A sparse additive model for treatment effect-modifier selection

Abstract

Summary Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. This article develops a sparse additive model focused on estimation of treatment effect modification with simultaneous treatment effect-modifier selection. We propose a version of the sparse additive model uniquely constrained to estimate the interaction effects between treatment and pretreatment covariates, while leaving the main effects of the pretreatment covariates unspecified. The proposed regression model can effectively identify treatment effect-modifiers that exhibit possibly nonlinear interactions with the treatment variable that are relevant for making optimal treatment decisions. A set of simulation experiments and an application to a dataset from a randomized clinical trial are presented to demonstrate the method.

An Innovative Non-Linear Prediction Model for Clinical Benefit in Women with Newly Diagnosed Breast Cancer Using Baseline FDG-PET/CT and Clinical Data

“…Subsequently, a logistic generalized additive model (GAM) was chosen as the statistical approach to assess the impact of all listed variables on the CB of treatment. This modeling choice was made because of its efficacy in capturing complex, non-linear relationships inherent in the data [36][37][38]. The determination of model parameters was conducted through a meticulous stratified bootstrap procedure, ensuring that the statistical properties of the stratified subsets aligned harmoniously with those of the original dataset.…”

“…A logistic generalized additive model was chosen as an innovative statistical approach to assess the impact of all listed variables on CB of treatment in our cohort. This modeling choice was rooted in its multidisciplinary approach (with regards to the data incorporated into the model) but above all, its efficacy in capturing complex, non-linear relationships inherent in the data [37,38]. Consequently, we developed a multivariate prediction model incorporating age, BMI, T, M, PT TLG, and PT volume as predictive biomarkers, with CB as the primary endpoint.…”

Functional additive models for optimizing individualized treatment rules

“…This figure appears in color in the electronic version of this article, and any mention of color refers to that version functional covariates 𝑋 = (𝑋 1 (𝑠), … , 𝑋 19 (𝑠)) was defined on the interval [0, 1]. We also considered 𝑞 = 5 baseline scalar covariates consisting of the week 0 HRSD score (𝑍 1 ), sex (𝑍 2 ), age at evaluation (𝑍 3 ), word fluency (𝑍 4 ), and Flanker accuracy (𝑍 5 ) cognitive test scores, which were identified as predictors of differential treatment response in a previous study (Park et al, 2020). In this dataset, 49% of the subjects were randomized to the sertraline (𝐴 = 2).…”

“…We also considered $$q=5$$ baseline scalar covariates consisting of the week 0 HRSD score ( Z 1 ), sex ( Z 2 ), age at evaluation ( Z 3 ), word fluency ( Z 4 ), and Flanker accuracy ( Z 5 ) cognitive test scores, which were identified as predictors of differential treatment response in a previous study (Park et al. , 2020). In this dataset, 49% of the subjects were randomized to the sertraline $$(A=2)$$.…”

Identification of Optimal Combined Moderators for Time to Relapse

“…On the other hand, exploratory analyses that examine one moderator per statistical model are known for the tendency of finding spurious interactions, especially when a long list of variables is tested for moderation effects. Thus, extant literature has focused on recommending an appropriate treatment by estimating effect modification via a systematic approach (Kraemer, 2013;Tian et al, 2014;Chen et al, 2017;Song et al, 2017;Liang and Yu, 2022;Yadlowsky et al, 2021;Park et al, 2022).…”