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Cited by 65Statistical Methods in Clinical TrialsAdvanced Causal Inference TechniquesStatistical Methods in Epidemiology

Randomized Controlled Trials With Time-to-Event Outcomes: How Much Does Prespecified Covariate Adjustment Increase Power?

Hernández, Adrián V., Eijkemans, Marinus J.C., Steyerberg, Ewout W.·2006·Annals of Epidemiology
DOI 10.1016/j.annepidem.2005.09.007
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Abstract

Purpose We evaluated the effects of various strategies of covariate adjustment on type I error, power, and potential reduction in sample size in randomized controlled trials (RCTs) with time-to-event outcomes.Methods We used Cox models in simulated data sets with different treatment effects (hazard ratios [HRs] = 1, 1.4, and 1.7), covariate effects (HRs = 1, 2, and 5), covariate prevalences (10% and 50%), and censoring levels (no, low, and high). Treatment and a single covariate were dichotomous. We examined the sample size that gives the same power as an unadjusted analysis for three strategies: prespecified, significant predictive, and significant imbalance.Results Type I error generally was at the nominal level. The power to detect a true treatment effect was greater with adjusted than unadjusted analyses, especially with prespecified and significant-predictive strategies. Potential reductions in sample size with a covariate HR between 2 and 5 were between 15% and 44% (covariate prevalence 50%) and between 4% and 12% (covariate prevalence 10%). The significant-imbalance strategy yielded small reductions. The reduction was greater with stronger covariate effects, but was independent of treatment effect, sample size, and censoring level.Conclusions Adjustment for one predictive baseline characteristic yields greater power to detect a true treatment effect than unadjusted analysis, without inflation of type I error and with potentially moderate reductions in sample size. Analysis of RCTs with time-to-event outcomes should adjust for predictive covariates.

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  • Adjusting for baseline covariates, including those used in stratified randomisation, can lead to improved power to detect an intervention effect, particularly when a baseline covariate is prognostic of the outcome.

    “Adjusting for baseline covariates, including those used in any stratified randomisation, is often advised in the analysis, particularly when a baseline covariate is prognostic of the outcome, as it can lead to improved power to detect an intervention effect.408 409 410”

    From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [410]

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Authors
Hernández, A. V., Eijkemans, M. J. C., & Steyerberg, E. W.
Affiliations
Erasmus MC (NL)
Corresponding author
Hernández, Adrián V.
ORCID
Venue
Annals of Epidemiology
Publisher
Elsevier BV
Published
2006-01
Volume
16
Issue
1
Pages
41-48
ISSN
1047-2797 (ISSN-L)
DOI
10.1016/j.annepidem.2005.09.007
OpenAlex
W2019474022
Cited by
65
References
26
Citations by year
2026: 4 · 2025: 3 · 2024: 5 · 2023: 3 · 2022: 6 · 2021: 6 · 2020: 3 · 2019: 2 · 2018: 1 · 2017: 1 · 2016: 4 · 2015: 4 · 2014: 3 · 2013: 6 · 2012: 2
FWCI
1.38
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Annals of Epidemiology (publishedVersion)
  • PubMed (publishedVersion)
  • EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
  • EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords
covariate adjustment, significant imbalance, sample size reduction, statistical power, type I error, time-to-event outcomes, Cox proportional hazards model, censoring
MeSH terms
Computer Simulation, Data Interpretation, Statistical, Humans, Netherlands, Time Factors, Proportional Hazards Models, Randomized Controlled Trials as Topic, Treatment Outcome, Sample Size

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