Optimal experimental designs for accelerated failure time with Type I and random censoring

摘要

Proportional Hazards models have been widely used to analyze survival data. In many cases survival data do not verify the assumption of proportional hazards. An alternative to the PH models with more relaxed conditions are Accelerated Failure Time models. These models are fairly commonly used in the field of manufacturing, but they are more and more frequent for modeling clinical trial data. They focus on the direct effect of the explanatory variables on the survival function allowing an easier interpretation of the effect of the corresponding covariates on the survival time. Optimal experimental designs are computed in this framework for Type I and random arrival. The results are applied to clinical models used to prevent tuberculosis in Ugandan adults infected with HIV.