An additive-multiplicative rates model for recurrent event data with informative terminal event

An additive-multiplicative model for longitudinal data with informative observation times

Designed clinical studies often assess outcomes at pre-planned time points. In most situations, standard statistical models, such as generalized linear mixed models and generalized additive models, are sufficient to depict the temporal trends of the outcome and produce valid inference. Complicating factors, however, do exist in practical data analyses. One complication arises when the outcome and observational processes are interdependent, that is, the observational process is informative; another challenge is patient characteristics may influence the longitudinally observed outcomes in non-additive ways, for example, by multiplicative factors. In this research, we extend the standard longitudinal models to accommodate informative observation through a more flexible modeling structure-one with additive-multiplicative components that do not require explicit specification of the dependency structure between the outcome and observation processes. Along this vein, we provide the essential theory for inference in such models. Simulation studies showed the proposed method performs well for finite-sample scenarios, and the method was applied to analyze a motivating example from an alcohol-associated hepatitis observational study.

Joint scale-change models for recurrent events and failure time

Recurrent event data arise frequently in various fields such as biomedical sciences, public health, engineering, and social sciences. In many instances, the observation of the recurrent event process can be stopped by the occurrence of a correlated failure event, such as treatment failure and death. In this article, we propose a joint scale-change model for the recurrent event process and the failure time, where a shared frailty variable is used to model the association between the two types of outcomes. In contrast to the popular Cox-type joint modeling approaches, the regression parameters in the proposed joint scale-change model have marginal interpretations. The proposed approach is robust in the sense that no parametric assumption is imposed on the distribution of the unobserved frailty and that we do not need the strong Poisson-type assumption for the recurrent event process. We establish consistency and asymptotic normality of the proposed semiparametric estimators under suitable regularity conditions. To estimate the corresponding variances of the estimators, we develop a computationally efficient resampling-based procedure. Simulation studies and an analysis of hospitalization data from the Danish Psychiatric Central Register illustrate the performance of the proposed method.