scholarly journals Estimating optimal treatment rules with an instrumental variable: A partial identification learning approach

2021 ◽  
Vol 83 (2) ◽  
pp. 318-345 ◽  
Author(s):  
Hongming Pu ◽  
Bo Zhang
Keyword(s):  
Instrumental Variable ◽  
Partial Identification ◽  
Optimal Treatment ◽  
Learning Approach ◽  
Treatment Rules

scholarly journals On using electronic health records to improve optimal treatment rules in randomized trials

Biometrics ◽  
2020 ◽  
Vol 76 (4) ◽  
pp. 1075-1086
Author(s):  
Peng Wu ◽  
Donglin Zeng ◽  
Haoda Fu ◽  
Yuanjia Wang
Keyword(s):  
Electronic Health Records ◽  
Randomized Trials ◽  
Optimal Treatment ◽  
Health Records ◽  
Electronic Health ◽  
Treatment Rules

scholarly journals Statistical Methods for Establishing Personalized Treatment Rules in Oncology

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Junsheng Ma ◽  
Brian P. Hobbs ◽  
Francesco C. Stingo
Keyword(s):  
Data Analysis ◽  
Personalized Medicine ◽  
Statistical Inference ◽  
Statistical Methods ◽  
Treatment Strategies ◽  
Optimal Treatment ◽  
Personalized Treatment ◽  
Statistical Software ◽  
Genetic Features ◽  
Treatment Rules

The process for using statistical inference to establish personalized treatment strategies requires specific techniques for data-analysis that optimize the combination of competing therapies with candidate genetic features and characteristics of the patient and disease. A wide variety of methods have been developed. However, heretofore the usefulness of these recent advances has not been fully recognized by the oncology community, and the scope of their applications has not been summarized. In this paper, we provide an overview of statistical methods for establishing optimal treatment rules for personalized medicine and discuss specific examples in various medical contexts with oncology as an emphasis. We also point the reader to statistical software for implementation of the methods when available.

Evaluating the maximum regret of statistical treatment rules with sample data on treatment response

2021 ◽  
Vol 21 (1) ◽  
pp. 97-122
Author(s):  
Valentyn Litvin ◽  
Charles F. Manski
Keyword(s):  
Treatment Response ◽  
Instrumental Variable ◽  
Statistical Treatment ◽  
Decision Makers ◽  
Sample Data ◽  
Data Generating Process ◽  
Study Population ◽  
Average Treatment Effects ◽  
Treatment Rules ◽  
Maximum Regret

In this article, we present the wald_tc command, which computes the maximum regret (MR) of a user-specified statistical treatment rule that uses sample data on realized treatment response (and optionally an instrumental variable) to determine a treatment choice for a population. Because the outcomes of counterfactual treatments are not observed and treatment selection in the study population may not be random, decision makers may be able only to partially identify average treatment effects. wald_tc allows users to compute the MR of a proposed statistical treatment rule under a flexible specification of the data-generating process and determines the state that generates MR.

scholarly journals Robustifying trial-derived optimal treatment rules for a target population

2019 ◽  
Vol 13 (1) ◽  
pp. 1717-1743
Author(s):  
Ying-Qi Zhao ◽  
Donglin Zeng ◽  
Catherine M. Tangen ◽  
Michael L. Leblanc
Keyword(s):  
Target Population ◽  
Optimal Treatment ◽  
Treatment Rules

Does the IBS - Patient of today get an optimal treatment?

2008 ◽  
Author(s):  
Elsa Eriksson ◽  
Kristina Andren ◽  
Marie Larsson ◽  
Henry Eriksson ◽  
Goran Kurlberg
Keyword(s):  
Optimal Treatment
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