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A Bayesian method for estimating the accuracy of recalled depression

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  • Carolyn M. Rutter
  • Gregory Simon

Abstract

Summary. We develop a Bayesian method that allows us to compare weekly depression states recalled for a 3‐month period to cross‐sectionally assessed measurements of current depression assessed during randomly timed phone interviews. Using these data, we examine the accuracy of recalled depression by linking a spline model for recalled depression and a logistic model for current depression. The logistic model includes the model‐based probability of depression based on recall as a covariate and covariates potentially related to the accuracy of recall. The model that we propose allows variability in both measures and can be modified to examine general relationships between longitudinal and cross‐sectional measurements.

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  • Carolyn M. Rutter & Gregory Simon, 2004. "A Bayesian method for estimating the accuracy of recalled depression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(2), pages 341-353, April.
    • Handle: RePEc:bla:jorssc:v:53:y:2004:i:2:p:341-353
    DOI: 10.1046/j.1467-9876.2003.05211.x
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