Choosing the Best Training Programme: Is there a Case for Statistical Treatment Rules?

2010 ◽  
Vol 72 (2) ◽  
pp. 172-201 ◽  
Author(s):  
Jonas Staghøj ◽  
Michael Svarer ◽  
Michael Rosholm
Keyword(s):  
Statistical Treatment ◽  
Training Programme ◽  
Treatment Rules

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.

DeGIR-/DGNR training programme in interventional radiology and neuroradiology

VASA ◽  
2017 ◽  
Vol 46 (6) ◽  
pp. 494-495 ◽  
Author(s):  
Peter Landwehr ◽  
Peter Reimer ◽  
Arno Bücker ◽  
Ansgar Berlis ◽  
Werner Weber
Keyword(s):  
Interventional Radiology ◽  
Training Programme
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