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In two harmonized efficacy studies to prevent HIV infection through multipleinfusions of the monoclonal antibody VRC01, a key objective is to evaluatewhether the serum concentration of VRC01, which changes cyclically over timealong with the infusion schedule, is associated with the rate of HIV infection.Simulation studies are needed in the development of such survival models. Inthis paper, we consider simulating event time data with a continuoustime-varying covariate whose values vary with time through multiple drugadministration cycles, and whose effect on survival changes differently beforeand after a threshold within each cycle. The latter accommodates settings witha zero-protection biomarker threshold above which the drug provides a varyinglevel of protection depending on the biomarker level, but below which the drugprovides no protection. We propose two simulation approaches: one based onsimulating survival data under a single-dose regimen first before data areaggregated over multiple doses, and another based on simulating survival datadirectly under a multiple-dose regimen. We generate time-to-event datafollowing a Cox proportional hazards model based on inverting the cumulativehazard function and a log link function for relating the hazard function to thecovariates. The method's validity is assessed in two sets of simulationexperiments. The results indicate that the proposed procedures perform well inproducing data that conform to their cyclic nature and assumptions of the Coxproportional hazards model.

Generating survival times using Cox proportional hazards models with  cyclic time-varying covariates, with application to a multiple-dose  monoclonal antibody clinical trial