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 University of Mannheim, Department of Economics in its series Working Papers with number 12-05.
Date of creation: 2012
Date of revision:
Stata ; duration analysis ; hazard rate ; unobserved heterogeneity ; left-truncation ; likelihood function ; dynamic selection ; twin data;
Find related papers by JEL classification:
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-04-23 (All new papers)
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