Causal inference with longitudinal data subject to irregular assessment times

Data collected in the context of usual care present a rich source of longitudinal data for research, but often require analyses that simultaneously enable causal inferences from observational data while handling irregular and informative assessment times. An inverse-weighting approach to this was recently proposed, and handles the case where the assessment times are at random (ie, conditionally independent of the outcome process given the observed history). In this paper, we extend the inverse-weighting approach to handle a special case of assessment not at random, where assessment and outcome processes are conditionally independent given past observed covariates and random effects. We use multiple outputation to accomplish the same purpose as inverse-weighting, and apply it to the Liang semi-parametric joint model. Moreover, we develop an alternative joint model that does not require covariates for the outcome model to be known at times where there is no assessment of the outcome. We examine the performance of these methods through simulation and illustrate them through a study of the causal effect of wheezing on time spent playing outdoors among children aged 2-9 years and enrolled in the TargetKids! study.

Automated summary

Data collected in the context of usual care provide a valuable resource for research. An inverse-weighting approach has been proposed to handle irregular assessment times. In this paper, the approach is extended to handle a special case of non-random assessment. Multiple outputation is used to achieve the same purpose, and an alternative joint model that does not require covariates is developed. The methods are examined through simulation and applied to a study on the causal effect of wheezing on time spent playing outdoors among children.