Bayesian adjustment of anticipatory covariates in analyzing retrospective data

Gebrenegus Ghilagaber, Johan Koskinen

Research output: Contribution to journalArticlepeer-review

83 Downloads (Pure)


In retrospective surveys, records on important variables such as the respondent's educational level and social class refer to what is achieved by the date of the survey. Such variables are then used as covariates in investigations of behavior such as marriage and divorce in life segments that have occurred before the survey. To what extent can any change in the behavior be attributed to the misclassification of respondents across the various levels of the anticipatory variable? To what extent do they reflect real differences in the behavior across the levels? The connection is obtained by a Bayesian adjustment, by specifying a continuous-time Markov model for the incompletely observed time-varying anticipatory covariates, and by implementing standard Bayesian data augmentation techniques. The issues are illustrated by estimating effects of educational level on risks of divorce in a multiplicative piecewise-constant hazard model. Results show that ignoring the time-inconsistency of anticipatory variables may seriously plague the analyses because the relative risks across the anticipatory educational level are overestimated. © Taylor & Francis Group, LLC.
Original languageEnglish
Pages (from-to)105-130
Number of pages25
JournalMathematical Population Studies
Issue number2
Publication statusPublished - Apr 2009


  • Anticipatory analysis
  • Bayesian analysis
  • Divorce
  • Education
  • Event-history analysis
  • MCMC
  • Retrospective surveys


Dive into the research topics of 'Bayesian adjustment of anticipatory covariates in analyzing retrospective data'. Together they form a unique fingerprint.

Cite this