Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution.

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Innovative methods for the identification of predictive biomarker signatures in oncology: Application to bevacizumab.

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Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution.

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Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution.

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Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution.

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David Page

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Finn Kuusisto

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Jeremy Weiss

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Jie Liu

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Kendrick Boyd

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Multivariable risk prediction can greatly enhance the statistical power of clinical trial subgroup analysis

Marginal structural models and causal inference in epidemiology

Doubly robust estimation in missing data and causal inference models.

Method for evaluating prediction models that apply the results of randomized trials to individual patients

The control of confounding by intermediate variables.

Estimating treatment effects for individual patients based on the results of randomised clinical trials.

Estimating Individualized Treatment Rules Using Outcome Weighted Learning

Menopausal hormone therapy and health outcomes during the intervention and extended poststopping phases of the Women's Health Initiative randomized trials.

Can overall results of clinical trials be applied to all patients?

An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies.

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machine learning

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Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution.

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P2860

P2860

Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution.

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