Measuring the performance of prediction models to personalize treatment choice

Author:

Efthimiou Orestis123ORCID,Hoogland Jeroen45ORCID,Debray Thomas P.A.46ORCID,Seo Michael17ORCID,Furukawa Toshiaki A.8,Egger Matthias1910,White Ian R.11ORCID

Affiliation:

1. Institute of Social and Preventive Medicine (ISPM), University of Bern Bern Switzerland

2. Institute of Primary Health Care (BIHAM), University of Bern Bern Switzerland

3. Department of Psychiatry University of Oxford Oxford UK

4. Julius Center for Health Sciences and Primary Care University Medical Center Utrecht, Utrecht University Utrecht The Netherlands

5. Department of Epidemiology and Data Science Amsterdam University Medical Centers Amsterdam The Netherlands

6. Smart Data Analysis and Statistics B.V. Utrecht The Netherlands

7. Graduate School for Health Sciences University of Bern Bern Switzerland

8. Departments of Health Promotion and Human Behavior and of Clinical Epidemiology Kyoto University Graduate School of Medicine/School of Public Health Kyoto Japan

9. Centre for Infectious Disease Epidemiology and Research, Faculty of Health Sciences University of Cape Town Cape Town South Africa

10. Population Health Sciences, Bristol Medical School University of Bristol Bristol UK

11. MRC Clinical Trials Unit at UCL University College London London UK

Abstract

When data are available from individual patients receiving either a treatment or a control intervention in a randomized trial, various statistical and machine learning methods can be used to develop models for predicting future outcomes under the two conditions, and thus to predict treatment effect at the patient level. These predictions can subsequently guide personalized treatment choices. Although several methods for validating prediction models are available, little attention has been given to measuring the performance of predictions of personalized treatment effect. In this article, we propose a range of measures that can be used to this end. We start by defining two dimensions of model accuracy for treatment effects, for a single outcome: discrimination for benefit and calibration for benefit. We then amalgamate these two dimensions into an additional concept, decision accuracy, which quantifies the model's ability to identify patients for whom the benefit from treatment exceeds a given threshold. Subsequently, we propose a series of performance measures related to these dimensions and discuss estimating procedures, focusing on randomized data. Our methods are applicable for continuous or binary outcomes, for any type of prediction model, as long as it uses baseline covariates to predict outcomes under treatment and control. We illustrate all methods using two simulated datasets and a real dataset from a trial in depression. We implement all methods in the R package predieval. Results suggest that the proposed measures can be useful in evaluating and comparing the performance of competing models in predicting individualized treatment effect.

Funder

European Commission

Medical Research Council Canada

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

ZonMw

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

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