Tri-Long Nguyen1, Gary S Collins2, Paul Landais3, Yannick Le Manach4. 1. Section of Epidemiology, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen K, Denmark; Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Windmill Road, Oxford, UK; Laboratory of Biostatistics, Epidemiology, Clinical Research and Health Economics, EA2415, Montpellier University, Montpellier, France; Departments of Anesthesia & Health Research Methods, Evidence, and Impact, Michael DeGroote School of Medicine, Faculty of Health Sciences, McMaster University and the Perioperative Research Group, Population Health Research Institute, Hamilton, Canada; Department of Pharmacy, Nîmes University Hospital, University of Montpellier, Nîmes, France. Electronic address: long@sund.ku.dk. 2. Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Windmill Road, Oxford, UK; NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK. 3. Laboratory of Biostatistics, Epidemiology, Clinical Research and Health Economics, EA2415, Montpellier University, Montpellier, France. 4. Departments of Anesthesia & Health Research Methods, Evidence, and Impact, Michael DeGroote School of Medicine, Faculty of Health Sciences, McMaster University and the Perioperative Research Group, Population Health Research Institute, Hamilton, Canada.
Abstract
OBJECTIVE: Causal treatment effects are estimated at the population level in randomized controlled trials, while clinical decision is often to be made at the individual level in practice. We aim to show how clinical prediction models used under a counterfactual framework may help to infer individualized treatment effects. STUDY DESIGN AND SETTING: As an illustrative example, we reanalyze the International Stroke Trial. This large, multicenter trial enrolled 19,435 adult patients with suspected acute ischemic stroke from 36 countries, and reported a modest average benefit of aspirin (vs. no aspirin) on a composite outcome of death or dependency at 6 months. We derive and validate multivariable logistic regression models that predict the patient counterfactual risks of outcome with and without aspirin, conditionally on 23 predictors. RESULTS: The counterfactual prediction models display good performance in terms of calibration and discrimination (validation c-statistics: 0.798 and 0.794). Comparing the counterfactual predicted risks on an absolute difference scale, we show that aspirin-despite an average benefit-may increase the risk of death or dependency at 6 months (compared with the control) in a quarter of stroke patients. CONCLUSIONS: Counterfactual prediction models could help researchers and clinicians (i) infer individualized treatment effects and (ii) better target patients who may benefit from treatments.
OBJECTIVE: Causal treatment effects are estimated at the population level in randomized controlled trials, while clinical decision is often to be made at the individual level in practice. We aim to show how clinical prediction models used under a counterfactual framework may help to infer individualized treatment effects. STUDY DESIGN AND SETTING: As an illustrative example, we reanalyze the International Stroke Trial. This large, multicenter trial enrolled 19,435 adult patients with suspected acute ischemic stroke from 36 countries, and reported a modest average benefit of aspirin (vs. no aspirin) on a composite outcome of death or dependency at 6 months. We derive and validate multivariable logistic regression models that predict the patient counterfactual risks of outcome with and without aspirin, conditionally on 23 predictors. RESULTS: The counterfactual prediction models display good performance in terms of calibration and discrimination (validation c-statistics: 0.798 and 0.794). Comparing the counterfactual predicted risks on an absolute difference scale, we show that aspirin-despite an average benefit-may increase the risk of death or dependency at 6 months (compared with the control) in a quarter of strokepatients. CONCLUSIONS: Counterfactual prediction models could help researchers and clinicians (i) infer individualized treatment effects and (ii) better target patients who may benefit from treatments.
Authors: Valentijn M T de Jong; Rebecca Z Rousset; Neftalí Eduardo Antonio-Villa; Arnoldus G Buenen; Ben Van Calster; Omar Yaxmehen Bello-Chavolla; Nigel J Brunskill; Vasa Curcin; Johanna A A Damen; Carlos A Fermín-Martínez; Luisa Fernández-Chirino; Davide Ferrari; Robert C Free; Rishi K Gupta; Pranabashis Haldar; Pontus Hedberg; Steven Kwasi Korang; Steef Kurstjens; Ron Kusters; Rupert W Major; Lauren Maxwell; Rajeshwari Nair; Pontus Naucler; Tri-Long Nguyen; Mahdad Noursadeghi; Rossana Rosa; Felipe Soares; Toshihiko Takada; Florien S van Royen; Maarten van Smeden; Laure Wynants; Martin Modrák; Folkert W Asselbergs; Marijke Linschoten; Karel G M Moons; Thomas P A Debray Journal: BMJ Date: 2022-07-12
Authors: Jeroen Hoogland; Joanna IntHout; Michail Belias; Maroeska M Rovers; Richard D Riley; Frank E Harrell; Karel G M Moons; Thomas P A Debray; Johannes B Reitsma Journal: Stat Med Date: 2021-08-16 Impact factor: 2.497