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Analysing competing risks data with transformation models

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  • J. P. Fine

Abstract

We present a flexible class of marginal models for the cumulative incidence function. The semiparametric transformation model is utilized in a decomposition for the marginal failure probabilities which extends previous work on Farewell's cure model. Novel estimation, inference and prediction procedures are developed, with large sample properties derived from the theory of martingales and U‐statistics. A small simulation study demonstrates that the methods are appropriate for practical use. The methods are illustrated with a thorough analysis of a prostate cancer clinical trial. Simple graphical displays are used to check for the goodness of fit.

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  • J. P. Fine, 1999. "Analysing competing risks data with transformation models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 817-830.
    • Handle: RePEc:bla:jorssb:v:61:y:1999:i:4:p:817-830
    DOI: 10.1111/1467-9868.00204
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    Cited by:

    1. Peter Gilbert & Ian McKeague & Yanqing Sun, 2004. "Tests for Comparing Mark-Specific Hazards and Cumulative Incidence Functions," UW Biostatistics Working Paper Series 1032, Berkeley Electronic Press.
    2. Gang Li & Qing Yang, 2016. "Joint Inference for Competing Risks Survival Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1289-1300, July.
    3. Yayuan Zhu & Ziqi Chen & Jerald F. Lawless, 2022. "Semiparametric analysis of interval‐censored failure time data with outcome‐dependent observation schemes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 236-264, March.
    4. Tomi Kyyrä, 2009. "Marginal Effects for Competing Risks Models with Piecewise Constant Hazards," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(4), pages 539-565, August.
    5. Jianbo Li & Minggao Gu & Tao Hu, 2012. "General partially linear varying-coefficient transformation models for ranking data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1475-1488, January.
    6. Virve Ollikainen & Tomi Kyyrä, 2006. "To Search or Not to Search? The Effects of UI Benefit Extension for the Elderly Unemployed," Discussion Papers 400, Government Institute for Economic Research Finland (VATT).
    7. Chia-Hui Huang, 2019. "Mixture regression models for the gap time distributions and illness–death processes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 168-188, January.
    8. Frank Eriksson & Jianing Li & Thomas Scheike & Mei‐Jie Zhang, 2015. "The proportional odds cumulative incidence model for competing risks," Biometrics, The International Biometric Society, vol. 71(3), pages 687-695, September.
    9. Hu, Tao & Xiang, Liming, 2016. "Partially linear transformation cure models for interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 257-269.
    10. Chia-Hui Huang & Bowen Li & Chyong-Mei Chen & Weijing Wang & Yi-Hau Chen, 2017. "Subdistribution Regression for Recurrent Events Under Competing Risks: with Application to Shunt Thrombosis Study in Dialysis Patients," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 339-356, December.
    11. Lin, Cunjie & Zhou, Yong, 2014. "Analyzing right-censored and length-biased data with varying-coefficient transformation model," Journal of Multivariate Analysis, Elsevier, vol. 130(C), pages 45-63.
    12. Mats J. Stensrud & Jessica G. Young & Torben Martinussen, 2021. "Discussion on “Causal mediation of semicompeting risks” by Yen‐Tsung Huang," Biometrics, The International Biometric Society, vol. 77(4), pages 1160-1164, December.
    13. Kyyrä, Tomi, 2007. "Studies on Wage Differentials and Labour Market Transitions," Research Reports 133, VATT Institute for Economic Research.
    14. Ambrogi, Federico & Biganzoli, Elia & Boracchi, Patrizia, 2009. "Estimating crude cumulative incidences through multinomial logit regression on discrete cause-specific hazards," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2767-2779, May.

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