Analysis of interval-censored disease progression data via multi-state models under a nonignorable inspection process

Statistics in Medicine

Volumn 29, Issue 11, 2010, Pages 1175-1189

  Chen, Baojiang ^a   Yi, Grace Y ^b   Cook, Richard J ^b  

^a UNIVERSITY OF WASHINGTON   (United States)

^b UNIVERSITY OF WATERLOO   (Canada)

Author keywords

EM algorithm; Longitudinal data; Maximum likelihood; Progressive models

Indexed keywords

ALGORITHM; ARTICLE; CLINICAL ARTICLE; DISEASE COURSE; FEMALE; HUMAN; MALE; MAXIMUM LIKELIHOOD METHOD; PSORIATIC ARTHRITIS; SIMULATION; STATISTICAL MODEL; STATISTICAL PARAMETERS;

ALGORITHMS; ARTHRITIS, PSORIATIC; COHORT STUDIES; COMPUTER SIMULATION; DISEASE PROGRESSION; HUMANS; LONGITUDINAL STUDIES; MODELS, IMMUNOLOGICAL; MODELS, STATISTICAL;

EID: 77951612162     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3804     Document Type: Article

References (43)

1. Bartholomew DJ. Some recent developments in social statistics. International Statistical Review 1983; 51:1-9.
2. Singer B, Spilerman S. The representation of social processes by Markov models. American Journal of Sociology 1976; 82:1-54.
3. Singer B, Spilerman S. Some methodological issues in the analysis of longitudinal surveys. Annals of Economic and Sociological Measurement 1976; 5:447-474.
4. Wasserman S. Analyzing social networks as stochastic processes. Journal of the American Statistical Association 1980; 75:280-294.
5. Albert PS, Waclawiw MA. A two-state Markov chain for heterogeneous transitional data: a quasi-likelihood approach. Statistics in Medicine 1998; 17:1481-1493.
6. Andersen PK, Borgan O, Gill RD, Keiding N. Statistical Models Based on Counting Processes. Springer: New York, NY, 1993.
7. Kalbfleisch JD, Lawless JF. The analysis of panel data under a Markov assumption. Journal of the American Statistical Association 1985; 80:863-871.
8. Kalbfleish JD, Lawless JF. Some statistical methods for panel life history data. Proceedings of the Statistics Canada Symposium on Analysis of Data in Time. Statistics Canada: Ottawa, Ont., 1989; 185-192.
9. Gentleman RC, Lawless JF, Lindsey JC, Yan P. Multi-state Markov models for analysing incomplete disease history data with illustrations for HIV diease. Statistics in Medicine 1994; 13:805-821. (Pubitemid 24136154)
10. Satten GA. Estimating the extent of tracking in interval-censored chain-of-events data. Biometrics 1999; 55:1228-1231.
11. Cook RJ, Yi GY, Lee KA, Gladman DD. A conditional Markov model for clustered progressive multistate processes under incomplete observation. Biometrics 2004; 60:436-443.
12. Cook RJ, Kalbfleisch JD, Yi GY. A generalized mover-stayer model for panel data. Biostatistics 2002; 3:407-420. (Pubitemid 35015932)
13. Grüger J, Kay R, Schumacher M. The validity of inferences based on incomplete observations in disease state models. Biometrics 1991; 47:595-605.
14. Sun J, Wei LJ. Regression analysis of panel count data with covariate-dependent observation and censoring times. Journal of the Royal Statistical Society, Series B 2000; 62:293-302.
15. Sun J, Tong X, He X. Regression analysis of panel count data with dependent observation times. Biometrics 2007; 63:1053-1059. (Pubitemid 350212572)
16. Betensky RA, Finkelstein DM. Testing for dependence between failure time and visit compliance with interval-censored data. Biometrics 2002; 58:58-63.
17. Dempster AP, Laird NM, Rubins DB. Maximum likelihood from incomplete data via the EM algorithm (with Discussion). Journal of the Royal Statistical Society, Series B 1977; 39:1-38.
18. Louis T. Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society, Series B 1982; 44:226-233.
19. Gladman DD, Farewell VT, Nadeau C. Clinical indicators of progression in psoriatic arthritis (PSA): multivariate relative risk model. Journal of Rheumatology 1995; 22:675-679.
20. Cox DR, Miller HD. The Theory of Stochastic Processes. Chapman & Hall: London, 1977.
21. He W, Lawless JF. Flexible maximum likelihood methods for bivariate proportional hazards models. Biometrics 2003; 59:837-848. (Pubitemid 38021212)
22. Lawless JF, Zhan M. Analysis of interval-grouped recurrent-event data using piecewise-constant rate functions. Canadian Journal of Statistics 1998; 26:549-565. (Pubitemid 128187227)
23. Lindsey JC, Ryan LM. A three-state multiplicative model for rodent tumorigenicity experiments. Applied Statistics 1993; 42:283-300.
24. Diggle PJ, Heagerty PJ, Liang K-Y, Zeger SL. Analysis of Longitudinal Data. Oxford University Press: Oxford, 2002.
25. White H. Maximum likelihood estimation under misspecified models. Econometrica 1982; 50:1-26.
26. Laird NM. Missing data in longitudinal studies. Statistics in Medicine 1988; 7:305-315.
27. Little RJA, Rubin DB. Statistical Analysis with Missing Data (1st edn). Wiley: New York, 1987.
28. Ibrahim JG, Chen MH, Lipsitz SR, Herring AH. Missing-data methods for generalized linear models: a comparative review. Journal of the American Statistical Association 2005; 100:332-346. (Pubitemid 40366842)
29. Zhu HT, Lee SY. Local inference for incomplete data models. Journal of the Royal Statistical Society, Series B 2001; 63:111-126.
30. Verbeke G, Molenberghs G, Thijs H, Lasaffre E, Kenward MG. Sensitivity analysis for non-random dropout: a local influence approach. Biometrics 2001; 57:43-50.
31. Van Steen K, Molenberghs G, Thijs H. A local influence approach to sensitivity analysis of incomplete longitudinal original data. Statistical Modelling: An International Journal 2001; 1:125-142.
32. Molenberghs G, Kenward MG, Goetghebeur E. Sensitivity analysis for incomplete contingency tables. Applied Statistics 2001; 50:15-29.
33. Kenward MG. Selection models for repeated measurements with nonrandom dropout: an illustration of sensitivity. Statistics in Medicine 1998; 17:2723-2732.
34. Jansen I, Molenberghs G, Aerts M, Thjis H, Van Steen K. A local influence approach to binary data from a psychiatric study. Biometrics 2003; 59:410-419. (Pubitemid 36750355)
35. Gladman DD, Farewell VT, Kopciuk KA, Cook RJ. HLA markers and progression in psoriatic arthritis. Journal of Rheumatology 1998; 25:730-733.
36. Chen B, Yi GY, Cook RJ. Analysis of interval-censored disease progression data via multi-state models under a nonignorable inspection process. Technical Report of University of Waterloo, 2008-01, 2008.
37. Goggins WB, Finkelstein DM, Zaslavsky AM. Applying the Cox proportional hazards model when the change time of a binary time-varying covariate is interval-censored. Biometrics 1999; 55:445-451.
38. Chen EB, Cook RJ. Regression modeling with recurrent events and time-dependent interval-censored marker data. Lifetime Data Analysis 2003; 9:275-291.
39. Cook RJ, Zeng L, Lee KA. A multistate model for bivariate interval-censored failure time data. Biometrics 2008; 64:1100-1109.
40. Staniswalis JG, Thall PF, Salch J. Semiparametric regression analysis for recurrent event interval counts. Biometrics 1997; 53:1334-1353. (Pubitemid 28013576)
41. Loader CR. Local likelihood density estimation. The Annals of Statistics 1996; 24:1602-1618.
42. Loader CR. Local Regression and Likelihood. Springer: New York, NY, 1999.
43. Turnbull BW. The empirical distribution function with arbitrarily grouped, censored and truncated data. Journal of the Royal Statistical Society, Series B 1976; 38:290-295.