Order restricted inference for comparing the cumulative incidence of a competing risk over several populations
Hammou El Barmi, Subhash Kochar, Hari Mukerjee
Abstract
There is a substantial literature on testing for the equality of the cumulative incidence functions associated with one specific cause in a competing risks setting across several populations against specific or all alternatives. In this paper we propose an asymptotically distribution-free test when the alternative is that the incidence functions are linearly ordered, but not equal. The motivation stems from the fact that in many examples such a linear ordering seems reasonable intuitively and is borne out generally from empirical observations. These tests are more powerful when the ordering is justified. We also provide estimators of the incidence functions under this ordering constraint, derive their asymptotic properties for statistical inference purposes, and show improvements over the unrestricted estimators when the order restriction holds.
Full-text: Open access
Permanent link to this document: http://projecteuclid.org/euclid.imsc/1207058263
Digital Object Identifier: doi:10.1214/193940307000000040
References
[1] Breslow, N. and Crowley, J. (1974). A large sample study of the life table and product limit estimates under random censorship. Ann. Statist. 2 437–453.
[2] Brunk, H. D., Franck, W. E., Hanson, D. L. and Hogg, R. V. (1966). Maximum likelihood estimation of the distributions of two stochastically ordered random variables. J. Amer. Statist. Assoc. 61 1067–1080.
[3] El Barmi, H. and Mukerjee, H. (2005). Inferences under a stochastic ordering constraint: The k-sample case. J. Amer. Statist. Assoc. 100 252–261.
[4] El Barmi, H. and Mukerjee, H. (2006). Restricted estimation of the cumulative incidence functions corresponding to competing risks. In Lecture Notes Monogr. Ser. 49 241–252. Institute of Mathematical Statistics.
[5] El Barmi, H., Kochar, S. C., Mukerjee, H. and Samaniego, F. J. (2004). Estimation of cumulative incidence functions in competing risks studies under an order restriction. J. Statist. Plann. Inference 118 145–165.
[6] Fleming, T. R. and Harrington, D. P. (1991). Counting Processes and Survival Analysis. Wiley, New York.
[7] Gray, R. J. (1988). A class of K-sample tests for comparing the cumulative incidence of a competing risk. Ann. Statist. 16 1141–1154.
[8] Hoel, D. G. (1972). A representation of mortality data by competing risks. Biometrics 28 475–478.
[9] Hogg, R. V. (1962). Iterated tests of the equality of several distributions. J. Amer. Statist. Assoc. 61 579–585.
[10] Kelly, R. E. (1989). Stochastic reduction of loss in estimating normal means by isotonic regression. Ann. Statist. 17 937–940.
[11] Lin, D. Y. (1997). Non-parametric inference for cumulative incidence functions in competing risks studies. Statistics in Medicine 16 901–910.
[12] Lin, D. Y., Fleming, T. R. and Wei, L. J. (1994). Confidence bands for survival curves under the proportional hazards model. Biometrika 81 73–81.
[13] Luo, X. and Turnbull, B. W. (1999). Comparing two treatments with multiple competing risks endpoints. Statist. Sinica 9 985–997.
[14] Pepe, M. and Mori, M. (1993). Kaplan-Meier, marginal or conditional probability curves in summarizing competing risks failure time data. Statistics in Medicine 12 737–751.
[15] Peterson, Jr., A. V. (1977). Expressing the Kaplan-Meier estimator as a function of empirical subsurvival functions. J. Amer. Statist. Assoc. 72 854–858.
[16] Præstgaard, J. T. and Huang, J. (1996). Asymptotic theory for nonparametric estimation of survival curves under order restrictions. Ann. Statist. 24 1679–1716.
[17] Robertson, T., Wright, F. T. and Dykstra, R. L. (1988). Order Restricted Statistical Inference. Wiley, New York.
[18] Rojo, J. (1995). On the weak convergence of certain estimators of stochastically ordered survival functions. J. Nonparametr. Statist. 4 349–363.
[19] Rojo, J. (2004). On the estimation of survival functions under a stochastic order constraint. In The First Erich L. Lehmann Symposium—Optimality. IMS Lecture Notes Monogr. Ser. 44 37–61. Inst. Math. Statist., Beachwood, OH.
[20] Rojo, J. and Ma, Z. (1996). On the estimation of stochastically ordered survival functions. J. Statist. Comput. Simulation 55 1–21.
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