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The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies

BACKGROUND: Adjustment for prognostic covariates can lead to increased power in the analysis of randomized trials. However, adjusted analyses are not often performed in practice. METHODS: We used simulation to examine the impact of covariate adjustment on 12 outcomes from 8 studies across a range of...

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Detalles Bibliográficos
Autores principales: Kahan, Brennan C, Jairath, Vipul, Doré, Caroline J, Morris, Tim P
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4022337/
https://www.ncbi.nlm.nih.gov/pubmed/24755011
http://dx.doi.org/10.1186/1745-6215-15-139
Descripción
Sumario:BACKGROUND: Adjustment for prognostic covariates can lead to increased power in the analysis of randomized trials. However, adjusted analyses are not often performed in practice. METHODS: We used simulation to examine the impact of covariate adjustment on 12 outcomes from 8 studies across a range of therapeutic areas. We assessed (1) how large an increase in power can be expected in practice; and (2) the impact of adjustment for covariates that are not prognostic. RESULTS: Adjustment for known prognostic covariates led to large increases in power for most outcomes. When power was set to 80% based on an unadjusted analysis, covariate adjustment led to a median increase in power to 92.6% across the 12 outcomes (range 80.6 to 99.4%). Power was increased to over 85% for 8 of 12 outcomes, and to over 95% for 5 of 12 outcomes. Conversely, the largest decrease in power from adjustment for covariates that were not prognostic was from 80% to 78.5%. CONCLUSIONS: Adjustment for known prognostic covariates can lead to substantial increases in power, and should be routinely incorporated into the analysis of randomized trials. The potential benefits of adjusting for a small number of possibly prognostic covariates in trials with moderate or large sample sizes far outweigh the risks of doing so, and so should also be considered.