= Population-averaged logistic model for repeated binary outcomes
{title2=$\operatorname{logit}\mathbb E(Y_{ij}\mid w_{ij})=w_{ij}^T\gamma$}
= Population-averaged logistic regression
{synonym}
For independent subjects with repeated <Bernoulli> observations, specify marginal means $m_{ij}=\operatorname{logit}^{-1}(w_{ij}^T\gamma)$. A <generalized estimating equation> uses their derivative matrix $D_i$ and working <covariance matrix> $V_i$ to solve $\sum_iD_i^TV_i^{-1}(Y_i-m_i)=0$. Subject-level <sandwich covariance matrices> give robust uncertainty when the mean is correctly specified but the working correlation is not. The coefficients describe population <odds ratios>; this mean specification does not prescribe a complete joint distribution within a subject.
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