A new sandwich variance estimator for PGEE with near-separated binary data provides conservative or near-nominal type I error in small-N low-event settings by retaining score-level leverage correction plus a finite-population upward adjustment.
Small sample performance of bias-corrected sandwich estimators for cluster-randomized trials with binary outcomes.Statistics in medicine, 34(2):281–296
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Overstuffed sandwiches and separation anxiety: finite-sample variance estimation for penalized GEE with near-separated binary data
A new sandwich variance estimator for PGEE with near-separated binary data provides conservative or near-nominal type I error in small-N low-event settings by retaining score-level leverage correction plus a finite-population upward adjustment.