Practical methods for competing risks data: A review

Statistical Methods in Medical Research

Volumn 21, Issue 3, 2012, Pages 257-272

  Bakoyannis, Giorgos ^a   Touloumi, Giota ^a  

^a UNIVERSITY OF ATHENS MEDICAL SCHOOL   (Greece)

Author keywords

cause specific hazard; competing risks; cumulative incidence; Fine and Gray model

Indexed keywords

ANTIRETROVIRUS AGENT;

ACQUIRED IMMUNE DEFICIENCY SYNDROME; CONFERENCE PAPER; DATA ANALYSIS; DEATH; EUROPE; FOLLOW UP; HIGHLY ACTIVE ANTIRETROVIRAL THERAPY; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS 1 INFECTION; INCIDENCE; MAJOR CLINICAL STUDY; MULTICENTER STUDY (TOPIC); PROPORTIONAL HAZARDS MODEL; RISK ASSESSMENT; SEROCONVERSION; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICAL PARAMETERS; TREATMENT OUTCOME;

ANTIRETROVIRAL THERAPY, HIGHLY ACTIVE; HIV INFECTIONS; HUMANS; MODELS, THEORETICAL; RISK ASSESSMENT;

EID: 84861836657     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280210394479     Document Type: Conference Paper

References (52)

1. Concerted Action on SeroConversion to AIDS and Death in Europe. Time from HIV-1 seroconversion to AIDS and death before widespread use of highly-active antiretroviral therapy: a collaborative re-analysis. Lancet. 2000 ; 355: 1131-1137
2. Porter K, Babiker A, Bhaskaran K, et al. Determinants of survival following HIV-1 seroconversion after the introduction of HAART. Lancet. 2003 ; 362: 1267-1274
3. Cox DR, Oakes D Analysis of survival data. London: Chapman & Hall ; 1984 :
4. Kalbfleisch JD, Prentice RL The statistical analysis of failure time data, 2nd ed. New York: Wiley ; 2002 :
5. Collet D Modeling survival data in medical research. New York: Chapman & Hall/CRC ; 1994 :
6. Tsiatis AA Encyclopedia of biostatistics. New York: Wiley ; 1999 :
7. Pepe MS, Mori M. Kaplan-Meier, marginal or conditional probability curves in summarizing competing risks failure time data?. Stat Med. 1993 ; 12: 737-751
8. Putter H, Fiocco M, Geskus RB. Tutorial in biostatistics: competing risks and multi-state models. Stat Med. 2007 ; 26: 2389-2430
9. BakoyannisGTouloumiG, on behalf of CASCADE collaboration. A practical guide on modeling competing risk data, www.cascade-collaboration.org (accessed June 2010).
10. Touloumi G, Pantazis N, Antoniou A, Stirnadel HA, Walker SA, Porter K. Highly active antiretroviral therapy interruption: predictors and virological and immunologic consequences. J Acquir Immune Defic Syndr. 2006 ; 42: 554-561
11. Prentice R, Kalbfleisch J, Peterson A, Flournoy N, Farewell V, Breslow N. The analysis of failure times in the presence of competing risks. Biometrics. 1978 ; 34: 541-554
12. Changes in the uptake of antiretroviral therapy and survival in people with known duration of HIV infection in Europe: results from CASCADE. HIV Med. 2000 ; 1: 224-231
13. Tsiatis AA. A nonidentifiability aspect of the problem of competing risks. Proc Nat Acad Sci USA. 1975 ; 72: 20-22
14. Andersen PK, Abildstrom SZ, Rosthoj S. Competing risks as a multi-state model. Stat Methods Med Res. 2002 ; 11: 203-215
15. Gail M. A review and critique of some models used in competing risk analysis. Biometrics. 1975 ; 31: 209-222
16. Andersen PK, Borgan O, Gill RD, Keiding N Statistical models based on counting processes. New York: Springer ; 1996 :
17. Gaynor JJ, Feuer EJ, Tan CC, et al. On the use of cause-specific failure and conditional failure probabilities: examples from clinical oncology data. J Am Stat Assoc. 1993 ; 88: 400-409
18. Aalen O, Johansen S. An empirical transition matrix for non-homogeneous Markov chains based on censored observations. Scand J Stat. 1978 ; 5: 141-150
19. Gray RJ. A class of k-sample tests for comparing the cumulative incidence of a competing risk. Ann Stat. 1988 ; 16: 1141-1154
20. Aalen O. Nonparametric estimation of partial transition probabilities in multiple decrement models. Ann Stat. 1978 ; 6: 534-545
21. Lin DY. Non-parametric inference for cumulative incidence functions in competing risks studies. Stat Med. 1997 ; 16: 901-910
22. Mocroft A, Youle M, Moore A, et al. Reasons for modification and discontinuation of antiretrovirals: results from a single treatment centre. AIDS. 2001 ; 25: 185-194
23. O'Brien ME, Clark RA, Besch CL, et al. Patterns and correlates of discontinuation of the initial HAART regimen in an urban outpatient cohort. J Acquir Immune Defic Syndr. 2003 ; 34: 407-414
24. Holt JD. Competing risk analyses with special reference to matched pair experiments. Biometrika. 1978 ; 65: 159-165
25. Andersen PK, Borgan O. Counting process models for life history data: a review. Scand J Stat. 1985 ; 12: 97-158
26. Lunn M, McNeil D. Applying Cox regression to competing risks. Biometrics. 1995 ; 51: 524-532
27. Andersen PK, Hansen LS, Keiding N. Nonparametric and semiparametric estimation of transition-probabilities from censored observation of a nonhomogeneous Markov process. Scand J Stat. 1991 ; 18: 153-167
28. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1999 ; 94: 496-509
29. Robins JM, Rotnitzky A AIDS epidemiology-methodological issues. Jewell N Dietz K Farewell V, ed. Boston: Birkhauser ; 1992: 24-33.
30. Ruan PK, Gray RJ. Analyses of cumulative incidence functions via non-parametric multiple imputation. Stat Med. 2008 ; 27: 5709-5724
31. Taylor JMG, Murray S, Hsu C-H. Survival estimation and testing via multiple imputations. Stat Prob Lett. 2002 ; 58: 221-232
32. Geskus RB. Cause-specific cumulative incidence estimation and the Fine and Gray model under both left truncation and right censoring. Biometrics. 2010 ;:
33. Heckman JJ, Honoré BE. The identifiability of the competing risks model. Biometrika. 1989 ; 76: 325-330
34. Pintilie M. Analysing and interpreting competing risk data. Stat Med. 2007 ; 26: 1360-1367
35. Latouche A, Beyersmann J, Fine JP. Letter to the editor: Comments on 'Analysing and interpreting competing risk data'. Stat Med. 2007 ; 26: 3676-3680
36. Wolbers M, Koller M. Letter to the editor: Comments on 'Analysing and interpreting competing risk data'. Stat Med. 2007 ; 26: 3521-3523
37. Pepe MS. Inference for events with dependent risks in multiple endpoint studies. J Am Stat Assoc. 1991 ; 86: 770-778
38. Geskus R. Censoring strategies when using competing risks with time-dependent covariates. Letter to the editor. J Acquir Immune Defic Syndr. 2007 ; 46: 512-512
39. Latouche A, Boisson V, Porcher R, Chevret S. Misspecified regression model for the subdistribution hazard of a competing risk. Stat Med. 2007 ; 26: 965-974
40. Zhang MJ, Fine J. Summarizing differences in cumulative incidence functions. Stat Med. 2008 ; 27: 4939-4949
41. Cortese G, Anderen PK. Competing risks and time-dependent covariates. Biometrical J. 2009 ; 51: 138-158
42. Beyersmann J, Schumacher M. Time-dependent covariates in the proportional subdistribution hazards model for competing risks. Biostatistics. 2008 ; 9: 765-776
43. Beyersmann J, Dettenkofer M, Bertz H, Schumacher M. A competing risks analysis of bloodstream infection after stem-cell transplantation using subdistribution hazards and cause-specific hazards. Stat Med. 2007 ; 26: 5360-5369
44. Solomon PJ. Effect of misspecification of regression models in the analysis of survival data. Biometrika. 1984 ; 71: 291-298
45. Struthers CA, Kalbfleisch JD. Misspecified proportional hazard models. Biometrika. 1986 ; 73: 363-369
46. Grambauer N, Schumacher M, Beyersmann J. Proportional subdistribution hazards modelling offers a summary analysis, even if misspecified. Stat Med. 2010 ; 29: 875-884
47. Bayersmann J, Latouche A, Buchholz A, Schumacher M. Simulating competing risks data in survival analysis. Stat Med. 2009 ; 28: 956-971
48. Klein JP. Modelling competing risks in cancer studies. Stat Med. 2006 ; 25: 1015-1034
49. Miller RG. What price Kaplan-Meier?. Biometrics. 1983 ; 39: 1077-1081
50. Jeong JH, Fine JP. Parametric regression on cumulative incidence function. Biostatistics. 2007 ; 8: 184-196
51. Lu K, Tsiatis AA. Multiple imputation methods for estimating regression coefficients in the competing risks model with missing cause of failure. Biometrics. 2001 ; 57: 1191-1197
52. Matsuura M, Eguchi S. Modeling late entry bias in survival analysis. Biometrics. 2005 ; 61: 559-566