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- W3124574412 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." @default.
- W3124574412 created "2021-02-01" @default.
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- W3124574412 date "2017-02-21" @default.
- W3124574412 modified "2023-10-18" @default.
- W3124574412 title "Individual treatment effect prediction for amyotrophic lateral sclerosis patients" @default.
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- W3124574412 doi "https://doi.org/10.1177/0962280217693034" @default.
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