Covariate adjustment in RCTs results in increased power to detect conditional effects compared with the power to detect unadjusted or marginal effects
Authors:
- Peter C. AustinPAPeter C. Austin
- Andrea MancaAMAndrea Manca
- Merrick ZwarensteinMZMerrick Zwarenstein
- David N. JuurlinkDJDavid N. Juurlink
- Matthew B. StanbrookMSMatthew B. Stanbrook
Open Access
- 30 August 2010
- journal article
- other
- Published by Elsevier in Journal of Clinical Epidemiology
- Vol. 63 (12) , 1392-1393
Abstract
No abstract availableKeywords
Funding Information
- Institute for Clinical Evaluative Sciences (ICES)
- Ontario Ministry of Health and Long-Term Care (MOHLTC)
- Heart and Stroke Foundation of Ontario
This publication has 8 references indexed in Scilit:
- Covariate adjustment increases statistical power in randomized controlled trialsJournal of Clinical Epidemiology, 2010
- A substantial and confusing variation exists in handling of baseline covariates in randomized controlled trials: a review of trials published in leading medical journalsJournal of Clinical Epidemiology, 2010
- Clinical Prediction Models2009
- 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.2618Statistics in Medicine, 2007
- Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo studyStatistics in Medicine, 2006
- Should We Adjust for Covariates in Nonlinear Regression Analyses of Randomized Trials?Controlled Clinical Trials, 1998
- INTERPRETATION AND CHOICE OF EFFECT MEASURES IN EPIDEMIOLOGIC ANALYSES1American Journal of Epidemiology, 1987
- Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariatesBiometrika, 1984