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Epidemiologic research using probabilistic outcome definitions.

Bing Cai1, Sean Hennessy, Vincent Lo Re

  • 1Epidemiology, Pfizer Inc., Collegeville, PA, USA; Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Center for Pharmacoepidemiology Research and Training (CPeRT), University of Pennsylvania, Philadelphia, PA, USA.

Pharmacoepidemiology and Drug Safety
|September 27, 2014
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Summary
This summary is machine-generated.

Probabilistic outcome definition (POD) offers unbiased risk ratio estimates in electronic health record research, overcoming bias from imperfect outcome algorithms. This method improves accuracy, even with low sensitivity or positive predictive values.

Keywords:
biasdatabase researchepidemiological methodsmultiple imputationoutcome validationpharmacoepidemiologyprobabilistic outcome definition

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Area of Science:

  • Epidemiology
  • Health Informatics
  • Biostatistics

Background:

  • Electronic healthcare data research often uses algorithms with imperfect accuracy (specificity, sensitivity, positive predictive value) to define clinical outcomes.
  • This imperfect outcome definition leads to misclassification and introduces bias into epidemiologic study results.

Purpose of the Study:

  • To introduce and evaluate a novel method, probabilistic outcome definition (POD), for defining clinical outcomes in large electronic healthcare datasets.
  • To assess the performance of POD in providing unbiased estimates of epidemiologic parameters, specifically the risk ratio, compared to conventional methods.

Main Methods:

  • POD utilizes logistic regression to estimate the probability of a clinical outcome based on multiple potential algorithms.
  • Multiple imputation techniques are employed within the POD framework to enable valid statistical inferences.
  • A simulation study was conducted to evaluate POD's performance against a conventional binary outcome definition, using predictive variables.

Main Results:

  • The conventional method yields unbiased estimates only when the true risk ratio is 1.0 (null effect).
  • When the risk ratio deviates from 1.0, the conventional method demonstrates significant bias, particularly with poor sensitivity or positive predictive value (<0.75).
  • The POD method consistently provides unbiased risk ratio estimates, irrespective of whether the true risk ratio is 1.0 or not, and remains accurate even with low sensitivity and positive predictive values.

Conclusions:

  • Probabilistic outcome definition (POD) represents an advancement in defining clinical outcomes for database research.
  • POD offers a significant advantage over conventional methods by providing unbiased risk ratio estimates.
  • The POD method is user-friendly and enhances the reliability of findings from electronic health record studies.