Dealing with irregular and informative visits

This Chapter discusses the issue of irregular, covariate-dependent measurement times when drawing causal inferences with non-randomized data. We call measurement times the times when a variable of interest is observed. The focus is on a longitudinal outcome, e.g., a clinical measure of interest, that is observed only sporadically. The reasons why irregular, covariate-dependent measurements can bias causal inference of the conditional or the marginal treatment effects on the longitudinal outcome are discussed. Two types of methods to account for irregular measurement times are distinguished, those using inverse weights to create a pseudo-population in which covariates are balanced across measured outcomes and those which are not measured, and methods based on the imputation of the longitudinal outcome process at a set of prespecified time points. Modeling of the measurement times and the causal assumptions that are required when using inverse weights are briefly discussed. We consider the advantages and pitfalls of imputation methods over weighting methods. A toy case study is presented, and R code is provided on the companion website to implement the two main methods discussed in this Chapter to deal with irregular measurement times.