Empirical comparison of four baseline covariate adjustment methods in analysis of continuous outcomes in randomized controlled trials
Clinical Epidemiology2014Vol. 6, pp. 227–227
Citations Over TimeTop 10% of 2014 papers
Shiyuan Zhang, James Paul, Manyat Nantha-Aree, Norman Buckley, Uswa Shahzad, Ji Cheng, Justin DeBeer, Mitchell Winemaker, David Wismer, Dinshaw Punthakee, Victoria Avram, Lehana Thabane
Abstract
ANCOVA, through both simulation and empirical studies, provides the best statistical estimation for analyzing continuous outcomes requiring covariate adjustment. Our empirical findings support the use of ANCOVA as an optimal method in both design and analysis of trials with a continuous primary outcome.
Related Papers
- → Common Misconceptions Concerning The Analysis Of Covariance(1977)19 cited
- A strategy to evaluate a covariate by group interaction in an analysis of covariance.(1998)
- → Incorporation of Covariates Through Principal Components in Analysis of Covariance: A Simulation Study(2023)1 cited
- → Multivariate Analysis of Covariance(2013)1 cited
- → ATI Analysis (ANCOVA with Interaction)(2017)