In randomized clinical trials, a pre-treatment measurement is often taken at baseline, and post-treatment effects are measured at several time points post-baseline, say t=1, ..., T. At the end of the trial, it is of interest to assess the treatment effect based on the mean change from baseline at the last time point T. We consider statistical methods for (i) a point estimate and 95 per cent confidence interval for the mean change from baseline at time T for each treatment group, and (ii) a p-value and 95 per cent 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). In this paper, we will consider the ANCOVA approach with change from baseline as a dependent variable and compare that with a constrained longitudinal data analysis (cLDA) model proposed by Liang and Zeger (Sankhya: Indian J. Stat. (Ser B) 2000; 62:134-148), in which the baseline is modeled as a dependent variable in conjunction with the constraint of a common baseline mean across the treatment groups. Some drawbacks of the ANCOVA model and potential advantages of the cLDA approach are discussed and illustrated using numerical simulations.