NONPARAMETRIC MIXED-EFFECTS MODELS FOR REPEATED BINARY DATA ARISING IN SERIAL DILUTION ASSAYS - AN APPLICATION TO ESTIMATING VIRAL BURDEN IN AIDS

This article develops methods for estimating treatment effects in mixe d-effects models using outcome data gathered from serial dilution assa ys. Our application allows us to estimate the viral burden of HIV infe ction before and after antiviral treatment from cell dilution assays. This assay is designed to determine the infectious units per patient p eripheral blood mononuclear cell (PBMC). The infectious unit is the am ount of virus required to produce detectable HIV infection in PBMC's f rom healthy, uninfected donors. At each dilution level of the patient cells, one observes whether or not it was possible for the virus from these cells to infect donor cells. Thus the assay result for each subj ect consists of a series of repeated binary outcomes. We propose an an alytic approach in which patient-specific titers (measures of viral bu rden) are modeled as random effects from an unknown distribution, and treatment effects are modeled as fixed. This approach makes use of all assay results, even if many assays fail to reach endpoint (i.e., they turn negative at the highest dilution level) and the assay design (di lution scheme) changes over time.