Inference for shared-frailty survival models with left-truncated data

G.J. van den Berg, B. Drepper

Research output: Contribution to journalArticleScientificpeer-review

14 Citations (Scopus)

Abstract

Shared-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 for parametric and semi-parametric shared frailty models. 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, namely 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 for the parametric case that maximizes the likelihood function that properly takes account of the interplay between truncation and dynamic selection.
Original languageEnglish
Pages (from-to)1075-1098
JournalEconometric Reviews
Volume35
Issue number6
DOIs
Publication statusPublished - 2016

Keywords

  • duration analysis
  • dynamic selection
  • hazard rate
  • left-truncation
  • likelihood function
  • stata
  • twin data
  • unobserved heterogeneity

Fingerprint Dive into the research topics of 'Inference for shared-frailty survival models with left-truncated data'. Together they form a unique fingerprint.

  • Cite this