2021
DOI: 10.1002/sim.9154
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A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint

Abstract: Randomized trials typically estimate average relative treatment effects, but decisions on the benefit of a treatment are possibly better informed by more individualized predictions of the absolute treatment effect. In case of a binary outcome, these predictions of absolute individualized treatment effect require knowledge of the individual's risk without treatment and incorporation of a possibly differential treatment effect (ie, varying with patient characteristics). In this article, we lay out the causal str… Show more

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Cited by 41 publications
(35 citation statements)
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“… 44 45 These predictions of patients’ absolute risk reduction require estimation of the patients’ short term risk of mortality with and without treatment, which might require the estimation of treatment effects that differ by patient. 45 …”
Section: Discussionmentioning
confidence: 99%
See 2 more Smart Citations
“… 44 45 These predictions of patients’ absolute risk reduction require estimation of the patients’ short term risk of mortality with and without treatment, which might require the estimation of treatment effects that differ by patient. 45 …”
Section: Discussionmentioning
confidence: 99%
“…44 45 These predictions of patients' absolute risk reduction require estimation of the patients' short term risk of mortality with and without treatment, which might require the estimation of treatment effects that differ by patient. 45 As variants of the disease emerge, new treatments are developed, and the disease is better managed, predictor effects and the incidence of mortality due to covid-19 may vary, thereby potentially limiting the predictive performance of the models we investigated.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…The tool is illustrated in figure 3. Such predictions often require shrinkage and penalisation techniques,47 52 53 in order to mitigate against overfitting (extreme predictions), and Karyotaki et al used the least absolute shrinkage and selection operator (LASSO) for this purpose 22…”
Section: Potential Benefits Of Using Ipd For Nmamentioning
confidence: 99%
“…Then, CATE is done by taking the difference between the estimates of these estimates. While most approaches concentrate on estimating CATE using observational data, it is also possible to do so using data from an RCT [Hoogland et al, 2021].…”
Section: Treatment Effect and Precision Medicinementioning
confidence: 99%