Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Canadian Journal of Statistics

DOI: 10.1002/cjs.70082

Authors: Janie Coulombe, Erica E. M. Moodie, Susan M. Shortreed

Institutions: Université de Montréal, McGill University, Kaiser Permanente, University of Washington

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,970