title: Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches.
abstract: BACKGROUND
  Logistic regression is a statistical model widely used in cross-sectional and cohort studies to identify and quantify the effects of potential disease risk factors. However, the impact of imperfect tests on adjusted odds ratios (and thus on the identification of risk factors) is under-appreciated. The purpose of this article is to draw attention to the problem associated with modelling imperfect diagnostic tests, and propose simple Bayesian models to adequately address this issue.
  
  
  METHODS
  A systematic literature review was conducted to determine the proportion of malaria studies that appropriately accounted for false-negatives/false-positives in a logistic regression setting. Inference from the standard logistic regression was also compared with that from three proposed Bayesian models using simulations and malaria data from the western Brazilian Amazon.
  
  
  RESULTS
  A systematic literature review suggests that malaria epidemiologists are largely unaware of the problem of using logistic regression to model imperfect diagnostic test results. Simulation results reveal that statistical inference can be substantially improved when using the proposed Bayesian models versus the standard logistic regression. Finally, analysis of original malaria data with one of the proposed Bayesian models reveals that microscopy sensitivity is strongly influenced by how long people have lived in the study region, and an important risk factor (i.e., participation in forest extractivism) is identified that would have been missed by standard logistic regression.
  
  
  CONCLUSIONS
  Given the numerous diagnostic methods employed by malaria researchers and the ubiquitous use of logistic regression to model the results of these diagnostic tests, this paper provides critical guidelines to improve data analysis practice in the presence of misclassification error. Easy-to-use code that can be readily adapted to WinBUGS is provided, enabling straightforward implementation of the proposed Bayesian models.
authors.affiliation: School of Forest Resources and Conservation, University of Florida, Gainesville, USA. drvalle@ufl.edu.
authors.forename: Denis
authors.initials: D
authors.lastname: Valle
authors.affiliation: School of Forest Resources and Conservation, University of Florida, Gainesville, USA. jmtucker@ufl.edu.
authors.forename: Joanna M Tucker
authors.initials: JM
authors.lastname: Lima
authors.affiliation: School of Forest Resources and Conservation, University of Florida, Gainesville, USA. justinjmillar@gmail.com.
authors.forename: Justin
authors.initials: J
authors.lastname: Millar
authors.affiliation: School of Forest Resources and Conservation, University of Florida, Gainesville, USA. punam.amratia@ufl.edu.
authors.forename: Punam
authors.initials: P
authors.lastname: Amratia
authors.affiliation: Emerging Pathogens Institute, University of Florida, Gainesville, USA. ubydulhaque@ufl.edu.|Geography Department, University of Florida, Gainesville, USA. ubydulhaque@ufl.edu.
authors.forename: Ubydul
authors.initials: U
authors.lastname: Haque
mesh_terms: D001938:Brazil / Q000453:epidemiology; D003951:Diagnostic Errors; D003955:Diagnostic Tests, Routine / Q000379:methods*; D006801:Humans; D008288:Malaria / Q000175:diagnosis* / Q000453:epidemiology*; D013223:Statistics as Topic