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Probability Laws01:49

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Overview
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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Bias in Epidemiological Studies01:29

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Confounding in Epidemiological Studies01:27

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Related Experiment Video

Updated: Jun 25, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Bayesian methods for correcting misclassification: an example from birth defects epidemiology.

Richard F MacLehose1, Andrew F Olshan, Amy H Herring

  • 1Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA. mac10029@umn.edu

Epidemiology (Cambridge, Mass.)
|February 24, 2009
PubMed
Summary
This summary is machine-generated.

Maternal smoking during pregnancy increases the risk of cleft lip with or without cleft palate (CL/P). This study corrected for reporting errors, confirming a link between smoking and CL/P, but found no clear association with cleft palate only (CPO).

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Last Updated: Jun 25, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Epidemiology
  • Congenital Malformations
  • Reproductive Health

Background:

  • Orofacial clefts, including cleft lip with or without cleft palate (CL/P) and cleft palate only (CPO), are common birth defects.
  • Epidemiologic studies link maternal smoking in early pregnancy to increased cleft risk, but concerns exist regarding data accuracy due to exposure misclassification.

Purpose of the Study:

  • To address potential bias in previous studies by correcting for misclassification of self-reported maternal smoking.
  • To provide more accurate estimates of the association between maternal smoking and the risk of CL/P and CPO.

Main Methods:

  • Employed four Bayesian models with varying assumptions on the sensitivity and specificity of self-reported smoking data.
  • Utilized prior distributions from existing reliability studies and Markov chain Monte Carlo algorithms for posterior distribution calculations.
  • Applied the methodology to data from the National Birth Defects Prevention Study.

Main Results:

  • Corrected analyses revealed a statistically significant increased risk of CL/P associated with maternal smoking during early pregnancy (posterior OR = 1.6, 95% CI = 1.1-2.2).
  • The association between maternal smoking and CPO was less conclusive, with no strong evidence of an increased risk (posterior OR = 1.1, 95% CI = 0.7-1.7).

Conclusions:

  • Findings support the hypothesis that periconceptional maternal smoking elevates the risk for CL/P.
  • The study found no significant evidence for an association between maternal smoking and CPO, though estimates were imprecise.
  • Recommended future research focus on validity studies of reporting rather than further observational analyses.