Featured Paper: Individual Treatment Effect Prediction for ALS Patients

This paper helps to illustrate the the trend of marrying machine learning with statistics, to combine their strengths: individualization of estimates and proper capture of weak signals on one side, and management & operational guarantees future unknown data on the other side. It focuses on individual effect prediction.

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 characteristics
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 random
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 compute
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 personalised
medicine and can also be used in assessing the treatment effect for other diseases
studied in a clinical trial.

Full paper link

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