Joint modeling of progression of HIV resistance mutations measured with uncertainty and failure time data

Development of HIV resistance mutations is a major cause for failure of antiretroviral treatment. This article proposes a method for jointly modeling the processes of viral genetic changes and treatment failure. Because the viral genome is measured with uncertainty, a hidden Markov model is used to fit the viral genetic process. The uncertain viral genotype is included as a time-dependent covariate in a Cox model for failure time, and an expectation-maximization algorithm is used to estimate the model parameters. This model allows simultaneous evaluation of the sequencing uncertainty and the effect of resistance mutation on the risk of virological and immunological failures. Various model checking tests are provided to assess the appropriateness of the model. Simulation studies are performed to investigate the finite-sample properties of the proposed methods, which are then applied to data collected in three phase II clinical trials testing antiretroviral treatments containing the drug efavirenz.