Northwestern University

Tue 12:00 PM

BIDS Tuesdays Seminar Series: Faculty Research (Efficient Use of Error Prone and Big Data for Predictive Modeling and Trial Design)

When: Tuesday, February 21, 2017
12:00 PM - 1:00 PM  

Where: Arthur Rubloff Building, Lakeview Conference Room (11th Floor), 750 N Lake Shore Dr, Chicago, IL 60611 map it

Audience: Faculty/Staff - Student - Post Docs/Docs - Graduate Students

Contact: Lindsay Jane Varasteh   312.503.1997

Group: Center for Data Science and Informatics (CDSI)

Category: Academic


Electronic Medical Record (EMR) databases offer significant potential for developing clinical hypotheses and identifying disease risk associations by fitting statistical models that capture the relationship between a binary response variable and a set of predictor variables that represent clinical, phenotypical, and demographic data for the patient. However, EMR response data may be error prone for a variety of reasons. Performing a manual chart review to validate data accuracy is time consuming, which limits the number of chart reviews in a large database. We will present a new design-of-experiments–based systematic chart validation and review approach that is more powerful than the random validation sampling used in existing approaches. The developed method judiciously and efficiently selects the cases to validate for maximum information content. Simulated experimental results show the effectiveness of the new technique when the event rate is small (<1%) in the selected cohort. The technique is further developed for designing controlled trials, where we are interested in understanding association of covariates with the outcome while conducting the trial. For the treatment and control groups selection, we develop simple guidelines and an optimization algorithm that achieves much more accurate estimates of the covariate-dependent effects of the treatment than random sampling. We demonstrate the advantage of our method through both theoretical and numerical performance comparisons. The advantages are more pronounced when the trial cohort size is small.

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