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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 2. Effect modeling approaches were seriously overfit and were prone to treatment mistargeting in the absence of true treatment interaction.

Figure 2

Benefit predictions were based on different models fitted in the samples: a model without treatment interactions (panel A), a model with all treatment interactions (panel B), a model with all treatment interactions using backward selection based on AIC (panel C), and a model with all treatment interactions fitted with Lasso regression (panel D). The agreement between predicted (brown bars) and observed (white bars) benefit in predicted benefit quartiles of the population was better for the risk modeling approach (A) compared to the effect modeling approaches (B-D). Moreover, the risk modeling approach (A) resulted in more heterogeneity of observed benefit, i.e. is better able to distinguish between patients with low and patients with high benefit.