Nonidentifiability in the presence of factorization for truncated data

B. Vakulenko-Lagun, J. Qian, S. H. Chiou, R. A. Betensky

Research output: Contribution to journalArticlepeer-review

Abstract

A time to event, X , is left-truncated by T if X can be observed only if T < X . This often results in oversampling of large values of X , and necessitates adjustment of estimation procedures to avoid bias. Simple risk-set adjustments can be made to standard risk-set-based estimators to accommodate left truncation when T and X are quasi-independent. We derive a weaker factorization condition for the conditional distribution of T given X in the observable region that permits risk-set adjustment for estimation of the distribution of X , but not of the distribution of T. Quasi-independence results when the analogous factorization condition for X given T holds also, in which case the distributions of X and T are easily estimated. While we can test for factorization, if the test does not reject, we cannot identify which factorization condition holds, or whether quasi-independence holds. Hence we require an unverifiable assumption in order to estimate the distribution of X or T based on truncated data. This contrasts with the common understanding that truncation is different from censoring in requiring no unverifiable assumptions for estimation.We illustrate these concepts through a simulation of left-truncated and right-censored data.

Original languageEnglish (US)
Pages (from-to)724-731
Number of pages8
JournalBiometrika
Volume106
Issue number3
DOIs
StatePublished - Sep 1 2019

Keywords

  • Constant-sum condition
  • Kendall's tau
  • Left truncation
  • Right censoring
  • Survival data

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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