Abstract
SummaryWe compare the robustness under model misspecification of two approaches to fitting logistic regression models with unmatched case–control data. One is the standard survey approach based on weighted versions of population estimating equations. The other is the likelihood-based approach that is standard in medical applications. The conventional view is that the (less efficient) survey-weighted approach leads to greater robustness. We conclude that this view is not always justified.
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