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Statistical Analysis of Bivariate Recurrent Event Data with Incomplete Observation Gaps
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 Title & Authors
Statistical Analysis of Bivariate Recurrent Event Data with Incomplete Observation Gaps
Kim, Yang-Jin;
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 Abstract
Subjects can experience two types of recurrent events in a longitudinal study. In addition, there may exist intermittent dropouts that results in repeated observation gaps during which no recurrent events are observed. Therefore, theses periods are regarded as non-risk status. In this paper, we consider a special case where information on the observation gap is incomplete, that is, the termination time of observation gap is not available while the starting time is known. For a statistical inference, incomplete termination time is incorporated in terms of interval-censored data and estimated with two approaches. A shared frailty effect is also employed for the association between two recurrent events. An EM algorithm is applied to recover unknown termination times as well as frailty effect. We apply the suggested method to young drivers` convictions data with several suspensions.
 Keywords
Bivariate recurrent event data;frailty effect;observation gap;piecewise constant;
 Language
English
 Cited by
 References
1.
Cai, J. and Schaubel, D. E. (2004). Marginal means/rates models for multiple type recurrent event types, Lifetime Data Analysis, 10, 121-138. crossref(new window)

2.
Cook, R. J., Lawless, J. F. and Lee, K. A. (2010). A copula-based mixed Poisson model for bivariate recurrent events under event-dependent censoring, Statistics in Medicine, 29, 694-707.

3.
Cook, R., Zeng, L. and Lee, K. (2008). A multistate model for bivariate interval-censored failure time data, Biometrics, 64, 1100-1109. crossref(new window)

4.
Duchateau, L., Jassen, P., Kezic, I. and Fortpied, C. (2003). Evolution of recurrent asthma event rate over time in frailty models, Journal of the Royal Statistical Society, Series C (Applied Statistics), 52, 355-363. crossref(new window)

5.
Finkelstein, D. M. (1986). A proportional hazards model for interval-censored failure time data, Biometrics, 42, 845-854. crossref(new window)

6.
Foucher, Y., Giral, M., Soulillou, J.-F. and Daures, J.-P. (2007). A semi-Markov model for multistate and interval-censored data with multiple terminal events. Application in renal transplantation, Statistics in Medicine, 26, 5381-5393. crossref(new window)

7.
Goetghebeur, E. and Ryan, L. (2000). Semiparametric regression analysis of interval-censored data, Biometrics, 56, 1139-1144. crossref(new window)

8.
Kim, Y. and Jhun, M. (2008). Analysis of recurrent event data with incomplete observation gaps, Statistics in Medicine, 27, 1075-1085. crossref(new window)

9.
Lawless, J. F. and Nadeau, J. C. (1995). Some simple robust methods for the analysis of recurrent events, Technometrics, 37, 158-168. crossref(new window)

10.
Lawless, J. F. and Zhan, M. (1998). Analysis of interval-grouped recurrent event data using piecewise constant rate functions, Canadian Journal of Statistics, 26, 549-565. crossref(new window)

11.
Lindsey, J. and Ryan, L. (1998). Methods for interval censored data. Tutorial in biostatistics, Statistics in Medicine, 17, 219-138. crossref(new window)

12.
Liu, L., Wolfe, R. A. and Huang, X. (2004). Shared frailty models for recurrent events and a terminal event, Biometrics, 60, 747-756. crossref(new window)

13.
Pan, W. (2000). Multiple imputation approach to Cox regression with interval censored data, Biometrics, 56, 199-203. crossref(new window)

14.
Sun, J., Kim, Y., Hewett, J., Johnson, J. C., Farmer, J. and Gibler, M. (2001). Evaluation of traffic injury prevention programs using counting process approaches, Statistics in Medicine, 96, 469-475.

15.
Therneau, T. M. and Hamilton, S. C. (1997). rhDNase as an example of recurrent event analysis, Statistics in Medicine, 16, 2029-2047. crossref(new window)

16.
Turnbull, B. W. (1976). The empirical distribution function with arbitrarily grouped censored and truncated data, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 38, 290-295.

17.
Zhao, Q. and Sun, J. (2006). Semiparametric and nonparametric estimation of recurrent event with observation gaps, Computational Statistics & Data Analysis, 51, 1924-1933. crossref(new window)