Covariate-Based Assignment to Treatment Groups: Some Simulation Results
Six estimators of treatment effect when assignment to treatment groups is based on the covariate are compared in terms of their empirical standard errors and percent relative bias. Results show that simple analysis of covariance estimator is not appropriate when, as a result of assignment, difference between covariate group means is relatively large and/or the distribution of covariate in treatment groups is skewed. Instead, estimators derived from model fitting assuming nonparallelism and blocking on the covariate are preferred.