Semiparametric analysis of longitudinal data with informative observation times and censoring times

ABSTRACT

We focus on regression analysis of irregularly observed longitudinal data which often occur in medical follow-up studies and observational investigations. The model for such data involves two processes: a longitudinal response process of interest and an observation process controlling observation times. Restrictive models and questionable assumptions, such as Poisson assumption and independent censoring time assumption, were posed in previous works for analysing longitudinal data. In this paper, we propose a more general model together with a robust estimation approach for longitudinal data with informative observation times and censoring times, and the asymptotic normalities of the proposed estimators are established. Both simulation studies and real data application indicate that the proposed method is promising.

KEYWORDS:

• Semiparametric regression
• longitudinal data
• informative observation times
• informative censoring times

Acknowledgments

The authors would like to thank the Editor-in-Chief, Prof. Jie Chen, the Associate Editor, and the three reviewers for their constructive and insightful comments and suggestions that greatly improved the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.