A sensitivity analysis to separate bias due to confounding from bias due to predicting misclassification by a variable that does both

Epidemiology. 2000 Sep;11(5):544-9. doi: 10.1097/00001648-200009000-00010.

Abstract

Variables that predict misclassification of exposure, outcome, or a confounder cannot be controlled by techniques that adjust for predictors of risk. They must be controlled by external adjustments. We confronted an analysis in which a variable predicted misclassification of the exposure and of a confounder. The same variable confounded the exposure-outcome relation. The analysis focused on the relation between less-than-definitive therapy and breast cancer mortality in the 5 years after diagnosis. Receipt of less-than-definitive prognostic evaluation predicted misclassification of definitive therapy (the exposure) and stage (a confounder). Prognostic evaluation also confounded the therapy-breast cancer mortality relation. We used a sensitivity analysis to separate the misclassification biases from the confounding bias. The relative hazard associated with less-than-definitive therapy in the original multivariable model equaled 1.75 (95% confidence interval = 1.02-3.00). The median estimate in 2,500 repetitions of the sensitivity analysis was a relative hazard of 1.64, and 90% of the estimates fell between 1.47 and 1.83. The sensitivity analysis suggests that less-than-definitive therapy confers an excess relative hazard of breast cancer mortality in the 5 years after diagnosis. The original analysis, which adjusted for confounding by prognostic evaluation but not its misclassification biases, overestimated the relative hazard.

MeSH terms

  • Age Factors
  • Aged
  • Aged, 80 and over
  • Bias*
  • Breast Neoplasms / mortality*
  • Breast Neoplasms / pathology
  • Breast Neoplasms / therapy
  • Classification
  • Confounding Factors, Epidemiologic*
  • Epidemiologic Methods*
  • Female
  • Humans
  • Middle Aged
  • Prognosis
  • Proportional Hazards Models
  • Rhode Island / epidemiology
  • Sensitivity and Specificity
  • Survival Analysis