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Activity Number: 63 - Statistical Methods in Precision and Personalized Medicine and Subgroup Analysis
Type: Contributed
Date/Time: Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
Sponsor: Biopharmaceutical Section
Abstract #323522
Title: Sample Size and Levels of Treatment Effect Heterogeneity: Impact on the Estimation of Individualized Treatment Rules for Patients with Multiple Sclerosis
Author(s): Xiaotong Jiang and Gabrielle Simoneau* and Bora Youn and Changyu Shen and Fabio Pellegrini and Carl de Moor
Companies: Biogen and Biogen and Biogen and Biogen and Biogen and Biogen
Keywords: precision medicine; doubly robust estimator; individualized treatment rules; treatment effect heterogeneity; count outcomes
Abstract:

Precision medicine (PM) methods aim to discover an optimal individualized treatment rule (ITR), which recommends a treatment based on patient characteristics at the time of treatment decision. The goal of this study is to understand the ability of PM methods to correctly identify optimal ITR for count outcomes. A diverse collection of PM methods is used including generalized linear models, tree-based models, and doubly robust estimators. Methods are evaluated with cross validation using the estimated value function, which is the average treatment effect in the population had the treatment choice followed the estimated ITR. In simulations, we study the impact of sample size and different levels of treatment effect heterogeneity. In the case study of a randomized clinical trial, we compared optimal ITRs between two multiple sclerosis treatments, dimethyl fumarate and glatiramer acetate. Results show that model performance depends on the proportions of neutral, moderate, and high responders to treatment, and a subset of methods outperforms the “one-size-fits-all" rule, where all patients receive the same treatment, especially with larger sample sizes.


Authors who are presenting talks have a * after their name.

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