|Title||Proportional rate models for recurrent time event data under dependent censoring: A comparative study.|
|Publication Type||Book Chapter|
|Year of Publication||2011|
|Authors||Amorim, Leila D. A. F., Jianwen Cai, and Donglin Zeng|
|Book Title||Recent Advances in Biostatistics|
In many biomedical studies, each patient can experience events that can occur repeatedly over time. Modeling techniques have been developed to analyze recurrent time-to-event data assuming independent censoring, i.e., the censoring process is unrelated to the event failure process conditional on the covariates. However, this assumption may not hold in general. This would happen if, for instance, the subjects who are at higher risks of recurrent events tend to withdraw from the stud earlier. Another form of dependent censoring could occur due to terminal events, such as death. Two methods were recently proposed to account for dependent censoring with the marginal rate models for analysis of recurrent event data. This paper overviews these approaches and critically compare them through extensive simulation studies. The simulation results showed that the approaches are effective for handling dependent censoring when the source of informative censoring is correctly specified. Further research is still needed for more complex situations.
|Original Publication||Proportional rate models for recurrent time event data under dependent censoring: A comparative study.|
Proportional rate models for recurrent time event data under dependent censoring: A comparative study.