In a typical randomized clinical trial, the response of interest is measured pre-treatment (i.e., at baseline, t = 0), and at one or more post-treatment time points (t = 1, …, T). At the end of the trial, it is common practice to report: (i) a point estimate and 95% confidence interval for the mean change from baseline at time T for each treatment group, and (ii) a p-value and 95% confidence interval for the between-group difference in the mean change from baseline. The manner in which the baseline responses are used in the analysis influences both the accuracy and the efficiency of items (i) and (ii). We will contrast the popular ANCOVA approach in which the baseline response is used as a covariate with a longitudinal data analysis (LDA) model proposed by Liang and Zeger (2000), highlighting advantages of the LDA approach over ANCOVA.