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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.
Parentage Assignment using Bi-Allelic Genetic Markers
Can be used for paternity and maternity assignment and outperforms
conventional methods where closely related individuals occur in the pool of
possible parents. The method compares the genotypes of offspring with any
combination of potentials parents and scores the number of mismatches of these
individuals at bi-allelic genetic markers (e.g. Single Nucleotide Polymorphisms).
It elaborates on a prior exclusion method based on the Homozygous Opposite Test
(HOT; Huisman 2017
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.
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)
Climate Window Analysis
Contains functions to detect and visualise periods of climate
sensitivity (climate windows) for a given biological response.
Please see van de Pol et al. (2016)
JAR Dependencies for the 'DatabaseConnector' Package
Provides external JAR dependencies for the 'DatabaseConnector' package.
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.
Tools for Type S (Sign) and Type M (Magnitude) Errors
Provides tools for working with Type S (Sign) and
Type M (Magnitude) errors, as proposed in Gelman and Tuerlinckx (2000)
Tidy Model Visualisation for Generalised Additive Models
Provides functions for visualising generalised additive models and getting predicted values using tidy tools from the 'tidyverse' packages.
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>).