Inverse-intensity weighted generalized estimating equations for longitudinal data subject to irregular observation: which variables should be included in the visit rate model?

Longitudinal data are often subject to irregular and informative visit times. Weighting generalized estimating equations by the inverse of the visit rate yields asymptotically unbiased estimates of regression coefficients provided that outcomes and visit times are conditionally independent, given the covariates in the visit model. Adding other covariates has no impact on the asymptotic bias of estimated regression coefficients, provided that conditional independence is maintained, but the impact on their variances is unknown. We show that variances are unchanged on adding variables associated with neither outcome nor visit process, and decrease on adding variables associated with outcome but not visit process. Adding variables associated with visits but not outcome may either increase or decrease variances of estimated outcome model regression coefficients, depending on the correlation structure of the covariates and the outcome. Application to a study of major depressive disorder found that the variances of estimated regression coefficients were of a similar magnitude when predictors of outcome but not visits were added to the visit rate model but consistently larger, in some cases by a factor of 2, on adding predictors of visits but not outcome. We recommend that visit process models include variables associated with outcome, but that those unassociated with the outcome be treated with caution.