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DatabaseConnector - Connecting to Various Database Platforms

An R 'DataBase Interface' ('DBI') compatible interface to various database platforms ('PostgreSQL', 'Oracle', 'Microsoft SQL Server', 'Amazon Redshift', 'Microsoft Parallel Database Warehouse', 'IBM Netezza', 'Apache Impala', 'Google BigQuery', 'Snowflake', 'Spark', 'SQLite', and 'InterSystems IRIS'). Also includes support for fetching data as 'Andromeda' objects. Uses either 'Java Database Connectivity' ('JDBC') or other 'DBI' drivers to connect to databases.

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hadesopenjdk

12.75 score 57 stars 16 dependents 908 scripts 3.3k downloads

SqlRender - Rendering Parameterized SQL and Translation to Dialects

A rendering tool for parameterized SQL that also translates into different SQL dialects. These dialects include 'Microsoft SQL Server', 'Oracle', 'PostgreSql', 'Amazon RedShift', 'Apache Impala', 'IBM Netezza', 'Google BigQuery', 'Microsoft PDW', 'Snowflake', 'Azure Synapse Analytics Dedicated', 'Apache Spark', 'SQLite', and 'InterSystems IRIS'.

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hadesopenjdk

12.05 score 87 stars 17 dependents 491 scripts 3.6k downloads

CohortConstructor - Build and Manipulate Study Cohorts Using a Common Data Model

Create and manipulate study cohorts in data mapped to the Observational Medical Outcomes Partnership Common Data Model.

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10.84 score 10 stars 4 dependents 235 scripts 1.3k downloads

PatientLevelPrediction - Develop Clinical Prediction Models Using the Common Data Model

A user friendly way to create patient level prediction models using the Observational Medical Outcomes Partnership Common Data Model. Given a cohort of interest and an outcome of interest, the package can use data in the Common Data Model to build a large set of features. These features can then be used to fit a predictive model with a number of machine learning algorithms. This is further described in Reps (2017) <doi:10.1093/jamia/ocy032>.

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hadesopenjdk

10.70 score 216 stars 330 scripts 311 downloads

FeatureExtraction - Generating Features for a Cohort

An R interface for generating features for a cohort using data in the Common Data Model. Features can be constructed using default or custom made feature definitions. Furthermore it's possible to aggregate features and get the summary statistics.

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hadesopenjdk

10.57 score 71 stars 3 dependents 258 scripts 1.2k downloads

ParallelLogger - Support for Parallel Computation, Logging, and Function Automation

Support for parallel computation with progress bar, and option to stop or proceed on errors. Also provides logging to console and disk, and the logging persists in the parallel threads. Additional functions support function call automation with delayed execution (e.g. for executing functions in parallel).

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hades

10.07 score 13 stars 16 dependents 98 scripts 6.3k downloads

DataQualityDashboard - Execute and View Data Quality Checks on OMOP CDM Database

Assesses data quality in Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) databases. Executes data quality checks and provides an R 'shiny' application to view the results.

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data-qualityopenjdk

9.99 score 176 stars 62 scripts 379 downloads

CohortMethod - Comparative Cohort Method with Large Scale Propensity and Outcome Models

Functions for performing comparative cohort studies in an observational database in the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Can extract all necessary data from a database. This implements large-scale propensity scores (LSPS) as described in Tian et al. (2018) <doi:10.1093/ije/dyy120>, using a large set of covariates, including for example all drugs, diagnoses, procedures, as well as age, comorbidity indexes, etc. Large scale regularized regression is used to fit the propensity and outcome models as described in Suchard et al. (2013) <doi:10.1145/2414416.2414791>. Functions are included for trimming, stratifying, (variable and fixed ratio) matching and weighting by propensity scores, as well as diagnostic functions, such as propensity score distribution plots and plots showing covariate balance before and after matching and/or trimming. Supported outcome models are (conditional) logistic regression, (conditional) Poisson regression, and (stratified) Cox regression. Also included are Kaplan-Meier plots that can adjust for the stratification or matching.

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hadescppopenjdk

9.92 score 89 stars 258 scripts 575 downloads

Andromeda - Asynchronous Disk-Based Representation of Massive Data

Storing very large data objects on a local drive, while still making it possible to manipulate the data in an efficient manner.

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hades

9.85 score 11 stars 10 dependents 64 scripts 1.5k downloads

Cyclops - Cyclic Coordinate Descent for Logistic, Poisson and Survival Analysis

This model fitting tool incorporates cyclic coordinate descent and majorization-minimization approaches to fit a variety of regression models found in large-scale observational healthcare data. Implementations focus on computational optimization and fine-scale parallelization to yield efficient inference in massive datasets. Please see: Suchard, Simpson, Zorych, Ryan and Madigan (2013) <doi:10.1145/2414416.2414791>.

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hadescpp

9.48 score 43 stars 6 dependents 95 scripts 1.0k downloads

CohortGenerator - Cohort Generation for the OMOP Common Data Model

Generate cohorts and subsets using an Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) Database. Cohorts are defined using 'CIRCE' (<https://github.com/ohdsi/circe-be>) or SQL compatible with 'SqlRender' (<https://github.com/OHDSI/SqlRender>).

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hadesopenjdk

8.51 score 14 stars 278 scripts 452 downloads

omock - Creation of Mock Observational Medical Outcomes Partnership Common Data Model

Creates mock data for testing and package development for the Observational Medical Outcomes Partnership common data model. The package offers functions crafted with pipeline-friendly implementation, enabling users to effortlessly include only the necessary tables for their testing needs.

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8.37 score 4 stars 384 scripts 796 downloads

EmpiricalCalibration - Routines for Performing Empirical Calibration of Observational Study Estimates

Routines for performing empirical calibration of observational study estimates. By using a set of negative control hypotheses we can estimate the empirical null distribution of a particular observational study setup. This empirical null distribution can be used to compute a calibrated p-value, which reflects the probability of observing an estimated effect size when the null hypothesis is true taking both random and systematic error into account. A similar approach can be used to calibrate confidence intervals, using both negative and positive controls. For more details, see Schuemie et al. (2013) <doi:10.1002/sim.5925> and Schuemie et al. (2018) <doi:10.1073/pnas.1708282114>.

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hadescpp

8.32 score 10 stars 5 dependents 154 scripts 633 downloads

SelfControlledCaseSeries - Self-Controlled Case Series

Execute the self-controlled case series (SCCS) design using observational data in the OMOP Common Data Model. Extracts all necessary data from the database and transforms it to the format required for SCCS. Age and season can be modeled using splines assuming constant hazard within calendar months. Event-dependent censoring of the observation period can be corrected for. Many exposures can be included at once (MSCCS), with regularization on all coefficients except for the exposure of interest. Includes diagnostics for all major assumptions of the SCCS.

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hadescppopenjdk

8.20 score 13 stars 72 scripts 517 downloads

OmopSketch - Characterise Tables of an OMOP Common Data Model Instance

Summarises key information in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Assess suitability to perform specific epidemiological studies and explore the different domains to obtain feasibility counts and trends.

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8.17 score 4 stars 1 dependents 141 scripts 750 downloads

PhenotypeR - Assess Study Cohorts Using a Common Data Model

Phenotype study cohorts in data mapped to the Observational Medical Outcomes Partnership Common Data Model. Diagnostics are run at the database, code list, cohort, and population level to assess whether study cohorts are ready for research.

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8.12 score 7 stars 52 scripts 996 downloads

ResultModelManager - Result Model Manager

Database data model management utilities for R packages in the Observational Health Data Sciences and Informatics programme. 'ResultModelManager' provides utility functions to allow package maintainers to migrate existing SQL database models, export and import results in consistent patterns.

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openjdk

7.21 score 4 stars 4 dependents 16 scripts 641 downloads

MeasurementDiagnostics - Diagnostics for Lists of Codes Based on Measurements

Diagnostics of list of codes based on concepts from the domains measurement and observation. This package works for data mapped to the Observational Medical Outcomes Partnership Common Data Model.

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7.13 score 1 stars 1 dependents 48 scripts 407 downloads

OmopViewer - Visualise OMOP Results using 'shiny' Applications

Visualise results obtained from analysing data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model using 'shiny' applications.

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7.11 score 3 stars 28 scripts 190 downloads

EvidenceSynthesis - Synthesizing Causal Evidence in a Distributed Research Network

Routines for combining causal effect estimates and study diagnostics across multiple data sites in a distributed study, without sharing patient-level data. Allows for normal and non-normal approximations of the data-site likelihood of the effect parameter.

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hadesopenjdk

6.95 score 9 stars 44 scripts 356 downloads

Eunomia - Standard Dataset Manager for Observational Medical Outcomes Partnership Common Data Model Sample Datasets

Facilitates access to sample datasets from the 'EunomiaDatasets' repository (<https://github.com/ohdsi/EunomiaDatasets>).

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hadesopenjdk

6.59 score 48 stars 182 scripts 880 downloads

CohortSymmetry - Sequence Symmetry Analysis Using the Observational Medical Outcomes Partnership Common Data Model

Calculating crude sequence ratio, adjusted sequence ratio and confidence intervals using data mapped to the Observational Medical Outcomes Partnership Common Data Model.

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6.46 score 2 stars 81 scripts 228 downloads

Characterization - Implement Descriptive Studies Using the Common Data Model

An end-to-end framework that enables users to implement various descriptive studies for a given set of target and outcome cohorts for data mapped to the Observational Medical Outcomes Partnership Common Data Model.

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openjdk

6.18 score 3 stars 28 scripts 304 downloads

OmopConstructor - Build Tables in the OMOP Common Data Model

Provides functionality to construct standardised tables from health care data formatted according to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. The package includes tools to build key tables such as observation period and drug era, among others.

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5.48 score 10 scripts 454 downloads

OmopOnSpark - Using a Common Data Model on Spark

Use health data in the Observational Medical Outcomes Partnership Common Data Model format in Spark.

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5.18 score 6 scripts 158 downloads

OhdsiReportGenerator - Observational Health Data Sciences and Informatics Report Generator

Extract results into R from the Observational Health Data Sciences and Informatics result database (see <https://ohdsi.github.io/Strategus/results-schema/index.html>) and generate reports/presentations via 'quarto' that summarize results in HTML format. Learn more about 'OhdsiReportGenerator' at <https://ohdsi.github.io/OhdsiReportGenerator/>.

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quartoopenjdk

5.08 score 5 scripts 546 downloads

CirceR - Construct Cohort Inclusion and Restriction Criteria Expressions

Wraps the 'CIRCE' (<https://github.com/ohdsi/circe-be>) 'Java' library allowing cohort definition expressions to be edited and converted to 'Markdown' or 'SQL'.

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hadesopenjdk

4.94 score 6 stars 1 dependents 96 scripts 813 downloads

PatientGenerator - Generator of Synthetic Patient Data for the OMOP Common Data Model

Tools to generate synthetic patient-level test datasets in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Includes a chat-driven generator backed by large language models and an interactive 'shiny' designer for editing CDM test sets.

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4.48 score 2 stars 42 downloads

LocalControl - Nonparametric Methods for Generating High Quality Comparative Effectiveness Evidence

Implements novel nonparametric approaches to address biases and confounding when comparing treatments or exposures in observational studies of outcomes. While designed and appropriate for use in studies involving medicine and the life sciences, the package can be used in other situations involving outcomes with multiple confounders. The package implements a family of methods for non-parametric bias correction when comparing treatments in observational studies, including survival analysis settings, where competing risks and/or censoring may be present. The approach extends to bias-corrected personalized predictions of treatment outcome differences, and analysis of heterogeneity of treatment effect-sizes across patient subgroups. For further details, please see: Lauve NR, Nelson SJ, Young SS, Obenchain RL, Lambert CG. LocalControl: An R Package for Comparative Safety and Effectiveness Research. Journal of Statistical Software. 2020. p. 1–32. Available from <doi:10.18637/jss.v096.i04>.

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cpp

4.22 score 1 stars 11 scripts 527 downloads

DatabaseConnectorJars - JAR Dependencies for the 'DatabaseConnector' Package

Provides external JAR dependencies for the 'DatabaseConnector' package.

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hadesopenjdk

2.70 score 1 stars 235 downloads