Regression analysis of incomplete data from event history studies with the proportional rates model

Stat Interface. 2018;11(1):91-97. doi: 10.4310/SII.2018.v11.n1.a8.


This paper discusses regression analysis of a type of incomplete mixed data arising from event history studies with the proportional rates model. By mixed data, we mean that each study subject may be observed continuously during the whole study period, continuously over some study periods and at some time points, or only at some discrete time points. Therefore, we have combined recurrent event and panel count data. For the problem, we present a multiple imputation-based estimation procedure and one advantage of the proposed marginal model approach is that it can be easily implemented. To assess the performance of the procedure, a simulation study is conducted and indicates that it performs well for practical situations and can be more efficient than the existing method. The methodology is applied to a set of mixed data from a longitudinal cohort study.

Keywords: Incomplete data; Marginal model; Multiple imputation; Proportional rates model.