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documentation:software:methods_library [2017/04/21 08:29]
schuemie
documentation:software:methods_library [2018/12/14 13:12] (current)
schuemie
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 ====== Methods library ====== ====== Methods library ======
  
-The methods library consists of a set of R packages that can be used to execute observational studies against data in the Common Data Model (CDM) format. ​The following packages are available:+The methods library consists of a set of R packages that can be used to execute observational studies against data in the Common Data Model (CDM) format. ​
  
-  * [[https://​github.com/​OHDSI/​SelfControlledCohort|SelfControlledCohort]],​ previously also known as Observational Screening. +For more informationsee the [[https://ohdsi.github.io/MethodsLibrary/|Methods Library website]].
-  * [[https://​github.com/​OHDSI/​SelfControlledCaseSeries|SelfControlledCaseSeries]]an implementation of the Multiple SCCS method allowing many covariates (e.g. all drug exposures) to be included in the model. +
-  * [[https://github.com/​OHDSI/​IcTemporalPatternDiscovery|IcTemporalPatternDiscovery]] +
-  * [[https://github.com/OHDSI/CohortMethod|CohortMethod]], for performing new-user cohort studies using large-scale propensity scores (e.g. including all drugs, conditions, procedures, comorbidity indices, etc.). +
-  * [[https://​github.com/​OHDSI/​CaseControl|CaseControl]],​ for performing case-control studies with options to match on age, gender, visit data, provider, and length of observation,​ as well as adjusting for many covariates +
-  * [[https://​github.com/​OHDSI/​CaseCrossover|CaseCrossover]],​ for performing case-crossover studies with options to adjust for time-trends in exposure (case-time-control),​ and specifying multiple control windows. +
- +
-All OHDSI methods are designed to be able to run customized one-off studies for a particular exposure-outcome pair, but also across a large set of pairs, and using many different predefined analysis choices. +
- +
-**Supporting packages** +
- +
-From the OMOP experiment we learned that it is important to measure and understand the operating characteristics of methods in the settings that they'​re used, and to use these measured operating characteristics to calibrate the estimates produced by the methods. To facilitate these tasks, the following packages have been developed:​ +
- +
-  * [[https://​github.com/​OHDSI/​MethodEvaluation|MethodEvaluation]],​ for evaluating the performance of a method against established reference sets and simulated data. +
-  * [[https://​github.com/​OHDSI/​EmpiricalCalibration|EmpiricalCalibration]],​ for estimating the standard error distribution of a method using negative controls, and computing calibrated p-values. +
- +
-**Using the OHDSI R packages** +
- +
-All packages have package manuals describing the functions available in the package, and most packages have vignettes that describe how to use the package in a more user-friendly way. You can access the manuals and vignettes from the front pages of each Github repository. +
- +
-[[documentation:​r setup|How to set up your R environment]] to run the methods library.+
documentation/software/methods_library.1492763354.txt.gz · Last modified: 2017/04/21 08:29 by schuemie