliative Care Research Department, Division of Psychiatry, University College London, Gower Street, London, WC1E 6BT, UK. v.vickerstaff@ucl.ac.uk.ORCID 0000-0002-3119-670X
Rumana Z OmarDepartment of Statistical Science, University College London, Gower Street, London, WC1E 6BT, UK.
Gareth AmblerDepartment of Statistical Science, University College London, Gower Street, London, WC1E 6BT, UK.

Funding

Marie Curie MCCC-FCO-11-UMarie Curie MCCC-FCO-16-U
6 · The paper itself

Abstract

backgroundMultiple 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.

methodsWe 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.

resultsThe 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.

Indexed as

AlgorithmsData Interpretation, StatisticalModels, TheoreticalComputer SimulationHumansOutcome Assessment, Health CareRandomized Controlled Trials as TopicSample SizeMultiple comparison methodsMultiple outcomeRandomised controlled trialsSample sizeStatistical analysis

Identifiers

PMID31226934
PMCPMC6588937

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.