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. Author manuscript; available in PMC: 2020 Oct 1.
Published in final edited form as: J Clin Epidemiol. 2019 Jun 10;114:72–83. doi: 10.1016/j.jclinepi.2019.05.029

Figure 5. The risk modeling approach (Constant relative treatment effect) had minimal prediction error in the absence of true interactions, but was outperformed by penalized regression approaches (Standard Lasso or Ridge) in the presence of true interactions.

Figure 5

The root mean squared error (rMSE) represents the root of the mean of the square differences between predicted benefit and true benefit in the population for base case simulation scenarios without true interaction (panel A), and with true interaction (panel B).