Clinical trials are the standard for identifying new drugs for the treatment of disease, but results are dependent on patient compliance. The success of treatments for HIV disease in particular may be judged in part by their effect on immunologic, virologic, or clinical measures collected on patients at regular predefined intervals. If patients drop out of a trial before study completion, the analysis of the repeatedly collected parameters needs to be undertaken and interpreted with care. The authors recommend using graphic techniques to assess the impact of the missing data on the profiles of the parameters over time. To assess treatment differences, a variety of simple tests are proposed that allow different assumptions to be made regarding the reasons for the incomplete data. A case study is presented providing an analysis of CD4 data from the Pediatric Aids Clinical Trials Group (PACTG) Protocol 051, in which only 52% of the patients completed the study while remaining on treatment; younger patients with lower CD4 counts were more likely to stop treatment earlier. This type of systematic missing data can lead to incorrect conclusions regarding different treatment effects on CD4 counts. With the data of PACTG 051, however, regardless of the methodology used, no treatment differences were found. Inconsistent conclusions would have indicated the need for more sophisticated statistical techniques to adequately test for treatment differences.