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Emily Fox | Stanford HAI

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peopleFaculty

Emily Fox

Professor of Statistics and, by courtesy, of Computer Science

Emily comes to Stanford from the University of Washington where she has held the post of Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science & Engineering and Department of Statistics. Since 2018, Emily has led the Health AI team at Apple where she is a Distinguished Engineer. Prior to joining UW, Emily was an Assistant Professor at the Wharton School Department of Statistics at the University of Pennsylvania. She earned her doctorate from Electrical Engineering and Computer Science (EECS) at MIT where her thesis was recognized with EECS' Jin-Au Kong Outstanding Doctoral Thesis Prize and the Leonard J. Savage Award for Best Thesis in Applied Methodology.

Emily has been recognized with a Presidential Early Career Award for Scientists and Engineers (PECASE), a Sloan Research Fellowship, ONR Young Investigator Award, and NSF CAREER Award. Her research interests are in large-scale Bayesian dynamic modeling, interpretability and computations, with applications in health and computational neuroscience.​

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Research
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Smart Start—Designing Powerful Clinical Trials Using Pilot Study Data

Emily Fox, Ramesh Johari, Priya Prahalad, Dessi P Zaharieva, Johannes Ferstad, Manisha Desai, David Scheinker, David Maahs
HealthcareJan 22

BACKGROUND

Digital health interventions may be optimized before evaluation in a randomized clinical trial. Although many digital health interventions are deployed in pilot studies, the data collected are rarely used to refine the intervention and the subsequent clinical trials.

METHODS

We leverage natural variation in patients eligible for a digital health intervention in a remote patient-monitoring pilot study to design and compare interventions for a subsequent randomized clinical trial.

RESULTS

Our approach leverages patient heterogeneity to identify an intervention with twice the estimated effect size of an unoptimized intervention.

CONCLUSIONS

Optimizing an intervention and clinical trial based on pilot data may improve efficacy and increase the probability of success. (Funded by the National Institutes of Health and others; ClinicalTrials.gov number, NCT04336969.)