Seminar Details
2018-05-29 (14:00) : Leveraging historical data for high-dimensional regression adjustment, a machine learning approach
At Euler building (room A.002)
Organized by Mathematical Engineering
Speaker :
Samuel Branders (Tools4Patient)
Abstract :
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 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. Our approach is to pre-specify the combination of the baseline covariates by building a "meta-covariate". In small studies, using this meta-covariate alone will limit the loss of degrees of freedom while making the best uses of all generated data.
Two advantages of fitting the covariates on independent data are to free the modeling from the study constraints and to limit the risk of overfitting. Those are of particular interest with complex data, i.e non-normal distribution or in the presence of non-linearities.
To demonstrate the benefit of the methodology, we discuss several questions:
- What are too many covariates? Can we go beyond the simple rule-of-thumb 1 variable for 10 patients?
- What should be the minimum performance of the ML model in this context?
- When should we use machine learning?