Inference for Shared-Frailty Survival Models with Left-Truncated Data
AbstractShared-frailty survival models specify that systematic unobserved determinants of duration outcomes are identical within groups of individuals. We consider random-effects likelihood-based statistical inference if the duration data are subject to left-truncation. Such inference with left-truncated data can be performed in the Stata software package. We show that with left-truncated data, the commands ignore the weeding-out process before the left-truncation points, affecting the distribution of unobserved determinants among group members in the data, that is, among the group members who survive until their truncation points. We critically examine studies in the statistical literature on this issue as well as published empirical studies that use the commands. Simulations illustrate the size of the (asymptotic) bias and its dependence on the degree of truncation. We provide a Stata command file that maximizes the likelihood function that properly takes account of the interplay between truncation and dynamic selection.
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Bibliographic InfoPaper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 6031.
Length: 25 pages
Date of creation: Oct 2011
Date of revision:
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Other versions of this item:
- van den Berg, Gerard J. & Drepper , Bettina, 2011. "Inference for shared-frailty survival models with left-truncated data," Working Paper Series 2011:26, IFAU - Institute for Evaluation of Labour Market and Education Policy.
- C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
- C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
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- Mario Cleves & William W. Gould & Roberto G. Gutierrez & Yulia Marchenko, 2010. "An Introduction to Survival Analysis Using Stata," Stata Press books, StataCorp LP, edition 3, number saus3, March.
- Ridder, Geert & Tunali, Insan, 1999.
"Stratified partial likelihood estimation,"
Journal of Econometrics,
Elsevier, vol. 92(2), pages 193-232, October.
- Jaap H. Abbring & Gerard J. Van Den Berg, 2007.
"The unobserved heterogeneity distribution in duration analysis,"
Biometrika Trust, vol. 94(1), pages 87-99.
- Jaap H. Abbring & Gerard J. van den Berg, 2006. "The Unobserved Heterogeneity Distribution in Duration Analysis," Tinbergen Institute Discussion Papers 06-059/3, Tinbergen Institute.
- Abbring, Jaap H & van den Berg, Gerard J, 2007. "The Unobserved Heterogeneity Distribution in Duration Analysis," CEPR Discussion Papers 6219, C.E.P.R. Discussion Papers.
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