[Statlist] Statistics Seminar, Friday 24 April at 16h

[Schaffner Portillo Maroussia]

STATISTICS SEMINAR

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Friday 24 April, 2020 - 16h00

zoom meeting<https://epfl.zoom.us/j/333379842>

Prof. David Madigan<https://datascience.columbia.edu/david-madigan-0>, Columbia University

will be speaking on :
Towards honest inference from real-world healthcare data

Abstract:

In practice, our learning healthcare system relies primarily on observational studies generating one effect estimate at a time using customized study designs with unknown operating characteristics and publishing - or not - one estimate at a time. When we investigate the distribution of estimates that this process has produced, we see clear evidence of its shortcomings, including an apparent over-abundance of statistically significant effects.
We propose a standardized process for performing observational research that can be evaluated, calibrated and applied at scale to generate a more reliable and complete evidence base than previously possible. We demonstrate this new paradigm by generating evidence about all pairwise comparisons of 39 treatments for hypertension for a relevant set of 58 health outcomes using nine large-scale health record databases from four countries.
In total, we estimate 1.3M hazard ratios, each using a comparative effectiveness study design and propensity score stratification on par with current one-off observational studies in the literature. Moreover, the process enables us to employ negative and positive controls to evaluate and calibrate estimates ensuring, for example, that the 95% confidence interval includes the true effect size 95% of the time. The result set consistently reflects current established knowledge where known, and its distribution shows no evidence of the faults of the current process.

Joint work with George Hripcsak, Patrick Ryan, Martijn Schuemie, and Marc Suchard.

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David Madigan is Professor of Statistics at Columbia University in New York City where he recently concluded a term as Dean of Arts and Sciences. He received a bachelor's degree in Mathematical Sciences and a Ph.D. in Statistics, both from Trinity College Dublin. He has previously worked for AT&T Inc., Soliloquy Inc., the University of Washington, Rutgers University, and SkillSoft, Inc. He has over 200 publications in such areas as Bayesian statistics, text mining, Monte Carlo methods, pharmacovigilance and probabilistic graphical models. He is an elected Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science. He has served terms as Editor-in-Chief of Statistical Science and of Statistical Analysis and Data Mining - the ASA Data Science Journal.

Best wishes,
Maroussia


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