{ "_id": "6a17f057acfb0bcc41da6db8", "Package": "CohortMethod", "Type": "Package", "Title": "Comparative Cohort Method with Large Scale Propensity and\nOutcome Models", "Version": "6.0.2", "Date": "2026-05-07", "Authors@R": "c(\nperson(\"Martijn\", \"Schuemie\", , \"schuemie@ohdsi.org\", role = c(\"aut\", \"cre\")),\nperson(\"Marc\", \"Suchard\", role = c(\"aut\")),\nperson(\"Patrick\", \"Ryan\", role = c(\"aut\"))\n)", "Maintainer": "Martijn Schuemie ", "Description": "Functions for performing comparative cohort studies in an\nobservational database in the Observational Medical Outcomes\nPartnership (OMOP) Common Data Model. Can extract all necessary\ndata from a database. This implements large-scale propensity\nscores (LSPS) as described in Tian et al. (2018)\n, using a large set of covariates,\nincluding for example all drugs, diagnoses, procedures, as well\nas age, comorbidity indexes, etc. Large scale regularized\nregression is used to fit the propensity and outcome models as\ndescribed in Suchard et al. (2013)\n. Functions are included for\ntrimming, stratifying, (variable and fixed ratio) matching and\nweighting by propensity scores, as well as diagnostic\nfunctions, such as propensity score distribution plots and\nplots showing covariate balance before and after matching\nand/or trimming. Supported outcome models are (conditional)\nlogistic regression, (conditional) Poisson regression, and\n(stratified) Cox regression. 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Schuemie", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Fields with minimum values", "Tables" ], "created": "2025-11-14 15:26:53", "modified": "2025-11-14 15:26:53", "commits": 1 }, { "source": "SingleStudies.Rmd", "filename": "SingleStudies.html", "title": "Single studies using the CohortMethod package", "author": "Martijn J. Schuemie, Marc A. 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