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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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A simulation study of diagnostics for selection bias.

Philip S Boonstra1, Roderick J A Little1,2, Brady T West2

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, MI.

Journal of Official Statistics
|September 27, 2021
PubMed
Summary
This summary is machine-generated.

Non-probability sampling can bias study results. New bias diagnostics, standardized measure of unadjusted bias (SMUB) and standardized measure of adjusted bias (SMAB), better predict true selection bias, even with incorrect assumptions.

Keywords:
Multiple ImputationNon-Ignorable Selection BiasPattern Mixture ModelSurvey Non-Response

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Area of Science:

  • Statistics
  • Biostatistics
  • Survey Methodology

Background:

  • Non-probability sampling methods, including non-response and non-selection, can introduce significant bias into parameter estimates.
  • Selection bias becomes particularly problematic and undetectable when it is 'non-ignorable,' meaning it depends on unobserved outcomes of interest.

Purpose of the Study:

  • To evaluate the effectiveness of newly introduced bias quantification statistics in assessing selection bias.
  • To compare the performance of standardized measure of unadjusted bias (SMUB) and standardized measure of adjusted bias (SMAB) against existing diagnostics.

Main Methods:

  • The study extends a prior simulation by Nishimura et al. (2016).
  • Incorporates two new statistics: SMUB and SMAB, which quantify bias under assumed levels of non-ignorable selection.
  • Assesses the correlation and predictive power of these new diagnostics.

Main Results:

  • The new sensitivity diagnostics (SMUB and SMAB) demonstrate a stronger correlation with the true extent of selection bias.
  • These diagnostics are more predictive of actual selection bias compared to other available methods.
  • The enhanced predictive power holds even when the assumed level of non-ignorability in the simulation is not perfectly accurate.

Conclusions:

  • SMUB and SMAB offer improved tools for detecting and quantifying selection bias in statistical estimates.
  • These diagnostics provide valuable sensitivity analysis for non-ignorable selection mechanisms.
  • Researchers can utilize these measures to better understand the potential impact of bias in their findings.