Individual Treatment Effect Prediction for ALS Patients

TL;DR

This paper introduces a novel model-based random forest method to predict individual treatment effects for ALS patients, enabling personalized medicine by identifying which patients benefit most from Riluzole.

Contribution

The paper develops a new personalized treatment effect prediction method using model-based random forests, specifically applied to ALS and Riluzole, advancing personalized medicine approaches.

Findings

Some ALS patients benefit more from Riluzole than others.

The method effectively detects patient similarities in treatment response.

It enables personalized treatment effect estimation in clinical trials.

Abstract

A treatment for a complicated disease may be helpful for some but not all patients, which makes predicting the treatment effect for new patients important yet challenging. Here we develop a method for predicting the treatment effect based on patient char- acteristics and use it for predicting the effect of the only drug (Riluzole) approved for treating Amyotrophic Lateral Sclerosis (ALS). Our proposed method of model-based ran- dom forests detects similarities in the treatment effect among patients and on this basis computes personalised models for new patients. The entire procedure focuses on a base model, which usually contains the treatment indicator as a single covariate and takes the survival time or a health or treatment success measurement as primary outcome. This base model is used both to grow the model-based trees within the forest, in which the patient characteristics that interact with the treatment are split variables, and to com- pute the personalised models, in which the similarity measurements enter as weights. We applied the personalised models using data from several clinical trials for ALS from the PRO-ACT database. Our results indicate that some ALS patients benefit more from the drug Riluzole than others. Our method allows shifting from stratified medicine to person- alised medicine and can also be used in assessing the treatment effect for other diseases studied in a clinical trial.