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Misclassification in administrative claims data: quantifying the impact on treatment effect estimates

Michele Jonsson Funk1, Suzanne N Landi1

  • 1Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill NC.

Current Epidemiology Reports
|June 19, 2015
PubMed

Insights

Misclassification bias is common in epidemiologic studies but often unquantified. This review emphasizes quantifying this bias in pharmacoepidemiology, particularly for comparative effectiveness research, using new methods to improve study validity.

Area of Science:

  • Epidemiology
  • Pharmacoepidemiology
  • Biostatistics

Background:

  • Misclassification bias is prevalent in epidemiological research but frequently overlooked in favor of random error assessment.
  • Pharmacoepidemiology often relies on administrative claims data, which are prone to unobservable biases like medication non-adherence and unrecorded health conditions.

Purpose of the Study:

  • To review current knowledge on misclassification bias in epidemiological studies.
  • To highlight the importance and methods for quantifying misclassification bias in pharmacoepidemiology.
  • To discuss misclassification in comparative effectiveness research and introduce methods for bias quantification.

Main Methods:

  • Review of existing literature on misclassification bias.
  • Discussion of bias in administrative claims data and its implications.
  • Demonstration of treatment effect bias in comparative effectiveness research with nondifferential misclassification.
  • Highlighting recently developed statistical methods for quantifying bias.

Main Results:

  • Misclassification bias significantly impacts study findings, often biasing results away from the null.
  • Nondifferential misclassification can lead to biased treatment effect estimates in comparative effectiveness studies.
  • New methods exist to quantify misclassification bias and its impact on results.

Conclusions:

  • Quantifying misclassification bias is crucial for strengthening the validity of pharmacoepidemiologic research.
  • The discussed methods offer potential for more accurate estimation of treatment effects and uncertainty.
  • Addressing misclassification bias is essential for reliable comparative effectiveness research.
Keywords:
Bayesian bias analysisadministrative claimscomparative effectivenessmeasurement errormisclassificationmodified maximum likelihoodmultiple imputation for measurement errornew user designpharmacoepidemiologyprobabilistic bias analysispropensity score calibrationregression calibration

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