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.
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.
Authors: David van Klaveren; Yvonne Vergouwe; Vasim Farooq; Patrick W Serruys; Ewout W Steyerberg Journal: J Clin Epidemiol Date: 2015-02-27 Impact factor: 6.437
Authors: Kali S Thomas; Jessica A Ogarek; Joan M Teno; Pedro L Gozalo; Vincent Mor Journal: J Gerontol A Biol Sci Med Sci Date: 2019-01-16 Impact factor: 6.053
Authors: Olivier Joannes-Boyau; Patrick M Honoré; Paul Perez; Sean M Bagshaw; Hubert Grand; Jean-Luc Canivet; Antoine Dewitte; Claire Flamens; Wilfried Pujol; Anne-Sophie Grandoulier; Catherine Fleureau; Rita Jacobs; Christophe Broux; Hervé Floch; Olivier Branchard; Stephane Franck; Hadrien Rozé; Vincent Collin; Willem Boer; Joachim Calderon; Bernard Gauche; Herbert D Spapen; Gérard Janvier; Alexandre Ouattara Journal: Intensive Care Med Date: 2013-06-06 Impact factor: 17.440
Authors: Ewout W Steyerberg; Andrew J Vickers; Nancy R Cook; Thomas Gerds; Mithat Gonen; Nancy Obuchowski; Michael J Pencina; Michael W Kattan Journal: Epidemiology Date: 2010-01 Impact factor: 4.822
Authors: Patrick S Parfrey; Geoffrey A Block; Ricardo Correa-Rotter; Tilman B Drüeke; Jürgen Floege; Charles A Herzog; Gerard M London; Kenneth W Mahaffey; Sharon M Moe; David C Wheeler; Glenn M Chertow Journal: Clin J Am Soc Nephrol Date: 2015-11-27 Impact factor: 8.237