Use of the positive predictive value to correct for disease misclassification in epidemiologic studies

Am J Epidemiol. 1993 Dec 1;138(11):1007-15. doi: 10.1093/oxfordjournals.aje.a116805.


Misclassification problems of the disease status often arise in large epidemiologic cohort studies in which the outcome is classified on the basis of record linkage with routinely collected error-prone data sources, such as cancer registries or mortality statistics. If the misclassification is nondifferential, i.e., independent of the exposure status, this leads to bias toward the null in estimates of relative risk. A variety of methods have been proposed to correct for this bias. Most approaches are based on estimates of the sensitivity and specificity of disease classification from validation studies, which typically require invasive and time-consuming diagnostic procedures. For ethical and practical reasons, such procedures may often not be applied on individuals classified as not having the disease, in which case estimates of sensitivity and specificity cannot be obtained. In this paper, an alternative correction method is proposed based on estimates of the positive predictive value, which requires validation of the diagnosis among samples of individuals classified as having the disease only. The method is applicable in situations with either differential or nondifferential specificity of disease classification as long as the sensitivity is nondifferential. Point estimates and large-sample interval estimates of the corrected relative risk are algebraically derived. The performance of the method is assessed by extensive simulations and found to be satisfactory even for small sample sizes.

MeSH terms

  • Bias
  • Cohort Studies*
  • Confidence Intervals
  • Databases, Factual
  • Disease / classification*
  • Epidemiologic Methods*
  • Humans
  • Incidence
  • Mathematics
  • Medical Record Linkage*
  • Odds Ratio
  • Predictive Value of Tests*
  • Prognosis
  • Reproducibility of Results
  • Sampling Studies
  • Sensitivity and Specificity