In randomized trials, outcome assessment times often vary across participants and may depend on the underlying outcome process, so outcomes observed near a target time need not represent the target population. Whether assessment is informative cannot be determined from the observed data alone. We extend the sensitivity analysis framework of Smith et al.\ (2024), which is anchored in an explainable assessment assumption and was restricted to continuous outcomes through an identity-link marginal mean model, to a broader class of link functions that accommodates binary and count outcomes. Within this framework, we develop an influence function-based augmented inverse intensity-weighted estimator that is root-$n$ consistent under standard regularity conditions. We also propose a data-driven procedure for selecting the range of the sensitivity parameter from plausible bounds on the target mean outcome and for constructing a confidence set for this range, which accounts for uncertainty in sensitivity parameter selection. We illustrate the method with data from the ARC study and evaluate its finite-sample performance in simulation studies.

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