Censoring issues in survival analysis

Annual Review of Public Health

Volumn 18, Issue , 1997, Pages 83-104

  Leung, Kwan Moon ^a,b   Elashoff, Robert M ^a   Afifi, Abdelmonem A ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b Quality Initiatives Division   (United States)

Author keywords

fight censoring; ignorability; informative censoring; interval censoring; survival analysis

Indexed keywords

CONTINGENCY TABLE; COVARIANCE; HEALTH SURVEY; HUMAN; LIFE EVENT; MAXIMUM LIKELIHOOD METHOD; PRIORITY JOURNAL; PROBABILITY; REGRESSION ANALYSIS; REVIEW; RISK ASSESSMENT; STATISTICAL ANALYSIS; SURVIVAL;

BIAS (EPIDEMIOLOGY); DATA INTERPRETATION, STATISTICAL; HUMANS; LIKELIHOOD FUNCTIONS; PROPORTIONAL HAZARDS MODELS; PUBLIC HEALTH; REPRODUCIBILITY OF RESULTS; SURVIVAL ANALYSIS; TIME FACTORS;

EID: 0030945925     PISSN: 01637525     EISSN: None     Source Type: Book Series    
DOI: 10.1146/annurev.publhealth.18.1.83     Document Type: Review

References (36)

1. Baker SG, Wax Y, Patterson BH. 1993. Regression analysis of grouped survival data: informative censoring and double sampling. Biometrics 49:379-89
2. Brook RJ. 1982. On the loss of information through censoring. Biometrika 69:137-44
3. Cox DR. 1983. A remark on censoring and surrogate response variable. J. R. Statist. Soc. B 45:391-93
4. De Gruttola V, Lagakos SW. 1989. Analysis of doubly-censored survival data, with application to AIDS. Biometrics 45:1-11
5. Dudley J, Jin S, Hoover D, Metz S, Thackeray R, et al. 1995. The multicenter AIDS cohort study: retention after 91/2 years. Am. J. Epidemiol. 142:323-30
6. Ebrahimi N, Soofi ES. 1990. Relative information loss under type II censored exponential data. Biometrika 77:429-35
7. Finkelstein DM, Wolfe RA. 1985. A semiparametric model for regression analysis of interval-censored failure time data. Biometrics 41:933-45
8. Fisher L, Kanarek P. 1974. Presenting censored survival data when censoring and survival times may not be independent. In Reliability and Biometry, ed. F Proschan, RJ Serfling, pp. 303-26. Philadelphia: SIAM
9. Franks P, Clancy CM, Gold MR. 1993. Health insurance and mortality. JAMA 270:737-41
10. Grüger J, Kay R, Schumacher M. 1991. The validity of inferences based on incomplete observations in disease state models. Biometrics 47:595-605
11. Hebert PR, Bolt RJ, Borhani NO, Cook NR, Cohen JW, et al. 1994. Design of a multicenter trial to evaluate long-term life-style intervention in adults with high-normal blood pressure levels: trials of hypertension prevention (Phase II). Ann. Epidemiol. 5:130-39
12. Heitjan DF. 1993. Ignorability and coarse data: some biomedical examples. Biometrics 49:1099-109
13. Heitjan DF, Rubin DB. 1991. Ignorability and coarse data. Ann. Stat. 19:2244-53
14. Hyde J. 1977. Testing survival under right censoring and left truncation. Biometrika 64:225-30
15. Jennrich R. 1983. A note on the behavior of the log rank permutation test under unequal censoring. Biometrika 70:133-37
16. Kalbfleisch JD, McKay RJ. 1979. On constant-sum models for censored survival data. Biometrika 66:87-90
17. Kaplan EL, Meier P. 1958. Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53:457-81
18. Kim MK, De Gruttola VD, Lagakos SW. 1993. Analyzing doubly censored data with covariates, with application to AIDS. Biometrics 49:13-22
19. Klein JP, Moeschberger ML. 1988. Bounds on net survival probabilities for dependent competing risks. Biometrics 44:529-38
20. Kongerud J, Samuelsen SO. 1991. A longitudinal study of respiratory symptoms in aluminum potroom workers. Am. Rev. Respir. Dis. 144:10-16
21. Lagakos SW. 1979. General right censoring and its impact on the analysis of survival data. Biometrics 35:139-56
22. Lagakos SW, Williams JS. 1978. Models for censored survival analysis: a cone class of variable-sum models. Biometrika 65:181-89
23. Law CG, Brookmeyer R. 1992. Effects of mid-point imputation on the analysis of doubly censored data. Stat. Med. 11:1569-78
24. Leung KM, Elashoff RM. 1996. A three-state disease model with interval-censored data: Estimation and applications to AIDS and cancer. Lifetime Data Anal. 2:175-94
25. Little RJA, Rubin DB. 1987. Statistical Analysis with Missing Data. New York: Wiley. 278 pp.
26. Moeschberger ML. 1974. Life tests under competing causes of failure. Technometrics 16:39-47
27. Peterson AV. 1976. Bounds for a joint distribution function with fixed subdistribution functions: application to competing risks. Proc. Natl. Acad. Sci. USA 73:11-13
28. Samuelsen SO, Kongerud J. 1994. Interval censoring in longitudinal data of respiratory symptoms in aluminium potroom workers: a comparison of methods. Stat. Med. 13:1771-80
29. Saunders B, Baily S, Phillips M, Allsop S. 1993. Women with alcohol problems: Do they relapse for reasons different to their male counterparts? Addiction 88:1413-22
30. Slud E, Byar D. 1988. How dependent causes of death can make risk factors appear protective. Biometrics 44:265-69
31. Slud EV, Rubinstein LV. 1983. Dependent competing risks and summary survival curves. Biometrika 70:643-49
32. Tsai WY, Jewell NP, Wang MC. 1987. A note on the product-limit estimator under right censoring and left truncation. Biometrika 74:883-86
33. Tsiatis A. 1975. A nonidentifiability aspect of the problem of competing risks. Proc. Natl. Acad. Sci. USA 72:20-22
34. Turnbull BW. 1976. The empirical distribution function with arbitrarily grouped, censored and truncated data. J. R. Stat. Soc. B 38:290-95
35. Turrero A. 1989. On the relative efficiency of grouped and censored survival data. Biometrika 76:125-31
36. Wax Y, Baker SG, Patterson BH. 1993. A score test for noninformative censoring using doubly sampled grouped survival data. Appl. Stat. 42:159-72