A random time interval approach for analysing the impact of a possible intermediate event on a terminal event

Biometrical Journal

Volumn 49, Issue 5, 2007, Pages 742-749

  Beyersmann, Jan ^a  

^a UNIVERSITY OF FREIBURG   (Germany)

Author keywords

Aalen Johansen estimator; Bivariate survival; Hospital infection; Multistate model

Indexed keywords

PROBABILITY DISTRIBUTIONS;

AALEN-JOHANSEN ESTIMATOR; BIVARIATE; BIVARIATE SURVIVAL; HOSPITAL INFECTION; INTERMEDIATE STATE; JOINT DISTRIBUTIONS; MATRIX OF TRANSITION PROBABILITIES; MULTI-STATE MODEL; TIME INTERVAL; TIME-SPENT;

HOSPITALS;

EID: 35448965241     PISSN: 03233847     EISSN: 15214036     Source Type: Journal    
DOI: 10.1002/bimj.200610342     Document Type: Article

References (34)

1. Aalen, O., Borgan, Ø., and Fekjær, H. (2001). Covariate adjustment of event histories estimated from Markov chains: the additive approach. Biometrics 57, 993-1001.
2. Aalen, O. and Johansen, S. (1978). An empirical transition matrix for non-homogeneous Markov chains based on censored observations. Scandinavian Journal of Statistics 5, 141-150.
3. Abbring, J. and van den Berg, G. (2003). The nonparametric identification of treatment effects in duration models. Econometrica 71(5), 1491-1517.
4. Andersen, P., Borgan, Ø., Gill, R. D., and Keiding, N. (1993). Statistical models based on counting processes. Springer Series in Statistics. New York, NY: Springer.
5. Andersen, P. and Keiding, N. (2002). Multi-state models for event history analysis. Statistical Methods in Medical Research 11(2), 91-115.
6. Beyersmann, J. (2005). On change in length of stay associated with an intermediate event: estimation within multi-state models and large sample properties. Ph.D. thesis, University Freiburg, Germany. www.freidok.uni- freiburg.de/volltexte/1843/.
7. Beyersmann, J., Gastmeier, P., Grundmann, H., Bärwolff, S., Geffers, C., Behnke, M., Rüden, H., and Schumacher, M. (2006). Use of multistate models to assess prolongation of intensive care unit stay due to nosocomial infection. Infection Control and Hospital Epidemiology 27, 493-499.
8. Datta, S. and Satten, G. A. (2001). Validity of the Aalen-Johansen estimators of stage occupation probabilities and Nelson-Aalen estimators of integrated transition hazards for non-Markov models. Statistics and Probability Letters 55(4), 403-411.
9. Fine, J., Jiang, H., and Chappell, R. (2001). On semi-competing risks data. Biometrika 88(4), 907-919.
10. Gill, R. D. and Johansen, S. (1990). A survey of product-integration with a view towards application in survival analysis. The Annals of Statistics 18(4), 1501-1555.
11. Glidden, D. (2002). Robust inference for event probabilities with non-Markov data. Biometrics 58, 361-368.
12. Grundmann, H., Bärwolff, S., Schwab, F., Tami, A., Behnke, M., Geffers, C., Halle, E., Göbel, U., Schiller, R., Jonas, D., Klare, I., Weist, K., Witte, W., Beck-Beilecke, K., Schumacher, M., Rüden, H., and Gastmeier, P. (2005). How many infections are caused by patient-to-patient transmission in intensive care units? Critical Care Medicine 33, 946-951.
13. Hougaard, P. (2000). Analysis of multivariate survival data. Statistics for Biology and Health. New York, NY: Springer.
14. Irala-Estévez, J., Martínez-Concha, D., Díaz-Molina, C., Masa-Calles, J., del Castillo, A. S., and Navajas, R. F.-C. (2001). Comparison of different methodological approaches to identify risk factors of nosocomial infection in intensive care units. Intensive Care Medicine 27(8), 1254-1262.
15. Jiang, H., Chappell, R., and Fine, J. P. (2003). Estimating the distribution of nonterminal event time in the presence of mortality or informative dropout. Controlled Clinical Trials 24(2), 135-146.
16. Jiang, H., Fine, J. P., and Chappell, R. (2005). Semiparametric analysis of survival data with left truncation and dependent right censoring. Biometrics 61(2), 567-575.
17. Jiang, H., Fine, J. P., Kosorok, M. R., and Chappell, R. (2005). Pseudo self-consistent estimation of a copula model with informative censoring. Scandinavian Journal of Statistics 32(1), 1-20.
18. Kaplan, E. and Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53, 457-481.
19. Kosorok, M. R., Fine, J. P., Jiang, H., and Chappell, R. (2002). Asymptotic theory for the Gamma frailty model with dependent censoring. Annals of the Institute of Statistical Mathematics 54(3), 476-499.
20. Latouche, A., Porcher, R., and Chevret, S. (2005). A note on including time-dependent covariate in regression model for competing risks data. Biometrical Journal 47(6), 807-814.
21. Lin, D. and Ying, Z. (1993). A simple nonparametric estimator of the bivariate survival function under univariate censoring. Biometrika 80 (3), 573-581.
22. Meira-Machado, L., de Uña-Álvarez, J., and Cadarso-Suárez, C. (2006). Nonparametric estimation of transition probabilities in a non-markov illness-death model. Lifetime Data Analysis 12(3), 325-344.
23. Neuhaus, G. (1971). On weak convergence of stochastic processes with multidimensional time parameter. Annals of Mathematical Statistics 42, 1285-1295.
24. Peng, L. and Fine, J. P. (2006). Rank estimation of accelerated lifetime models with dependent censoring. Journal of the American Statistical Association 101(475), 1085-1093.
25. Prentice, R., Moodie, Z., and Wu, J. (2004). Nonparametric estimation of the bivariate survivor function. In: D. Lin and P. Heagerty (Eds.), Proceedings of the second Seattle Symposium in Biostatistics, Volume 179 of Lecture notes in statistics, pp. 113-142. New York, NY: Springer.
26. R Development Core Team (2004). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-900051-00-3.
27. Robins, J., Blevins, D., Ritter, G., and Wulfson, M. (1992). G-estimation of the effect of prophylaxis therapy for pneumocystic carinii pneumonia on the survival of AIDS patients. Epidemiology 3, 319-336.
28. Robins, J. M. (1997). Causal inference from complex longitudinal data. M. Berkane (ed.), Latent variable modeling and applications to causality. Proceedings of the 1994 UCLA conference. New York, NY: Springer. Lect. Notes Stat. 120, 69-117.
29. Schulgen, G., Kropec, A., Kappstein, I., Daschner, F., and Schumacher, M. (2000). Estimation of extra hospital stay attributable to nosocomial infections: heterogeneity and timing of events. Journal of Clinical Epidemiology 53, 409-417.
30. Schulgen, G. and Schumacher, M. (1996). Estimation of prolongation of hospital stay attributable to nosocomial infections. Lifetime Data Analysis 2, 219-240.
31. Tsai, W.-Y. and Crowley, J. (1998). A note on nonparametric estimators of the bivariate survival function under univariate censoring. Biometrika 85(3), 573-580.
32. Utikal, K. J., Parner, J. T., and Keiding, N. (2003). A marginal approach to assessing the effect of an intermediate event. Technical report, University of Kopenhagen, Institute of Public Health, Department of Biostatistics.
33. van der Vaart, A. and Wellner, J. A. (1996). Weak convergence and empirical processes. With applications to statistics. Springer Series in Statistics. New York, NY: Springer.
34. Wangler, M., Beyersmann, J., and Schumacher, M. (2006). changeLOS: An R-package for change in length of hospital stay based on the Aalen-Johansen estimator. R News 6(2), 31-35. http://cran.r-project.org/doc/ Rnews/Rnews_2006-2.pdf.