Evaluating Utility Measurement From Recurrent Marker Processes in the Presence of Competing Terminal Events

ABSTRACT

In follow-up studies, utility marker measurements are usually collected upon the occurrence of recurrent events until a terminal event such as death takes place. In this article, we define the recurrent marker process to characterize utility accumulation over time. For example, with medical cost and repeated hospitalizations being treated as marker and recurrent events, respectively, the recurrent marker process is the trajectory of cumulative cost, which stops to increase after death. In many applications, competing risks arise as subjects are at risk of more than one mutually exclusive terminal event, such as death from different causes, and modeling the recurrent marker process for each failure type is often of interest. However, censoring creates challenges in the methodological development, because for censored subjects, both failure type and recurrent marker process after censoring are unobserved. To circumvent this problem, we propose a nonparametric framework for the recurrent marker process with competing terminal events. In the presence of competing risks, we start with an estimator by using marker information from uncensored subjects. As a result, the estimator can be inefficient under heavy censoring. To improve efficiency, we propose a second estimator by combining the first estimator with auxiliary information from the estimate under noncompeting risks model. The large sample properties and optimality of the second estimator are established. Simulation studies and an application to the SEER-Medicare linked data are presented to illustrate the proposed methods. Supplementary materials for this article are available online.

KEYWORDS:

• Competing risks
• Cumulative incidence function
• Marked recurrent events
• Nonparametric estimation

Supplementary Materials

 

	Proofs and Simulation Results: The file contains proof of Theorem 3.1, Theorem 3.2, Corollary 3.3, Theorem 4.1, and Theorem 5.1. The file also contains the additional simulation results not presented in the article. (websupp.pdf)
	R code: R code to perform the methods described in the article. (recmp.R)

Acknowledgements

The content of this article is based on the first author's Ph.D. dissertation conducted at Johns Hopkins University under the supervision of the second author. The authors acknowledge the efforts of the National Cancer Institute, the Office of Research, Development and Information, CMS, Information Management Services (IMS), Inc.; and the Surveillance, Epidemiology, and End Results (SEER) Program tumor registries in the creation of the SEER-Medicare database. The interpretation and reporting of these data are the sole responsibility of the authors.

Funding

This research was supported in part by the National Institute of Health grants R01HL122212 and R01CA193888.