Author: Jennifer C.E Lane; James Weaver; Kristin Kostka; Talita Duarte-Salles; Maria Tereza F. Abrahao; Heba Alghoul; Osaid Alser; Thamir M Alshammari; Patricia Biedermann; Edward Burn; Paula Casajust; Mitch Conover; Aedin C. Culhane; Alexander Davydov; Scott L. DuVall; Dmitry Dymshyts; Sergio Fernández Bertolín; Kristina Fišter; Jill Hardin; Laura Hester; George Hripcsak; Seamus Kent; Sajan Khosla; Spyros Kolovos; Christophe G. Lambert; Johan ver der Lei; Ajit A. Londhe; Kristine E. Lynch; Rupa Makadia; Andrea V. Margulis; Michael E. Matheny; Paras Mehta; Daniel R. Morales; Henry Morgan-Stewart; Mees Mosseveld; Danielle Newby; Fredrik Nyberg; Anna Ostropolets; Rae Woong Park; Albert Prats-Uribe; Gowtham A. Rao; Christian Reich; Jenna Reps; Peter Rijnbeek; Selva Muthu Kumaran Sathappan; Martijn Schuemie; Sarah Seager; Anthony Sena; Azza Shoaibi; Matthew Spotnitz; Marc A. Suchard; Joel Swerdel; Carmen Olga Torre; David Vizcaya; Haini Wen; Marcel de Wilde; Seng Chan You; Lin Zhang; Oleg Zhuk; Patrick Ryan; Daniel Prieto-Alhambra
Title: Safety of hydroxychloroquine, alone and in combination with azithromycin, in light of rapid wide-spread use for COVID-19: a multinational, network cohort and self-controlled case series study Document date: 2020_4_10
ID: 2hbcbvt6_38
Snippet: PS stratification was used as the analytical strategy to adjust for imbalance between exposure cohorts in a comparison, using a large-scale regularized logistic regression 36 fitted with a LASSO penalty and with the optimal hyperparameter determined through 10-fold cross validation. Baseline patient characteristics were constructed for inclusion as potentially confounding covariates. 42 From this large set of tens of thousands of covariates, key .....
Document: PS stratification was used as the analytical strategy to adjust for imbalance between exposure cohorts in a comparison, using a large-scale regularized logistic regression 36 fitted with a LASSO penalty and with the optimal hyperparameter determined through 10-fold cross validation. Baseline patient characteristics were constructed for inclusion as potentially confounding covariates. 42 From this large set of tens of thousands of covariates, key predictors of exposure classification were selected for the propensity score. The predictor variables included were based on all observed patient characteristics and covariates available at each data source, including conditions, procedures, visits, observations and measurements. All covariates that occur in fewer than 0.1% of patients within the target and comparator cohorts were excluded prior to propensity score model fitting for computational efficiency.
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