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arxiv: 2109.06707 · v2 · pith:P6SMDQBV · submitted 2021-09-14 · cs.LG · cs.AI

A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patients

Adam Izdebski , Patrick J. Thoral , Robbert C.A. Lalisang , Dean M. McHugh , Diederik Gommers , Olaf L. Cremer , Rob J. Bosman , Sander Rigter
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Evert-Jan Wils Tim Frenzel Dave A. Dongelmans Remko de Jong Marco A.A. Peters Marlijn J.A Kamps Dharmanand Ramnarain Ralph Nowitzky Fleur G.C.A. Nooteboom Wouter de Ruijter Louise C. Urlings-Strop Ellen G.M. Smit D. Jannet Mehagnoul-Schipper Tom Dormans Cornelis P.C. de Jager Stefaan H.A. Hendriks Sefanja Achterberg Evelien Oostdijk Auke C. Reidinga Barbara Festen-Spanjer Gert B. Brunnekreef Alexander D. Cornet Walter van den Tempel Age D. Boelens Peter Koetsier Judith Lens Harald J. Faber A. Karakus Robert Entjes Paul de Jong Thijs C.D. Rettig Sesmu Arbous Lucas M. Fleuren Tariq A. Dam Michele Tonutti Daan P. de Bruin Paul W.G. Elbers Giovanni Cin\`a
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classification cs.LG cs.AI
keywords treatmenteffectestimationcovid-19datamethodologyobservationalpatients
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Despite the recent progress in the field of causal inference, to date there is no agreed upon methodology to glean treatment effect estimation from observational data. The consequence on clinical practice is that, when lacking results from a randomized trial, medical personnel is left without guidance on what seems to be effective in a real-world scenario. This article proposes a pragmatic methodology to obtain preliminary but robust estimation of treatment effect from observational studies, to provide front-line clinicians with a degree of confidence in their treatment strategy. Our study design is applied to an open problem, the estimation of treatment effect of the proning maneuver on COVID-19 Intensive Care patients.

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