Given a sample of regression data from (Y, Z), a new diagnostic plotting method is proposed for checking the hypothesis H0: the data are from a given Cox model with the time-dependent covariates Z. It compares two estimates of the marginal distribution FY of Y. One is an estimate of the modified expression of FY under H0, based on a consistent estimate of the parameter under H0, and based on the baseline distribution of the data. The other is the Kaplan-Meier-estimator of FY, together with its confidence band. The new plot, called the marginal distribution plot, can be viewed as a test for testing H0. The main advantage of the test over the existing residual tests is in the case that the data do not satisfy any Cox model or the Cox model is mis-specified. Then the new test is still valid, but not the residual tests and the residual tests often make type II error with a very large probability.
References
[1]
Cox, D.R. and Oakes, D. (1984) Analysis of Survival Data. Chapman and Hall, New York.
[2]
Kalbfleisch, J.D. and Prentice, R.L. (1980) The Statistical Analysis of Failure Time Data. Wiley, New York.
[3]
Zhou, M. (2001) Understanding the Cox Regression Models with Time-Change Covariates. American Statistician, 55, 153-155.
https://doi.org/10.1198/000313001750358491
[4]
Therneau, T.M., Grambsch, P.M. and Fleming, T.R. (1990) Martingale-Based Residuals for Survival Models. Biometrika, 77, 147-160.
https://doi.org/10.1093/biomet/77.1.147
[5]
Baltazar-Aban, I. and Pena, E. (1995) Properties of Hazard-Based Residuals and Implications in Model Diagnostics. Journal of the American Statistical Association, 90, 185-197. https://doi.org/10.1080/01621459.1995.10476501
[6]
Barlow, W.E. and Prentice, R.L. (1998) Residuals for Relative Risk Regression. Biometrika, 75, 65-74. https://doi.org/10.1093/biomet/75.1.65
[7]
Lin, D.Y., Wei, L.J. and Ying, Z. (1993) Checking the Cox Model with Cumulative Sums of the Martingale-Based Residuals. Biometrika, 81, 557-572.
https://doi.org/10.1093/biomet/80.3.557
[8]
Scheike, T.H. and Martinussen, T. (2004) On Estimation and Tests of Time-Varying Effects in the Proportional Hazards Model. Scandinavian Journal of Statistics, 31, 51-62. https://doi.org/10.1111/j.1467-9469.2004.00372.x
[9]
Hastie, T.J. and Tibshirani, R.J. (1990) Generalized Additive Models. Chapman and Hall, New York.
[10]
Grambsch, P.M. and Therneau, T.M. (1994) Proportional Hazards Tests and Diagnostics Based on Weighted Residuals. Biometrika, 81, 515-526.
https://doi.org/10.1093/biomet/81.3.515
[11]
Wong, G.Y.C., Osborne, M.P., Diao, Q. and Yu, Q. (2016) Piecewise Cox Models With Right-Censored Data. Communication in Statistics: Simulation and Computation, Online.
[12]
Tian, L., Zucker, D. and Wei, L.J. (2005) On the Cox Model with Time-Varying Regression Coefficients. Journal of the American Statistical Association, 100, 172-183.
https://doi.org/10.1198/016214504000000845
[13]
Therneau, T.M. and Grambsch, P.M. (2000) Modeling Survival Data: Extending the Cox Model. Springer, Berlin. https://doi.org/10.1007/978-1-4757-3294-8