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Regression analysis based on conditional likelihood approach under semi-competing risks data

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Abstract

Medical studies often involve semi-competing risks data, which consist of two types of events, namely terminal event and non-terminal event. Because the non-terminal event may be dependently censored by the terminal event, it is not possible to make inference on the non-terminal event without extra assumptions. Therefore, this study assumes that the dependence structure on the non-terminal event and the terminal event follows a copula model, and lets the marginal regression models of the non-terminal event and the terminal event both follow time-varying effect models. This study uses a conditional likelihood approach to estimate the time-varying coefficient of the non-terminal event, and proves the large sample properties of the proposed estimator. Simulation studies show that the proposed estimator performs well. This study also uses the proposed method to analyze AIDS Clinical Trial Group (ACTG 320).

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References

  • Chang SH (2000) A two-sample comparison for multiple ordered event data. Biometrics 56: 183–189

    Article  MathSciNet  MATH  Google Scholar 

  • Ding A, Wang W, Hsieh JJ, Shi G (2009) Marginal regression analysis for semi-competing risks data under dependent censoring. Scand J Stat 36: 481–500

    Article  MathSciNet  MATH  Google Scholar 

  • Fahrmeir L, Klinger A (1998) A nonparametric multiplicative hazard model for event history analysis. Biometrika 85: 581–592

    Article  MATH  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  • Fine JP, Yan J, Kosorok MR (2004) Temporal process regression. Biometrika 91: 683–703

    Article  MathSciNet  MATH  Google Scholar 

  • Goffman C (1965) Calculus of several variables. Harper and Row, New York

    Google Scholar 

  • Hosmer DW, Lemeshow S, May S (2008) Applied survival analysis: regression modeling of time-to-event data. Wiley, New York

    Book  MATH  Google Scholar 

  • Hsieh JJ, Wang W, Ding A (2008) Regression analysis based on semi-competing risks data. J R Stat Soc B 70(Part 1): 3–20

    MathSciNet  MATH  Google Scholar 

  • Kalbfleisch JD, Prentice RL (2002) The statistical analysis of failure time data. Wiley, New York

    Book  MATH  Google Scholar 

  • Lin DY, Robins JM, Wei LJ (1996) Comparing two failure time distributions in the presence of dependent censoring. Biometrika 83: 381–393

    Article  MathSciNet  MATH  Google Scholar 

  • Martinussen T, Scheike TH, Skovgaard IM (2002) Efficient estimation of fixed and time-varying covariate effects in multiplicative intensity models. Scand J Stat 29: 57–74

    Article  MathSciNet  MATH  Google Scholar 

  • Murphy SA, Sen PK (1991) Time-dependent coefficients in a Cox-type regression model. Stoch Process Appl 39: 153–180

    Article  MathSciNet  MATH  Google Scholar 

  • Peng L, Fine JP (2005) Regression analysis of semi-competing risks data. Technical Report 1097, Department of Statistics, University of Wisconsin, Madison

  • Peng L, Fine JP (2006) Rank estimation of accelerated lifetime models with dependent censoring. J Am Stat Assoc 101: 1085–1093

    Article  MathSciNet  MATH  Google Scholar 

  • Peng L, Fine JP (2007) Regression modeling of semi-competing risks data. Biometrics 63: 96–108

    Article  MathSciNet  MATH  Google Scholar 

  • Zucker DM, Karr AF (1990) Nonparametric survival analysis with time-dependent effects: a penalized partial likelihood approach. Annals Stat 18: 329–353

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Jin-Jian Hsieh.

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Hsieh, JJ., Huang, YT. Regression analysis based on conditional likelihood approach under semi-competing risks data. Lifetime Data Anal 18, 302–320 (2012). https://doi.org/10.1007/s10985-012-9219-3

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  • DOI: https://doi.org/10.1007/s10985-012-9219-3

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