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Accounting for misclassified outcomes in binary regression models using multiple imputation with internal validation

Jessie K Edwards, Stephen R Cole, Melissa A Troester

    American Journal of Epidemiology
    |March 15, 2014
    PubMed
    Summary
    This summary is machine-generated.

    Multiple imputation effectively reduces bias from outcome misclassification in epidemiological studies when validation data are available. This statistical method offers flexibility and ease of implementation for researchers.

    Keywords:
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    Area of Science:

    • Epidemiology
    • Biostatistics
    • Clinical Research

    Background:

    • Outcome misclassification is a common issue in epidemiological research, potentially leading to biased results.
    • Existing methods to address outcome misclassification are often underutilized in practice.
    • Validation data from a subgroup of participants can be leveraged to correct for misclassification bias.

    Purpose of the Study:

    • To describe and illustrate the application of multiple imputation for handling outcome misclassification in epidemiological studies.
    • To compare the performance of multiple imputation with other methods, including naive analysis and analysis restricted to a validation subgroup.
    • To assess the statistical power and bias of different approaches in the presence of outcome misclassification.

    Main Methods:

    • Utilized data from the multicenter Herpetic Eye Disease Study (308 participants, 1992-1998).
    • Employed multiple imputation to account for outcome misclassification, comparing it with naive analysis, validation subgroup analysis, and direct maximum likelihood.
    • Extended the multiple imputation approach to estimate risk ratios using log-binomial regression.

    Main Results:

    • Multiple imputation yielded an odds ratio of 0.60 (95% CI: 0.24, 1.51), comparable to direct maximum likelihood (OR=0.62) and validation subgroup analysis (OR=0.57).
    • Naive analysis using self-reported outcomes resulted in a biased odds ratio of 0.90 (95% CI: 0.47, 1.73).
    • Multiple imputation and direct maximum likelihood demonstrated greater statistical power than analysis restricted to the validation subgroup, while all three provided unbiased estimates.

    Conclusions:

    • Multiple imputation is a valuable and flexible method for epidemiologists to reduce bias caused by outcome misclassification when validation data are available.
    • The approach provides unbiased estimates and improved statistical power compared to analyses restricted to validation subgroups.
    • Multiple imputation offers practical advantages in implementation for researchers familiar with missing data techniques.