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Transformation models with informative partly interval-censored data

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Abstract

Partly interval censoring is frequently encountered in clinical trials when the failure time of an event is observed exactly for some subjects but is only known to fall within an observed interval for others. Although this kind of censoring has drawn recent attention in survival analysis, available methods typically assume that the observed interval is independent of the failure time and that all potential predictors can be fully observable. However, the above assumptions may not be valid in practice. This paper considers a new joint modeling approach to simultaneously model the failure and observation times and correlate these two stochastic processes through shared latent factors. The proposed model comprises a transformation model for the failure time of interest, a proportional hazards model for the length of censoring interval, and a factor analysis model for characterization of the latent factors. A multi-stage data augmentation procedure is introduced to tackle the challenges posed by the complex model and data structure. A Bayesian approach coupled with monotone spline approximation and Markov chain Monte Carlo techniques is developed to estimate the unknown parameters and nonparametric functions. The satisfactory performance of the proposed method is demonstrated through simulations, and it is then applied to a Framingham Heart study.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grant Nos. 12301332,12226416 and 12271060) and Research Grants Council, University Grants Committee (Grant Nos. 14302220 and 14303622).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Changchun University of Technology, Changchun, China

    Jingjing Jiang & Chunjie Wang

  2. School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, China

    Deng Pan

  3. Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China

    Xinyuan Song

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  1. Jingjing Jiang
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  2. Chunjie Wang
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  3. Deng Pan
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  4. Xinyuan Song
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Contributions

Jingjing Jiang and Deng Pan conducted numerical studies, Chunjie Wang and Xinyuan Song formulated the model and method, and Jiangjing Jiang and Xinyuan Song wrote the main manuscript text. All authors reviewed the manuscript.

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Correspondence to Chunjie Wang or Xinyuan Song.

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Jiang, J., Wang, C., Pan, D. et al. Transformation models with informative partly interval-censored data. Stat Comput 34, 8 (2024). https://doi.org/10.1007/s11222-023-10306-3

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