Journal of the Korean Statistical Society

The Korean Statistical Society (한국통계학회)
권호 표지

A Kolmogorov-Smirnov-Type Test for Independence of Bivariate Failure Time Data Under Independent Censoring

  • Kim, Jingeum (Department of Applied Statistics, University of Suwon)
  • Published : 1999.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

We propose a Kolmogorov-Smirnov-type test for independence of paired failure times in the presence of independent censoring times. This independent censoring mechanism is often assumed in case-control studies. To do this end, we first introduce a process defined as the difference between the bivariate survival function estimator proposed by Wang and Wells (1997) and the product of the product-limit estimators (Kaplan and Meier (1958)) for the marginal survival functions. Then, we derive its asymptotic properties under the null hypothesis of independence. Finally, we assess the performance of the proposed test by simulations, and illustrate the proposed methodology with a dataset for remission times of 21 pairs of leukemia patients taken from Oakes(1982).

Keywords

References

  1. The Annals of Statistics v.14 Simulated Power Functions Beran, R.
  2. Biometrika v.65 A Model for Association in Bivariate Life Tables and Its Application in Epideiological Studies of Familial Tendency in Chronic Disease Incidence Clayton, D. G.
  3. Biometrika v.69 Rank Tests for Association with Right Censored Data Cuzick, J.
  4. The Annals of Statistics v.14 Rank Tests for Independence for Bivariate Censored Data Dabrowska, D. M.
  5. Journal of the American Statistical Association v.55 Bivariate Exponential Distributions Gumbel, E. J.
  6. Journal of the American Statistical Association v.53 Nonparametric Estimation from Incomplete Observations Kaplan, E. L.;Meier, P.
  7. Biometrics v.38 A Concordance Test for Independence in the Presence of Censoring Oakes, D.
  8. Scandinavian Journal of Statistics v.13 A Test of Independence Between Two Censored Survival Times Pons, O.
  9. Scandinavian Journal of Statistics v.18 Tests of Independence for Bivariate Censored Data Based on the Empirical Joint Hazard Function Pons, P.;de Turckheim, E.
  10. Biormtrika v.79 Covariance and Survivor Function Estimation Using Censored Multivariate Failure Time Data Prentice, R. L.;Cai, J.
  11. Biometrics v.52 Tests of Indepedence for Bivariate Survival Data Shih, J. H.;Louis, T. A.
  12. Biometrika v.84 Nonparametric Estimators of the Bivariate Survival Function Under Simplified Censoring Conditions Wang, W.;Well, M. T.