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Yingqing Chen, PhD Candidate in Biostatistics

The proportional hazards model for survival time data assumes that the risk factors of interest predict their effect multiplicatively on an underlying unknown hazard function. Although this model has been studied widely in the statistical literature, it may not be applicable when the assumption of constant proportionality is violated. In a two-arm randomized clinical trial, for example, participants in the treatment group would have the same risk process through time as those in the control group, except that the treatment would speed up or slow down this process. Some alternatives such as the accelerated failure time model have been developed in the literature. In this talk, an accelerated hazards model is introduced to estimate such a treatment effect when there is a scale change relationship between hazard functions. The methodology and its estimation procedure are studied within a two-sample setting. Extensions of the model to other general settings are also discussed. The proposed method is applied to a real data set to investigate the practical usage.