结果(博弈论)
特征选择
稳健性(进化)
二进制数
变量(数学)
人口
数学优化
萧条(经济学)
选择(遗传算法)
计算机科学
数学
计量经济学
人工智能
医学
数理经济学
数学分析
生物化学
化学
算术
环境卫生
宏观经济学
经济
基因
作者
Erica E. M. Moodie,Zeyu Bian,Janie Coulombe,Lian Yi,Archer Y. Yang,Susan M. Shortreed
标识
DOI:10.1093/biostatistics/kxad022
摘要
Abstract Despite growing interest in estimating individualized treatment rules, little attention has been given the binary outcome setting. Estimation is challenging with nonlinear link functions, especially when variable selection is needed. We use a new computational approach to solve a recently proposed doubly robust regularized estimating equation to accomplish this difficult task in a case study of depression treatment. We demonstrate an application of this new approach in combination with a weighted and penalized estimating equation to this challenging binary outcome setting. We demonstrate the double robustness of the method and its effectiveness for variable selection. The work is motivated by and applied to an analysis of treatment for unipolar depression using a population of patients treated at Kaiser Permanente Washington.
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