Invited Commentary: Treatment Drop-in—Making the Case for Causal Prediction. Issue 10 (17th February 2021)

• Record Type: Journal Article
• Authors: Sperrin, Matthew; Diaz-Ordaz, Karla; Pajouheshnia, Romin
• Is Part Of: American journal of epidemiology. Volume 190:Issue 10(2021)
• Page Start: 2015
• Page End: 2018
• Publication Date: 2021-02-17
• Subjects: counterfactual causal inference -- risk prediction -- treatment drop-in; Epidemiology -- Periodicals; Public health -- Periodicals; 614.4
• DOI: 10.1093/aje/kwab030
• Languages: English
• ISSNs: 0002-9262

Abstract

Abstract: Clinical prediction models (CPMs) are often used to guide treatment initiation, with individuals at high risk offered treatment. This implicitly assumes that the probability quoted from a CPM represents the risk to an individual of an adverse outcome in absence of treatment. However, for a CPM to correctly target this estimand requires careful causal thinking. One problem that needs to be overcome is treatment drop-in: where individuals in the development data commence treatment after the time of prediction but before the outcome occurs. In this issue of the Journal, Xu et al. ( Am J Epidemiol . 2021;190(10):2000–2014) use causal estimates from external data sources, such as clinical trials, to adjust CPMs for treatment drop-in. This represents a pragmatic and promising approach to address this issue, and it illustrates the value of utilizing causal inference in prediction. Building causality into the prediction pipeline can also bring other benefits. These include the ability to make and compare hypothetical predictions under different interventions, to make CPMs more explainable and transparent, and to improve model generalizability. Enriching CPMs with causal inference therefore has the potential to add considerable value to the role of prediction in healthcare.