|Title||Concordance-Assisted Learning for Estimating Optimal Individualized Treatment Regimes.|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Fan, Caiyun, Wenbin Lu, Rui Song, and Yong Zhou|
|Journal||J R Stat Soc Series B Stat Methodol|
|Date Published||2017 Nov|
In this article, we propose a new concordance-assisted learning for estimating optimal individualized treatment regimes. We first introduce a type of concordance function for prescribing treatment and propose a robust rank regression method for estimating the concordance function. We then find treatment regimes, up to a threshold, to maximize the concordance function, named prescriptive index. Finally, within the class of treatment regimes that maximize the concordance function, we find the optimal threshold to maximize the value function. We establish the convergence rate and asymptotic normality of the proposed estimator for parameters in the prescriptive index. An induced smoothing method is developed to estimate the asymptotic variance of the proposed estimator. We also establish the -consistency of the estimated optimal threshold and its limiting distribution. In addition, a doubly robust estimator of parameters in the prescriptive index is developed under a class of monotonic index models. The practical use and effectiveness of the proposed methodology are demonstrated by simulation studies and an application to an AIDS data.
|Alternate Journal||J R Stat Soc Series B Stat Methodol|
|Original Publication||Concordance-assisted learning for estimating optimal individualized treatment regimes.|
|PubMed Central ID||PMC5774868|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States|
Concordance-Assisted Learning for Estimating Optimal Individualized Treatment Regimes.