Application of Adjusted Survival Curves to Renal Transplant Data
08 February 2018 (online)
An important means in the analysis of survival time data is the estimation and graphical representation of survival probabilities. In this paper unifactorial parametric and non-parametric survival curve estimators and two types of adjusted survival curves based on a parametric multifactorial approach are applied to renal transplant data. It is shown that the resulting survival curves can differ substantially. The unifactorial survival curves yield biased results in case of serious disequilibrium in the data. This drawback of the unifactorial methods has been overcome by the use of adjusted survival curves which take possible distortions in the data set into account. The benefits of adjusted survival curves in assessing potentially prognostic factors are elucidated by the application to data from renal transplantation.
- 1 Kalbfleisch JD, Prentice RL. The Statistical Analysis of Failure Time Data. New York: Wiley; 1980
- 2 Geurts JHJ. Some small-sample non-proportional hazards results for the Kaplan-Meier estimator. Statistica Neerlandica 1985; 01: 1-13.
- 3 Geurts JHJ. On the small-sample performance of Efron’s and of Gill’s version of the product limit estimator under nonpropor-tional hazards. Biometrics 1987; 43: 683-92.
- 4 Gill R. Large sample behaviour of the product-limit estimator on the whole line. Ann Statist 1983; 11 (01) 49-58.
- 5 Breslow N, Crowley J. A large sample study of the life table and product limit estimates under random censorship. Ann Statist 1974; 02: 437-53.
- 6 Chen YY, Hollander M, Langberg NA. Small-sample results for the Kaplan-Meier estimator. J Am Statist Assoc 1982; 77: 141-44.
- 7 Miller RG. What price Kaplan-Meier?. Biometrics 1983; 39: 1077-81.
- 8 Chang I, Gelman R, Pagano M. Corrected group prognostic curves and summary statistics. J Chron Dis 1982; 35: 669-74.
- 9 Makuch RW. Adjusted survival curve estimation using covariates. J Chron Dis 1982; 35: 437-43.
- 10 Amato DA. A generalized Kaplan-Meier estimator for heterogeneous populations. Commun Statist-Theory Meth 1988; 17: 263-86.
- 11 Opelz G. Analysis of clinical transplant data: a personal comment. In: Acquisition, Analysis and Use of Clinical Transplant Data. Janßen R, Opelz G. eds. Lecture Notes in Medical Informatics. Heidelberg: Springer; 1987: 69-77.
- 12 Hennige M, Köhler CO, Opelz G. Multivariate prediction model of kidney transplant success rates. Transplantation 1986; 42: 491-3.
- 13 Bonsel GJ, Klompmaker IJ, van’t Veer F. et al. Use of prognostic models for assessment of value of liver transplantation in primary biliary cirrhosis. The Lancet 1990; 335: 493-7.
- 14 SAS. SAS/STAT User’s Guide. Release 6.03 Edition. Cary, NC: SAS Institute Inc; 1988
- 15 Ciampi A, Lawless JF, McKinney SM. et al. Regression and recursive partition strategies in the analysis of medical survival data. J Clin Epidemiol 1988; 41: 737-48.
- 16 Draper NR, Smith H. Applied Regression Analysis. New York: Wiley; 1966
- 17 RS/1 User’s Guide: Graphs. Release 4. Cambridge: BBN Software Products Corporation; 1988
- 18 RS/1 User’s Guide: RPL Programming. Release 4. Cambridge: BBN Software Products Corporation; 1988
- 19 RS/1 User’s Guide: Statistical Tools. Release 4. Cambridge: BBN Software Products Corporation; 1988
- 20 Svejgaard A, Hauge M, Jersild C. et al. The HLA-System. An Introductory Survey. 2nd ed.. Monographs in Human Genetics. Vol. 7. Basel: Karger; 1979
- 21 Markus BH, Dickson ER, Grambsch PM. et al. Transplantation improves survival in patients with primary biliary cirrhosis: comparison of estimated survival based on Mayo model actual survival in the Pittsburgh transplant population. N Engl J Med 1989; 320: 1709-13.
- 22 Neuberger J, Altman DG, Christensen E. et al. Use of a prognostic index in evaluation of liver transplantation for primary biliary cirrhosis. Transplantation 1986; 41: 713-6.
- 23 Thomsen BL, Keiding N, Altman DG. A note on the calcuclation of expected survival, illustrated by the survival of liver transplant patients. Statist Med 1991; 10: 733-8.