Baseline characteristics and statistical power in randomized controlled trials: Selection, prognostic targeting, or covariate adjustment?

IMPACT Study Grp, Bob Roozenbeek, Andrew I. R. Maas, Hester F. Lingsma, Isabella Butcher, Juan Lu, Anthony Marmarou, Gillian McHugh, James Weir, Gordon D. Murray, Ewout W. Steyerberg

Research output: Contribution to journalArticlepeer-review


Objective. Heterogeneity of patients is a common problem in randomized controlled trials (RCTs) in various fields of clinical research. We aimed to investigate the potential benefits of different approaches for dealing with heterogeneity in a case study on traumatic brain injury (TBI).

Design and Setting: Statistical modeling studies in three surveys and six randomized controlled trials. Patients: Individual patient data (n = 8033) from the IMPACT database.

Interventions: We investigated the statistical power and efficiency of randomized controlled trials (RCTs) in relation to (1) selection according to baseline characteristics, (2) prognostic targeting (i.e., excluding those with a relatively extreme prognosis), and (3) covariate-adjusted analysis. Statistical power was expressed as the required sample size for obtaining 80% power and efficiency as the relative change in study duration, reflecting both gains in power and adverse effects on recruitment. Uniform and targeted treatment effects were simulated for 6 month unfavorable outcome.

Results: For a uniform treatment effect, selection resulted in a sample size reduction of 33% in the surveys and 5% in the RCTs, but decreased recruitment by 65% and 41%, respectively. Hence, the relative study duration was prolonged (surveys: +95%; RCTs: +60%). Prognostic targeting resulted in sample size reductions of 28% and 17%, and increased relative study duration by +5% in surveys and +11% in the RCTs. Covariate adjustment reduced sample sizes by 30% and 16%, respectively, and did not affect recruitment. For a targeted treatment effect, the sample size reductions by selection (surveys: 47%; RCTs: 20%) and prognostic targeting (surveys: 49%; RCTs: 41%) were larger and adverse effects on recruitment smaller.

Conclusions. The benefits of selection and prognostic targeting in terms of statistical power are reversed by adverse effects on recruitment. Covariate adjusted analysis in a broadly selected group of patients is advisable if a uniform treatment effect is assumed, since there is no decrease in recruitment. (Crit Care Med 2009; 37:2683-2690)

Original languageEnglish
Pages (from-to)2683-2690
Number of pages8
JournalCritical Care Medicine
Issue number10
Publication statusPublished - Oct 2009


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    Murray, G.



    Project: Research

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