Analysis of multivariate failure-time data from HIV clinical trials

We illustrate the use of marginal methods for the analysis of multivariate failure-time data using a large trial in HIV infection in which the composi te endpoint of AIDS or death incorporates more than 20 events with varying severity. Multivariate failure-time methods are required to investigate whe ther treatment delays development of new AIDS events. AIDS events can be gr ouped and treatment effects estimated using only the first event to occur i n each group for each individual. Alternatively, all events can be included by fitting a separate baseline hazard for development of each event, and r estricting treatment effects to be common within groups of events. In eithe r case, model-based or minimum-variance estimates of the overall effect of treatment can be constructed. The covariance matrix for the treatment-effec t estimates can be used in multiple testing procedures. Results from the De lta trial suggest that combination antiretroviral therapy with AZT plus eit her ddI or ddC may delay progression to more severe AIDS events compared to AZT monotherapy. These late events are generally untreatable and prophylax is is not available. Trials are not generally powered to detect treatment e ffects on individual events making up a composite endpoint, and therefore a ll analyses are exploratory rather than providing definitive evidence. Howe ver, marginal multivariate models provide an easily available approach for modeling the effect of covariates on multiple disease processes, and allow the likely effects of treatment to be presented in a manner which is easily understood. They can be used in a variety of ways to explore different pat terns of treatment effects and are also useful for testing multiple hypothe ses regarding treatment effects on several different composite endpoints. C ontrol Clin Trials 2000;21:75-93 (C) Elsevier Science Inc. 2000.