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Regression analysis of longitudinal data with outcome‐dependent sampling and informative censoring

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  • Weining Shen
  • Suyu Liu
  • Yong Chen
  • Jing Ning

Abstract

We consider a regression analysis of longitudinal data in the presence of outcome‐dependent observation times and informative censoring. Existing approaches commonly require a correct specification of the joint distribution of longitudinal measurements, the observation time process, and informative censoring time under the joint modeling framework and can be computationally cumbersome due to the complex form of the likelihood function. In view of these issues, we propose a semiparametric joint regression model and construct a composite likelihood function based on a conditional order statistics argument. As a major feature of our proposed methods, the aforementioned joint distribution is not required to be specified, and the random effect in the proposed joint model is treated as a nuisance parameter. Consequently, the derived composite likelihood bypasses the need to integrate over the random effect and offers the advantage of easy computation. We show that the resulting estimators are consistent and asymptotically normal. We use simulation studies to evaluate the finite‐sample performance of the proposed method and apply it to a study of weight loss data that motivated our investigation.

Suggested Citation

  • Weining Shen & Suyu Liu & Yong Chen & Jing Ning, 2019. "Regression analysis of longitudinal data with outcome‐dependent sampling and informative censoring," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 46(3), pages 831-847, September.
  • Handle: RePEc:bla:scjsta:v:46:y:2019:i:3:p:831-847
    DOI: 10.1111/sjos.12373
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    Cited by:

    1. Ruiwen Zhou & Jianguo Sun, 2022. "Estimation of the Proportional Mean Residual Life Model with Internal and Longitudinal Covariates," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 550-563, December.
    2. Fengting Yi & Niansheng Tang & Jianguo Sun, 2022. "Simultaneous variable selection and estimation for joint models of longitudinal and failure time data with interval censoring," Biometrics, The International Biometric Society, vol. 78(1), pages 151-164, March.

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