Conventional longitudinal data analysis methods assume that outcomes are independent of the data-collection schedule. However, the independence assumption may be violated, for example, when adverse events trigger additional physician visits in between prescheduled follow-ups. Observation times may therefore be associated with outcome values, which may introduce bias when estimating the e ect of covariates on outcomes using standard longitudinal regression methods. Existing semi-parametric methods that accommodate outcome-dependent observation times are limited to the analysis of continuous outcomes. We develop new methods for the analysis of binary outcomes, while retaining the exibility of semi-parametric models. Our methods are based on counting process approaches, rather than relying on possibly intractable likelihood-based or pseudo-likelihood-based approaches, and provide marginal, population-level inference. In simulations, we evaluate the statistical properties of our proposed methods. Comparisons are made to 'naive' GEE approaches that either do not account for outcome-dependent observation times or incorporate weights based on the observation-time process. We illustrate the utility of our proposed methods using data from a randomized controlled trial of interventions designed to improve adherence to warfarin therapy. We show that our method performs well in the presence of outcome-dependent observation times, and provide identical inference to 'naive' approaches when observation times are not associated with outcomes.



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