Pretest-posttest studies are an important and popular method for assessing treatment effects or the effectiveness of an intervention in many areas of scientific research. There are two distinct features for this type of study: availability of baseline information for all subjects in the study and missingness by design of measures of the responses. In this talk, we first provide a brief overview of existing research on the topic. We then present alternative empirical likelihood approaches to inferences on the treatment effects with efficient use of all available information. Theoretical results are developed, and finite sample performances of the proposed methods with comparison to existing ones are investigated through simulation studies.
This is joint work with Min Chen and Mary Thompson.