May 25, 2020Open Access

Counterfactual clinical prediction models could help to infer individualized treatment effects in randomized controlled trials—An illustration with the International Stroke Trial

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Resumen

Counterfactual prediction models could help researchers and clinicians (i) infer individualized treatment effects and (ii) better target patients who may benefit from treatments.

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Nguyen et al. (Mon,) studied this question.

synapsesocial.com/papers/69dccee72e182f147e1333c3 https://doi.org/https://doi.org/10.1016/j.jclinepi.2020.05.022

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Conditional Independence in Statistical Theory1979 · 1,548 citations
2What is “quality of evidence” and why is it important to clinicians?2008 · 3,254 citations
3Subgroup analyses in randomised controlled trials: quantifying the risks of false-positives and false-negatives2001 · 583 citations
4Estimating causal effects of treatments in randomized and nonrandomized studies.1974 · 9,489 citations
5Assessing Treatment‐Selection Markers using a Potential Outcomes Framework2012 · 56 citations

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