Variable selection for misclassified current status data under the proportional hazards model

Abstract

Misclassified current status data arise when the failure time of interest is observed or known only to be either smaller or larger than an observation time rather than observed exactly, and the failure status is examined by a diagnostic test with testing error. Such data commonly occur in various scientific fields, including clinical trials, demographic studies and epidemiological surveys. This paper discusses regression analysis of such data with the focus on variable selection or identifying predictable and important covariates associated with the failure time of interest. For the problem, a penalized maximum likelihood approach is proposed under the Cox proportional hazards model and the smoothly clipped absolute deviation penalty. More specifically, we develop a penalized EM algorithm to relieve the computational burden in maximizing the resulting, complex penalized likelihood function. A simulation study is conducted to examine the empirical performance of the proposed approach in finite samples, and an illustration to a set of real data on chlamydia is also provided.

Keywords:

• Current status data
• EM algorithm
• Misclassification
• Penalized likelihood
• Proportional hazards model
• Variable selection

Acknowledgments

We wish to thank the reviewers for the very helpful comments and suggestions. This work was partly supported by the National Nature Science Foundation of China (11901128), Nature Science Foundation of Guangdong Province of China (Grant No. 2021A1515010044), and Science and Technology Program of Guangzhou of China (202102010512).