Clinical Value of Predicting Individual Treatment Effects for Intensive Blood Pressure Therapy: A Machine Learning Experiment to Estimate Treatment Effects from Randomized Trial Data
| Autor: | Tony Duan, Sanjay Basu, Andrew Y. Ng, Dillon Laird, Pranav Rajpurkar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Male
medicine.medical_specialty Calibration (statistics) Blood Pressure Risk Assessment Article Machine Learning Risk Factors Internal medicine medicine Data Mining Humans Baseline (configuration management) Antihypertensive Agents Aged Randomized Controlled Trials as Topic business.industry Absolute risk reduction Middle Aged Blood pressure Treatment Outcome Hypertension Clinical value Cardiology Female Cardiology and Cardiovascular Medicine business |
| Popis: | Background: The absolute risk reduction (ARR) in cardiovascular events from therapy is generally assumed to be proportional to baseline risk—such that high-risk patients benefit most. Yet newer analyses have proposed using randomized trial data to develop models that estimate individual treatment effects. We tested 2 hypotheses: first, that models of individual treatment effects would reveal that benefit from intensive blood pressure therapy is proportional to baseline risk; and second, that a machine learning approach designed to predict heterogeneous treatment effects—the X-learner meta-algorithm—is equivalent to a conventional logistic regression approach. Methods and Results: We compared conventional logistic regression to the X-learner approach for prediction of 3-year cardiovascular disease event risk reduction from intensive (target systolic blood pressure Conclusions: Predictions for individual treatment effects from trial data reveal that patients may experience ARRs not simply proportional to baseline cardiovascular disease risk. Machine learning methods may improve discrimination and calibration of individualized treatment effect estimates from clinical trial data. Clinical Trial Registration: URL: https://www.clinicaltrials.gov . Unique identifiers: NCT01206062; NCT00000620. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|