Combining cross-sectional and prospective data methods to improve transition parameter estimation for characterizing the accumulation of HIV-1 drug resistance mutations

The order and rate of acquisition of HIV drug resistance mutations have been estimated from longitudinal and cross-sectional data using Markov models and branching trees, respectively. This article proposes methods that make use of both longitudinal and cross-sectional data simultaneously by employing link functions between the two parameter sets. Most functions that link the two parameter sets also depend on the time on treatment before the start of the study-information that may not be available. Nonetheless, under certain assumptions, some link functions eliminate the dependence on time. Using such functions, the two sources of information can be combined to improve the precision of parameter estimation. The method also accommodates error in the link functions from uncertainty in the assumptions required for the links or other reasons. These methods are applied to data from AIDS Clinical Trial Group protocol 398, a randomized comparison of mono- versus dual-protease inhibitor use in heavily treatment experienced HIV patients. Combining the two sources of information allows detection of differences between rates of transition that are not detectable using prospective data alone.