Tuesday, March 31st
LRW – 1003
2:00pm – 3:00pm
Towards Honest Inference From Real-World Healthcare Data
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
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.