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Semiparametric Box–Cox power transformation models for censored survival observations

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  • Tianxi Cai
  • Lu Tian
  • L. J. Wei

Abstract

The accelerated failure time model specifies that the logarithm of the failure time is linearly related to the covariate vector without assuming a parametric error distribution. In this paper, we consider the semiparametric Box--Cox transformation model, which includes the above regression model as a special case, to analyse possibly censored failure time observations. Inference procedures for the transformation and regression parameters are proposed via a resampling technique. Prediction of the survival function of future subjects with a specific covariate vector is also provided via pointwise and simultaneous interval estimates. All the proposals are illustrated with datasets from two clinical studies. Copyright 2005, Oxford University Press.

Suggested Citation

  • Tianxi Cai & Lu Tian & L. J. Wei, 2005. "Semiparametric Box–Cox power transformation models for censored survival observations," Biometrika, Biometrika Trust, vol. 92(3), pages 619-632, September.
    • Handle: RePEc:oup:biomet:v:92:y:2005:i:3:p:619-632
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    File URL: http://hdl.handle.net/10.1093/biomet/92.3.619
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    Citations

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    Cited by:

    1. Atkinson, Anthony C. & Riani, Marco & Corbellini, Aldo, 2021. "The box-cox transformation: review and extensions," LSE Research Online Documents on Economics 103537, London School of Economics and Political Science, LSE Library.
    2. Layla Parast & Beth Ann Griffin, 2017. "Landmark estimation of survival and treatment effects in observational studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 161-182, April.
    3. Roland A. Matsouaka & Junlong Li & Tianxi Cai, 2014. "Evaluating marker-guided treatment selection strategies," Biometrics, The International Biometric Society, vol. 70(3), pages 489-499, September.
    4. Yingye Zheng & Tianxi Cai & Janet L. Stanford & Ziding Feng, 2010. "Semiparametric Models of Time-Dependent Predictive Values of Prognostic Biomarkers," Biometrics, The International Biometric Society, vol. 66(1), pages 50-60, March.
    5. Denis Agniel & Tianxi Cai, 2017. "Analysis of multiple diverse phenotypes via semiparametric canonical correlation analysis," Biometrics, The International Biometric Society, vol. 73(4), pages 1254-1265, December.
    6. Layla Parast & Carolyn M. Rutter, 2017. "Discussion of “A risk-based measure of time-varying prognostic discrimination for survival models,” by C. Jason Liang and Patrick J. Heagerty," Biometrics, The International Biometric Society, vol. 73(3), pages 742-744, September.
    7. Lu Mao & Tuo Wang, 2021. "A class of proportional win‐fractions regression models for composite outcomes," Biometrics, The International Biometric Society, vol. 77(4), pages 1265-1275, December.
    8. Zhang, Haixiang & Huang, Jian & Sun, Liuquan, 2020. "A rank-based approach to estimating monotone individualized two treatment regimes," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    9. Yuanyuan Shen & Tianxi Cai, 2016. "Identifying predictive markers for personalized treatment selection," Biometrics, The International Biometric Society, vol. 72(4), pages 1017-1025, December.

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