Accounting for Baseline Observations in Randomized Clinical Trials

In clinical trials investigating the treatment of chronic, progressive diseases, a common design is to make some physiologic measurement of the disease state at the start of the trial, randomize patients to either the experimental treatment or some control treatment (placebo), follow the patients over some period of time, and then measure once again the physiologic measurement of the disease state. The question then arises: How should the baseline measurements made prior to randomization be used? In this manuscript, I consider several common strategies that might be used in this setting, including analyzing only the follow-up measurement, analyzing the change in measurements, and the analysis of covariance (ANCOVA) approach of adjusting for the baseline measurement in a linear regression model. I illustrate several different ways to motivate the use of ANCOVA, as well as examining the settings in which not measuring baseline might be preferred. I conclude with a discussion of how these results might change when using a randomized crossover design.