Evaluating health outcomes in the presence of competing risks: A review of statistical methods and clinical applications

Medical Care

Volumn 48, Issue 6 SUPPL., 2010, Pages

  Varadhan, Ravi ^a,b   Weiss, Carlos O ^a   Segal, Jodi B ^a,b   Wu, Albert W ^a,b   Scharfstein, Daniel ^b   Boyd, Cynthia ^a,b  

^a JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Cause specific hazards; Comparative effectiveness; Cumulative incidence function; Event free survival; Semicompeting risks

Indexed keywords

DIETHYLSTILBESTROL;

CARDIOVASCULAR DISEASE; CAUSE SPECIFIC HAZARD; CLINICAL EVALUATION; COMPARATIVE EFFECTIVENESS; CUMULATIVE INCIDENCE FUNCTION; DEATH; EVENT FREE SURVIVAL; HUMAN; KAPLAN MEIER METHOD; MEDICAL INFORMATION; MORTALITY; NEOPLASM; OUTCOME ASSESSMENT; PRIORITY JOURNAL; PROPORTIONAL HAZARDS MODEL; PROSTATE CANCER; REGRESSION ANALYSIS; REVIEW; RISK ASSESSMENT; RISK REDUCTION; SIMULATION; STATISTICAL ANALYSIS; COMPUTER SIMULATION; DISEASE FREE SURVIVAL; METHODOLOGY; RISK FACTOR; STATISTICAL MODEL; STATISTICS; TREATMENT OUTCOME;

COMPARATIVE EFFECTIVENESS RESEARCH; COMPUTER SIMULATION; DISEASE-FREE SURVIVAL; HUMANS; LOGISTIC MODELS; RISK FACTORS; STATISTICS AS TOPIC; TREATMENT OUTCOME;

EID: 77953636324     PISSN: 00257079     EISSN: None     Source Type: Journal    
DOI: 10.1097/MLR.0b013e3181d99107     Document Type: Review

References (31)

1. Seal HL. Studies in the history of probability and statistics. XXXV: multiple decrements or competing risks. Biometrika. 1977;64:429-439.
2. Fix E, Neyman J. A simple stochastic model of recovery, relapse, death and loss of patients. Hum Biol. 1951;23:205-241.
3. Tsiatis A. A nonidentifiability aspect of the problem of competing risks. Proc Natl Acad Sci USA. 1975;72:20-22.
4. Prentice RL, Kalbfleisch JD, Peterson AV Jr, et al. The analysis of failure times in the presence of competing risks. Biometrics. 1978;34: 541-554.
5. Gooley TA, Leisenring W, Crowley J, et al. Estimation of failure probabilities in the presence of competing risks: new representations of old estimators. Stat Med. 1999;18:695-706.
6. Gaynor JJ. On the use of cause-specific failure and conditional failure probabilities. Examples from clinical oncology data. J Am Stat Assoc. 1993;88:400.
7. Pepe MS. Inference for events with dependent risks in multiple endpoint studies. J Am Stat Assoc. 1991;86:770.
8. Kay R, Schumacher M. Unbiased assessment of treatment effects on disease recurrence and survival in clinical trials. Stat Med. 1983;2:41-58.
9. Lentine KL, Rocca Rey LA, Kolli S, et al. Variations in the risk for cerebrovascular events after kidney transplant compared with experience on the waiting list and after graft failure. Clin J Am Soc Nephrol. 2008;3:1090-1101.
10. Ridker PM, Danielson E, Fonseca FA, et al. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N Engl J Med. 2008;359:2195-2207.
11. Freemantle N, Calvert M, Wood J, et al. Composite outcomes in randomized trials: greater precision but with greater uncertainty? JAMA. 2003;289:2554-2559.
12. Walter LC, Covinsky KE. Cancer screening in elderly patients: a framework for individualized decision making. JAMA. 2001;285:2750-2756.
13. Gail MH, Constantino JP, Bryant J, et al. Weighing the risk and benefits of tamoxifen treatment for preventing breast cancer. J Natl Cancer Inst. 1999;91:1829-1846.
14. Kalbfleisch JD, Prentice RL. The Statistical Analysis of Failure Time Data. New York, NY: Wiley; 1980.
15. Benichou J, Gail MH. Estimates of absolute cause-specific risk in cohort studies. Biometrics. 1990;46:813-826.
16. Korn EL, Dorey FJ. Applications of crude incidence curves. Stat Med. 1992;11:813-829.
17. Gray RJ. Class of K-sample tests for comparing the cumulative incidence of a competing risk. Ann Stat.1988;16:1141.
18. Cheng SC, Fine JP, Wei LJ. Prediction of cumulative incidence function under the proportional hazards model. Biometrics. 1998;54:219-228.
19. Fine JP, Gray RJ. Proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1999;94:496.
20. Fine JP. Regression modeling of competing crude failure probabilities. Biostatistics. 2001;2:85-97.
21. Scheike TH. Direct modelling of regression effects for transition probabilities in multistate models. Scand J Stat. 2007;34:17.
22. Klein JP, Andersen PK. Regression modeling of competing risks data based on pseudovalues of the cumulative incidence function. Biometrics. 2005;61:223-229.
23. Robins JM. An analytic method for randomized trials with informative censoring: part 1. Lifetime Data Anal. 1995;1:241-254.
24. R Development Care Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2009.
25. Lauer MS, Topol EJ. Clinical trials-multiple treatments, multiple end points, and multiple lessons. JAMA. 2003;289:2575-2577.
26. Byar DP, Green SB. The choice of treatment for cancer patients based on covariate information. Bull Cancer. 1980;67:477-490.
27. Kay R. Treatment effects in competing-risks analysis of prostate cancer data. Biometrics. 1986;42:203-211.
28. Lin DY, Robins JM, Wei JY. Comparing two failure time distributions in the presence of dependent censoring. Biometrika. 1996;83:381-393.
29. Peng L, Fine JP. Regression modeling of semicompeting risks data. Biometrics. 2007;63:96-108.
30. Rubin DB. Causal inference without counterfactuals, by A.P. Dawid [comment]. J Am Stat Assoc. 2000;95:435-438.
31. Peto R, Peto J. Asymptotically efficient rank invariance test procedures. J R Stat Soc. 1972;135:185-206.