Below are the packages included in HADES. For each package a link is
provided with more information, including instructions on how to install
and use the package.
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New-user cohort studies using large-scale regression for propensity and
outcome models.Learn
more…
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Self-Controlled Case Series analysis using few or many predictors,
includes splines for age and
seasonality.Learn
more…
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A self-controlled cohort design, where time preceding exposure is used
as
control.Learn
more…
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Routines for combining causal effect estimates and study diagnostics
across multiple data sites in a distributed
study.Learn
more…
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Build and evaluate predictive models for user-specified outcomes, using
a wide array of machine learning
algorithms.Learn
more…
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Performing patient level prediction using deep
learningLearn
more…
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Building and validating ensemble patient-level predictive
models.Learn
more…
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Various types of characterizations of a target and outcome
cohort.Learn
more…
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Compute incidence rates and
proportionsLearn
more…
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Develop and manipulate complex cohort definitions in
RLearn more…
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An R wrapper for Circe, a library for creating cohort definitions,
expressing them as JSON, SQL, or
Markdown.Learn more…
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Instantiating cohorts in a database based on a set of cohort
definitions.Learn
more…
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The OHDSI Phenotype Library: a collection of community-maintained
pre-definined
cohorts.Learn
more…
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Generate a wide set of diagnostics to evaluate cohort definitions
against databases in the
CDM.Learn
more…
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Semi-automated evaluation of cohorts, producing metrics suchs as
sensitivity, specificity, and positive and negative preditive
value.Learn more…
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Visually explore all individual-level data of patients in a
cohortLearn
more…
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Knowledge-Enhanced Electronic Profile
ReviewLearn more…
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Generate descriptive statistics on an entire OMOP CDM
databaseLearn more…
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Expose and evaluate observational data
quality.Learn
more…
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Use negative control exposure-outcome pairs to profile and calibrate a
particular analysis
design.Learn
more…
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Use real data and established reference sets as well as simulations
injected in real data to evaluate the performance of
methods.Learn
more…
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Storing very large data objects on a local drive, while still making it
possible to manipulate the data in an efficient
manner.Learn more…
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A large scale k-nearest neighbor classifier using the Lucene search
engine.Learn more…
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Broken Adaptive Ridge Regression with
Cyclops.Learn
more…
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Highly efficient implementation of regularized logistic, Poisson and Cox
regression.Learn
more…
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Connect directly to a wide range of database platforms, including SQL
Server, Oracle, and
PostgreSQL.Learn
more…
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A standard CDM dataset for testing and demonstration purposes that runs
on an embedded SQLite
database.Learn more…
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Automatically extract large sets of features for user-specified cohorts
using data in the
CDM.Learn
more…
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Hydrating package skeletons into executable R study packages based on
specifications in JSON
format.Learn more…
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Performing L0-based regressions using
CyclopsLearn
more…
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Securely sharing (large) files between OHDSI
collaborators.Learn
more…
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Contains shiny modules that can be used within shiny result
interfacesLearn
more…
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Support for parallel computation with logging to console, disk, or
e-mail.Learn
more…
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A lightweight utility for data migrations allowing old results to work
with new shiny apps and data
visualisations.Learn
more…
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Interact with OHDSI WebAPI web
services.Learn
more…
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Interactively view analysis results across different types of
analyses.Learn
more…
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Generate SQL on the fly for the various SQL
dialects.Learn
more…