Meta-analysis of individual patient data with semi-competing risks under the Weibull joint frailty–copula model

Abstract

In meta-analysis of individual patient data with semi-competing risks, the joint frailty–copula model has been proposed, where frailty terms account for the between-study heterogeneity and copulas account for dependence between terminal and nonterminal event times. In the previous works, the baseline hazard functions in the joint frailty–copula model are estimated by the nonparametric model or the penalized spline model, which requires complex maximization schemes and resampling-based interval estimation. In this article, we propose the Weibull distribution for the baseline hazard functions under the joint frailty–copula model. We show that the Weibull model constitutes a conjugate model for the gamma frailty, leading to explicit expressions for the moments, survival functions, hazard functions, quantiles, and mean residual lifetimes. These results facilitate the parameter interpretation of prognostic inference. We propose a maximum likelihood estimation method and make our computer programs available in the R package, joint.Cox. We also show that the delta method is feasible to calculate interval estimates, which is a useful alternative to the resampling-based method. We conduct simulation studies to examine the accuracy of the proposed methods. Finally, we use the data on ovarian cancer patients to illustrate the proposed method.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3

References

  1. Belaghi RA, Asl MN (2019) Estimation based on progressively type-I hybrid censored data from the Burr XII distribution. Stat Pap 60(3):411–453

    MathSciNet  MATH  Google Scholar 

  2. Burr IW (1942) Cumulative frequency functions. Ann Math Stat 13(2):215–232

    MathSciNet  Article  Google Scholar 

  3. Burzykowski T, Molenberghs G, Buyse M, Geys H, Renard D (2001) Validation of surrogate end points in multiple randomized clinical trials with failure time end points. Appl Stat 50(4):405–422

    MathSciNet  MATH  Google Scholar 

  4. Chen YH (2012) Maximum likelihood analysis of semicompeting risks data with semiparametric regression models. Lifetime Data Anal 18:36–57

    MathSciNet  Article  Google Scholar 

  5. Duchateau L, Janssen P (2007) The frailty model. Springer, New York

    Google Scholar 

  6. Duchateau L, Janssen P, Lindsey P, Legrand C, Nguti R, Sylvester R (2002) The shared frailty model and the power for heterogeneity tests in multicenter trials. Comput Stat Data Anal 40(3):603–620

    MathSciNet  Article  Google Scholar 

  7. EL-Sagheer RM (2018) Estimation of parameters of Weibull–Gamma distribution based on progressively censored data. Stat Pap 59(2):725–757

    MathSciNet  Article  Google Scholar 

  8. Emura T (2019) joint.Cox: the joint frailty–copula models between tumour progression and death for meta-analysis, CRAN

  9. Emura T, Nakatochi M, Murotani K, Rondeau V (2017) A joint frailty–copula model between tumour progression and death for meta-analysis. Stat Methods Med Res 26(6):2649–2666

    MathSciNet  Article  Google Scholar 

  10. Emura T, Nakatochi M, Matsui S, Michimae H, Rondeau V (2018) Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: meta-analysis with a joint model. Stat Methods Med Res 27(9):2842–2858

    MathSciNet  Article  Google Scholar 

  11. Emura T, Matsui S, Rondeau V (2019) Survival analysis with correlated endpoints, joint frailty–copula models. JSS research series in statistics. Springer, Singapore

    Google Scholar 

  12. Fine JP, Jiang H, Chappell R (2001) On semi-competing risks data. Biometrika 88(4):907–919

    MathSciNet  Article  Google Scholar 

  13. Ganzfried BF, Riester M, Haibe-Kains B, Risch T, Tyekucheva S, Jazic I, ... & Huttenhower C (2013) curatedOvarianData: clinically annotated data for the ovarian cancer transcriptome. Database. https://doi.org/10.1093/database/bat013

  14. Lee KH, Haneuse S, Schrag D, Dominici F (2015) Bayesian semiparametric analysis of semicompeting risks data: investigating hospital readmission after a pancreatic cancer diagnosis. J R Stat Soc Ser C (Appl Stat) 64(2):253–273

    MathSciNet  Article  Google Scholar 

  15. Lee KH, Dominici F, Schrag D, Haneuse S (2016) Hierarchical models for semicompeting risks data with application to quality of end-of-life care for pancreatic cancer. J Am Stat Assoc 111(515):1075–1095

    MathSciNet  Article  Google Scholar 

  16. Li Z, Chinchilli VM, Wang M (2019) A Bayesian joint model of recurrent events and a terminal event. Biom J 60(1):187–202

    MathSciNet  Article  Google Scholar 

  17. Liu X (2012) Planning of accelerated life tests with dependent failure modes based on a gamma frailty model. Technometrics 54(4):398–409

    MathSciNet  Article  Google Scholar 

  18. MacDonald IL (2014) Does Newton–Raphson really fail? Stat Methods Med Res 23(3):308–311

    MathSciNet  Article  Google Scholar 

  19. Molenberghs G, Verbeke G, Efendi A, Braekers R, Demétrio CG (2015) A combined gamma frailty and normal random-effects model for repeated, overdispersed time-to-event data. Stat Methods Med Res 24(4):434–452

    MathSciNet  Article  Google Scholar 

  20. Nelsen RB (2006) An introduction to copulas, 2nd edn. Springer, New York

    Google Scholar 

  21. Peng M, Xiang L (2019) Joint regression analysis for survival data in the presence of two sets of semi-competing risks. Biom J. https://doi.org/10.1002/bimj.201800137

    MathSciNet  Article  MATH  Google Scholar 

  22. Peng M, Xiang L, Wang S (2018) Semiparametric regression analysis of clustered survival data with semi-competing risks. Comput Stat Data Anal 124:53–70

    MathSciNet  Article  Google Scholar 

  23. Rondeau V, Pignon JP, Michiels S, collaborative Group (2015) A joint model for the dependence between clustered times to tumour progression and deaths: a meta-analysis of chemotherapy in head and neck cancer. Stat Methods Med Res 24(6):711–729

    MathSciNet  Article  Google Scholar 

  24. Rotolo F, Legrand C, Van Keilegom I (2013) A simulation procedure based on copulas to generate clustered multi-state survival data. Comput Methods Programs Biomed 109(3):305–312

    Article  Google Scholar 

  25. Rotolo F, Paoletti X, Michiels S (2018) surrosurv: An R package for the evaluation of failure time surrogate endpoints in individual patient data meta-analyses of randomized clinical trials. Comput Methods Programs Biomed 155:189–198

    Article  Google Scholar 

  26. Schneider S, Demarqui FN, Colosimo EA, Mayrink VD (2019) An approach to model clustered survival data with dependent censoring. Biom J. https://doi.org/10.1002/bimj.201800391

    Article  MATH  Google Scholar 

  27. Touraine C, Helmer C, Joly P (2016) Predictions in an illness-death model. Stat Methods Med Res 25(4):1452–1470

    MathSciNet  Article  Google Scholar 

  28. Vu HTV, Segal MR, Knuiman MW, James IR (2001) Asymptotic and small sample statistical properties of random frailty variance estimates for shared gamma frailty models. Commun Stat Simul 30:581–595

    MathSciNet  Article  Google Scholar 

  29. Weibull W (1951) Wide applicability. J Appl Mech 103(730):293–297

    MATH  Google Scholar 

Download references

Acknowledgements

The authors kindly thank the associate editor and two anonymous referees for their valuable suggestions that improved the paper. We are grateful to Jia-Han Shih for his technical assistance for the data analysis and simulation studies. The research of Emura T is funded by the grant from the Ministry of Science and Technology of Taiwan (MOST, 107-2118-M-008 -003 -MY3).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Takeshi Emura.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (CSV 94 kb)

Supplementary material 2 (PDF 1354 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wu, B., Michimae, H. & Emura, T. Meta-analysis of individual patient data with semi-competing risks under the Weibull joint frailty–copula model. Comput Stat 35, 1525–1552 (2020). https://doi.org/10.1007/s00180-020-00977-1

Download citation

Keywords

  • Clustered survival data
  • Gamma frailty
  • Mean residual life
  • Hierarchical model
  • Random effects
  • Survival analysis