# Individual Treatment Effect Prediction for ALS Patients

arXiv: https://arxiv.org/abs/1604.08720 · HTML: https://mlsanity.com/paper/1604.08720/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2016.

## Built on by (most cited first, 5 of 5)

- [What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?](https://mlsanity.com/paper/2206.10323/) (2022)
- [Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forests](https://mlsanity.com/paper/2210.02836/) (2022)
- [Targeting relative risk heterogeneity with causal forests](https://mlsanity.com/paper/2309.15793/) (2023)
- [Sequential Permutation Testing of Random Forest Variable Importance Measures](https://mlsanity.com/paper/2206.01284/) (2022)
- [Model-based recursive partitioning for discrete event times](https://mlsanity.com/paper/2209.06592/) (2022)

## Abstract (arXiv)

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.