Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error

被引:0
作者
James A. Watson
Chris C. Holmes
机构
[1] Mahidol Oxford Tropical Medicine Research Unit,
[2] Faculty of Tropical Medicine,undefined
[3] Mahidol University,undefined
[4] Nuffield Department of Medicine,undefined
[5] University of Oxford,undefined
[6] Department of Statistics,undefined
[7] University of Oxford,undefined
来源
Trials | / 21卷
关键词
Heterogeneous treatment effects; Randomised trials; Machine learning; Subgroup statistical analysis plan;
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[1]  
Rothwell P(2005)Subgroup analysis in randomised controlled trials: importance, indications, and interpretation Lancet 365 176-86
[2]  
Altman D(2015)Clinical trials: subgroup analyses in randomized trials – more rigour needed Nat Rev Clin Oncol 12 506-7
[3]  
Breiman L(2001)Statistical modeling: the two cultures (with comments and a rejoinder by the author) Stat Sci 16 199-231
[4]  
Crump RK(2008)Nonparametric tests for treatment effect heterogeneity Rev Econ Stat 90 389-405
[5]  
Hotz VJ(2009)Subgroup analysis via recursive partitioning J Mach Learn Res 10 141-58
[6]  
Imbens GW(2010)Calibrating parametric subject-specific risk estimation Biometrika 97 389-404
[7]  
Mitnik OA(2011)Subgroup identification from randomized clinical trial data Stat Med 30 2867-80
[8]  
Su X(2012)Estimating individualized treatment rules using outcome weighted learning J Am Stat Assoc 107 1106-18
[9]  
Tsai C-L(2013)Estimating treatment effect heterogeneity in randomized program evaluation Ann Appl Stat 7 443-70
[10]  
Wang H(2016)Recursive partitioning for heterogeneous causal effects Proc Natl Acad Sci 113 7353-60