Irregular visits in longitudinal studies: Comparing inverse intensity of visit weighting and imputation in a causal analysis of antidepressant therapeutic treatment

Irregular observation times are common in longitudinal observational studies and can affect causal inferences. We use data from electronic health records of Kaiser Permanente Washington patients in the United States who initiated an antidepressant medication between 2008 and 2018, with a confirming diagnosis of depression. We are interested in the estimation of the average causal effect of citalopram compared with fluoxetine on the severity of depression symptoms measured via the Patient Health Questionnaire. The score on the Patient Health Questionnaire is measured irregularly during follow‐up, and its observation is associated with patient characteristics. We describe two approaches, one based on imputation and one based on inverse weighting, to address observation irregularity in these data and obtain a consistent causal treatment effect. The approaches are compared in large simulation studies and applied to the Kaiser Permanente Washington data. Advantages and pitfalls of these methods are discussed.