Improving Disease Prevalence Estimates Using Missing Data Techniques
Abstract
The prevalence of a disease in a population is defined as the proportion of people who are infected. Selection bias in disease prevalence estimates occurs if non-participation in testing is correlated with disease status. Missing data are commonly encountered in most medical research. Unfortunately, they are often neglected or not properly handled during analytic procedures, and this may substantially bias the results of the study, reduce the...
Paper Details
Title
Improving Disease Prevalence Estimates Using Missing Data Techniques
Published Date
Jan 1, 2016
Journal
Volume
06
Issue
06
Pages
1110 - 1122
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