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PSI Conference 2018: Leveraging historical data for high dimensional regression adjustment, a machine learning approach

06 June 2018

The amount of data collected from patients involved in clinical trials is continuously growing. All those patient's characteristics are potential covariates that could be used to improve study analysis and power. At the same time, the development of computerized systems simplifies the access to huge amount of historical data. However, it is still difficult to leverage those big data when dealing with small clinical trials, such as in Phases I and II. Their restricted number of patients limits the possible number of covariates included in the analysis. The purpose of this talk is to present how machine learning can overcome this problem by taking advantage of historical data with larger sample sizes. We also put the approach in perspective with the regulatory guideline on the use of adjustment for baseline covariates.

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