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. 2020 Nov 2;12:1223–1234. doi: 10.2147/CLEP.S274466

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© 2020 Meid et al.

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Prediction of individual treatment effects and recommendations from individualized treatment rules.

Notes: Conditional average treatment effect (CATE) estimation was conducted via LASSO-based penalized regression to provide the predicted individual treatment effect (PITE) expressed as benefit scores.²⁴^,³⁵ A decision rule would suggest selecting treatment A if the benefit score is greater than zero. Classifying individuals according to their benefit scores will yield differing group means in the variables that are effect modifiers (top). Thus, patients under current treatment B (indicated in blue color) may also be recommended the alternative; this applies to those patients having received treatment B whose benefit scores are greater than the decision threshold set to zero (mid). This ITR therefore defines an action space where outcome frequencies above the threshold could be presumably reduced by selecting the alternative. Likewise, this also applies to the opposite case, where such a re-assignment of treatment could reduce outcome frequencies suggesting option B at benefit scores below the threshold of zero (mid, bottom).