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Recent Development in the Joint Modeling of Longitudinal Quality of Life Measurements and Survival Data from Cancer Clinical Trials

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Advanced Statistical Methods in Data Science

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

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Abstract

In cancer clinical trials, longitudinal Quality of Life (QoL) measurements and survival time on a patient may be analyzed by joint models, which provide more efficient estimation than modeling QoL data and survival time separately, especially when there is strong association between the longitudinal measurements and the survival time. Most joint models in the literature assumed classical linear mixed model for longitudinal measurements and Cox’s proportional hazards model for survival times. The linear mixed model with normal distributed random components may not be sufficient to model bounded QoL measurements. Moreover, when some patients are immune to recurrence of relapse and can be viewed as cured, the proportional hazards model is not suitable for survival times. In this paper, we review some recent developments in joint models to deal with bounded longitudinal QoL measurements and survival times with a possible cure fraction. One of such joint models assumes a linear mixed tt model for longitudinal measurements and a promotion time cure model for survival data, and the two parts are linked through a latent variable. Another joint model employs a simplex distribution to model the bounded QoL measurements and a classical proportional hazard to model survival times, and the two parts share a random effect. Semiparametric estimation methods have been proposed to estimate the parameters in the models. The models are illustrated with QoL measurements and recurrence times from a clinical trial on women with early breast cancer.

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Acknowledgements

The authors wish to thank the editors and associate editors for their helpful comments and suggestions. Hui Song was supported by National Natural Sciences Foundation of China (Grant No.11601060), Dalian High Level Talent Innovation Programme (No.2015R051) and Fundamental Research Funds for the Central Universities of China.

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Authors and Affiliations

  1. School of Mathematical Sciences, Dalian University of Technology, 116024, Dalian,Liaoning, China

    Hui Song

  2. Departments of Public Health Sciences and Mathematics and Statistics, Queens University, K7L 3N6, Kingston, ON, Canada

    Yingwei Peng & Dongsheng Tu

Authors
  1. Hui Song
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  2. Yingwei Peng
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  3. Dongsheng Tu
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Corresponding author

Correspondence to Hui Song .

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Editors and Affiliations

  1. University of North Carolina , Chapel Hill, NC, North Carolina, USA

    Ding-Geng Chen

  2. Department of Statistics, University of British Columbia Department of Statistics, Vancouver, Canada

    Jiahua Chen

  3. Department of Mathematics and Statistics, University of Calgary Department of Mathematics and Statistics, Calgary, AB, Canada

    Xuewen Lu

  4. Dept of Statistics and Actuarial Science, University of Waterloo Dept of Statistics and Actuarial Science, Waterloo, Canada

    Grace Y. Yi

  5. Dept of Statistics and Actuarial Science, Western University Dept of Statistics and Actuarial Science, London, Ontario, Canada

    Hao Yu

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Song, H., Peng, Y., Tu, D. (2016). Recent Development in the Joint Modeling of Longitudinal Quality of Life Measurements and Survival Data from Cancer Clinical Trials. In: Chen, DG., Chen, J., Lu, X., Yi, G., Yu, H. (eds) Advanced Statistical Methods in Data Science. ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-2594-5_8

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