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Journal of Clinical Epidemiology
Volume 63, Issue 12
, Pages 1392-1393
, December 2010
Covariate adjustment in RCTs results in increased power to detect conditional effects compared with the power to detect unadjusted or marginal effects
References
- . Covariate adjustment increases statistical power in randomized controlled trials. J Clin Epidemiol. 2010;63:1387
- . A substantial and confusing variation exists in handling of baseline covariates in randomized controlled trials: a review of trials published in leading medical journals. J Clin Epidemiol. 2010;63:142–153
- . Clinical prediction models: a practical approach to development, validation, and updating. New York, NY: Springer; 2009;
- . Interpretation and choice of effect measures in epidemiologic analyses. Am J Epidemiol. 1987;125:761–768
- . Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates. Biometrika. 1984;7:431–444
- . Should we adjust for covariates in nonlinear regression analyses of randomized trials?. Control Clin Trials. 1998;19:249–256
- . Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study (p n/a) by Peter C. Austin, Paul Grootendorst, Sharon-Lise T. Normand, Geoffrey M. Anderson, Statistics in Medicine, Published Online: 16 June 2006. DOI: 10.1002/sim. 2618. Stat Med. 2007;26:3208–3210
PII: S0895-4356(10)00189-7
doi: 10.1016/j.jclinepi.2010.05.004
© 2010 Elsevier Inc. All rights reserved.
« Previous
Journal of Clinical Epidemiology
Volume 63, Issue 12
, Pages 1392-1393
, December 2010
