Using the Causal Inference Framework to Support Individualized Drug Treatment Decisions Based on Observational Healthcare Data

Andreas D Meid; Carmen Ruff; Lucas Wirbka; Felicitas Stoll; Hanna M Seidling; Andreas Groll; Walter E Haefeli

doi:10.2147/CLEP.S274466

Using the Causal Inference Framework to Support Individualized Drug Treatment Decisions Based on Observational Healthcare Data

Clin Epidemiol. 2020 Nov 2:12:1223-1234. doi: 10.2147/CLEP.S274466. eCollection 2020.

Authors

Andreas D Meid¹, Carmen Ruff¹, Lucas Wirbka¹, Felicitas Stoll¹, Hanna M Seidling^{1

2}, Andreas Groll³, Walter E Haefeli^{1

2}

Affiliations

¹ Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg 69120, Germany.
² Cooperation Unit Clinical Pharmacy, University of Heidelberg, Heidelberg 69120, Germany.
³ Department of Statistics, TU Dortmund University, Dortmund 44227, Germany.

Abstract

When healthcare professionals have the choice between several drug treatments for their patients, they often experience considerable decision uncertainty because many decisions simply have no single "best" choice. The challenges are manifold and include that guideline recommendations focus on randomized controlled trials whose populations do not necessarily correspond to specific patients in everyday treatment. Further reasons may be insufficient evidence on outcomes, lack of direct comparison of distinct options, and the need to individually balance benefits and risks. All these situations will occur in routine care, its outcomes will be mirrored in routine data, and could thus be used to guide decisions. We propose a concept to facilitate decision-making by exploiting this wealth of information. Our working example for illustration assumes that the response to a particular (drug) treatment can substantially differ between individual patients depending on their characteristics (heterogeneous treatment effects, HTE), and that decisions will be more precise if they are based on real-world evidence of HTE considering this information. However, such methods must account for confounding by indication and effect measure modification, eg, by adequately using machine learning methods or parametric regressions to estimate individual responses to pharmacological treatments. The better a model assesses the underlying HTE, the more accurate are predicted probabilities of treatment response. After probabilities for treatment-related benefit and harm have been calculated, decision rules can be applied and patient preferences can be considered to provide individual recommendations. Emulated trials in observational data are a straightforward technique to predict the effects of such decision rules when applied in routine care. Prediction-based decision rules from routine data have the potential to efficiently supplement clinical guidelines and support healthcare professionals in creating personalized treatment plans using decision support tools.

Keywords: claims data; confounding by indication; decision-making; effect modification; heterogeneous treatment effects; prediction-based decision rules.