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Incorporating Follow-up Time in M-Estimation for Survival Data

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Abstract

It has been approximately 30 years since D.R. Cox introduced the proportional hazards method to model the relationship between covariates and survival time. However, the proportional hazards model has limited value when the proportionality assumption is violated. Over the years, there have many been many alternative proposals to the proportional hazards regression model for the case of right censored survival data, but to date none have demonstrated widespread acceptance. In general, problems encountered in these methods include their computational algorithms or evaluation of their asymptotic properties. In this work, an estimating equation based on a U-statistic of degree 2 is proposed. It is easy to implement and the U-statistic framework provides a straightforward development of asymptotic inferential theory for the regression parameters.

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References

  • P. J. Bickel, F. Gotze, and W. R. van Zwet, “The Edgeworth expansion for U-statistics of degree two,” Ann. Stat. vol. 14 pp. 1463–1484, 1986.

    Google Scholar 

  • G. E. P. Box and D. R. Cox, “An analysis of transformations,” J. Roy. Stat. Soc., Ser. B vol. 26 pp. 211–246, 1964.

    Google Scholar 

  • J. Buckley and I. R. James, “Linear regression with censored data,” Biometrika vol. 66 pp. 429–436, 1979.

    Google Scholar 

  • D. R. Cox, “Regression models and life tables (with Discussion),” J. Roy. Stat. Soc., Ser. B vol. 34 pp. 187–220, 1972.

    Google Scholar 

  • M. Fygenson and Y. Ritov, “Monotone estimating equations for censored data,” Ann. Stat. vol. 22 pp. 732–746, 1994.

    Google Scholar 

  • R.J. Gray, “Estimation of regression parameters and the hazard function in transformed linear survival models,” Biometrics vol. 56 pp. 571–576, 2000.

    Google Scholar 

  • W. Hardle, P. Hall, and H. Ichimura, “Optimal smoothing in single-index models,” Ann. Stat. vol. 21 pp. 157–178, 1993.

    Google Scholar 

  • G. Heller and J. S. Simonoff, “A comparison of estimators for regression with a censored response variable,” Biometrika vol. 77 pp. 515–520, 1990.

    Google Scholar 

  • K. R. Hess, D. M. Serachitopol, and B. W. Brown, “Hazard function estimators: A simulation study,” Stat. Med. vol. 18 pp. 3075–3088, 1999.

    Google Scholar 

  • M. Joffe, “Administrative and artificial censoring in censored regression models,” Stat. Med. vol. 20 pp. 2287–2304, 2001.

    Google Scholar 

  • T. L. Lai and Z. Ying, “Large sample theory of a modified Buckley-James estimator for regression analysis with censored data,” Ann. Stat. vol. 19 pp. 1370–1402, 1991.

    Google Scholar 

  • R. G. Miller, Survival Analysis, Wiley: New York, 1981.

    Google Scholar 

  • J. A. Nelder and R. Mead, “A simplex method for function minimization,” Comput. J. vol. 7 pp. 308–313, 1965.

    Google Scholar 

  • D. Oakes, “A note on the Kaplan-Meier estimator,” Am. Stat. vol. 47 pp. 39–40, 1993.

    Google Scholar 

  • Y. Ritov, “Estimation in a linear regression model with censored data,” Ann. Stat. vol. 18 pp. 303–328, 1990.

    Google Scholar 

  • J. Robins, “Estimation of the time-dependent accelerated failure time model in the presence of confounding factors,” Biometrika vol. 79 pp. 321–334, 1992.

    Google Scholar 

  • J. M. Robins and A. A. Tsiatis, “Correcting for non-compliance in randomized trials using rank preserving structural failure time models,” Commun. Stat. Theo. Meth. vol. 20 pp. 2609–2631, 1991.

    Google Scholar 

  • G. R. Shorack and J. A. Wellner, Empirical Processes with Applications to Statistics, Wiley: New York, 1986.

    Google Scholar 

  • A. A. Tsiatis, “Estimating regression parameters using linear rank tests for censored data,” Ann. Stat. vol. 18 pp. 354–372, 1990.

    Google Scholar 

  • Z. Ying, “A large sample study of rank estimation for censored regression data,” Ann. Stat. vol. 21 pp. 76–99, 1993.

    Google Scholar 

  • Z. Ying, S. H. Jung, and L. J. Wei, “Survival analysis with median regression models,” J. Am. Stat. Assoc. vol. 90 pp. 178–184, 1995.

    Google Scholar 

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Heller, G. Incorporating Follow-up Time in M-Estimation for Survival Data. Lifetime Data Anal 10, 51–64 (2004). https://doi.org/10.1023/B:LIDA.0000019255.21735.9b

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  • DOI: https://doi.org/10.1023/B:LIDA.0000019255.21735.9b

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