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# sdtm.oak V0.2.0
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- DM Domain: New functions `calc_min_max_date()` and `oak_calc_ref_dates()` to support DM domain programming.
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- Supplementary Qualifier SUPP Domains - New function`generate_sdtm_supp()` to facilitate the programming of supplemental qualifier domains.
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- Bug Fixes:
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- Resolved an issue in mapping algorithms to prevent overwriting results.
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- Fixed the `problems()` function to ensure blank values are excluded from warning messages when converting dates to ISO8601 format.
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- Corrected controlled terminology functions to alert users when a value cannot be mapped according to the controlled terms in `assign_ct()` and `hardcode_ct()` functions.
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With this release, users can now efficiently create the DM domain as well as various SDTM domains across Findings, Events, Findings About, Supplemental qualifiers and Intervention classes, significantly broadening the range of supported domains.
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# sdtm.oak V0.1.1
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Fixed the bug that was causing test cases to fail. Updated `derive_blfl()`
* Raw Data Structure: Data from different EDC systems come in varying structures, with different variable names, dataset names, etc.
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- Raw Data Structure: Data from different EDC systems come in varying
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structures, with different variable names, dataset names, etc.
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* Varying Data Collection Standards: Despite the availability of CDASH, pharmaceutical companies still create different eCRFs using CDASH standards.
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- Varying Data Collection Standards: Despite the availability of CDASH
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(Clinical Data Acquisition Standards Harmonization), pharmaceutical
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companies still create different eCRFs using CDASH standards.
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Due to the differences in raw data structures and data collection standards, it may seem impossible to develop a common approach for programming SDTM datasets.
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Due to the differences in raw data structures and data collection
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standards, it may seem impossible to develop a common approach for
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programming SDTM datasets.
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## GOAL
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`{sdtm.oak}` aims to address this issue by providing an EDC-agnostic, standards-agnostic solution. It is an open-source R package that offers a framework for the modular programming of SDTM in R. With future releases; it will also strive to automate the creation of SDTM datasets based on the metadata-driven approach using standard SDTM specifications.
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'sdtm.oak' aims to address this issue by providing an EDC-agnostic,
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standards-agnostic solution. It is an open-source R package that offers
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a framework for the modular programming of SDTM in R. With future
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releases; we plan to develop a code generation feature based on a
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standardized SDTM specification format, which has the potential to
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automate the creation of SDTM datasets.
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## Scope
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Our goal is to use `{sdtm.oak}` to program most of the domains specified in SDTMIG (Study Data Tabulation Model Implementation Guide: Human Clinical Trials) and SDTMIG-AP (Study Data Tabulation Model Implementation Guide: Associated Persons). This R package is based on the core concept of `algorithms`, implemented as functions capable of carrying out the SDTM mappings for any domains listed in the CDISC SDTMIG and across different versions of SDTM IGs. The design of these functions allows users to specify a raw dataset and a variable name(s) as parameters, making it EDC (Electronic Data Capture) agnostic. As long as the raw dataset and variable name(s) exist, `{sdtm.oak}` will execute the SDTM mapping using the selected function. It's important to note that `{sdtm.oak}` may not handle sponsor-specific details related to managing metadata for LAB tests, unit conversions, and coding information, as many companies have unique business processes. With subsequent releases, strive to automate SDTM creation using a metadata-driven approach based on a standard SDTM specification format.
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Our goal is to use 'sdtm.oak' to program most of the domains specified
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in SDTMIG (Study Data Tabulation Model Implementation Guide: Human
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Clinical Trials) and SDTMIG-AP (Study Data Tabulation Model
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Implementation Guide: Associated Persons). This R package is based on
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the core concept of `algorithms`, implemented as functions capable of
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carrying out the SDTM mappings for any domains listed in the CDISC
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SDTMIG and across different versions of SDTM IGs. The design of these
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functions allows users to specify a raw dataset and a variable name(s)
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as parameters, making it EDC (Electronic Data Capture) agnostic. As long
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as the raw dataset and variable name(s) exist, 'sdtm.oak' will execute
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the SDTM mapping using the selected function. It’s important to note
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that 'sdtm.oak' may not handle sponsor-specific details related to
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managing metadata for LAB tests, unit conversions, and coding
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information, as many companies have unique business processes.
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## This Release
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With the V0.2.0 release of 'sdtm.oak' users can now efficiently
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create the DM domain and various SDTM domains, encompassing Findings,
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Events, Findings About, and Intervention classes. However, the V0.2.0 release does NOT cover Trial Design Domains, SV (Subject Visits), SE (Subject Elements), RELREC (Related Records), Associated Person domains, or the EPOCH Variable across all domains.
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## Road Map
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This Release: The V0.1.0 release of `{sdtm.oak}` users can create the majority of the SDTM domains. Domains that are NOT in scope for the V0.1.0 release are DM, Trial Design Domains, SV, SE, RELREC, Associated Person domains, and EPOCH Variable across all domains.
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Subsequent Releases:
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We are planning to develop the below features in the subsequent releases.\
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- Functions required to derive reference date variables in the DM domain.\
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- Metadata driven automation based on the standardized SDTM specification.\
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- Functions required to program the EPOCH Variable.\
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- Functions to derive standard units and results based on metadata.
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Subsequent Releases: We are planning to develop the below features in
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the subsequent releases.\
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- Metadata driven code generation based on the standardized SDTM
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specification.\
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- Functions required to program the Domains SV (Subject Visits), SE (Subject Elements) and the EPOCH Variable.\
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- Functions to derive standard units and results based on metadata.
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- Additional features to be developed based on the user feedback.
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## References and Documentation
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* Please go to [Algorithms](https://pharmaverse.github.io/sdtm.oak/articles/algorithms.html) article to learn about Algorithms.
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* Please go to [Create Interventions Domain](https://pharmaverse.github.io/sdtm.oak/articles/interventions_domain.html) to learn about step by step process to create an Events domain.
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* Please go to [Create Findings Domain](https://pharmaverse.github.io/sdtm.oak/articles/findings_domain.html) to learn about step by step process to create a Findings domain.
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* Please go to [Path to Automation](https://pharmaverse.github.io/sdtm.oak/articles/study_sdtm_spec.html)
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to learn about how the foundational release sets up the stage for automation.
We ask users to follow the mentioned approach and try `{sdtm.oak}` to map any SDTM domains supported in this release. Users can also utilize the test data in the package to become familiar with the concepts before attempting on their own data. Please get in touch with us using one of the recommended approaches listed below:
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We ask users to follow the mentioned approach and try 'sdtm.oak' to map
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any SDTM domains supported in this release. Users can also utilize the
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test data in the package to become familiar with the concepts before
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attempting on their own data. Please get in touch with us using one of
We thank the contributors and authors of the package. We also thank the CDISC COSA for sponsoring the `{sdtm.oak}`. Additionally, we would like to sincerely thank the volunteers from Roche, Pfizer, GSK, Vertex, and Merck for their valuable input as integral members of the CDISC COSA - OAK leadership team.
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We thank the contributors and authors of the package. We also thank the
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CDISC COSA for sponsoring the 'sdtm.oak'. Additionally, we would like to
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sincerely thank the volunteers from Roche, Pfizer, GSK, Vertex, and
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Merck for their valuable input as integral members of the CDISC COSA -
An EDC (Electronic Data Capture systems) and Data Standard agnostic solution that enables the pharmaceutical programming community to develop CDISC (Clinical Data Interchange Standards Consortium) SDTM (Study Data Tabulation Model) datasets in R. The reusable algorithms concept in 'sdtm.oak' provides a framework for modular programming and also can automate SDTM creation based on the standard SDTM spec.
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An EDC (Electronic Data Capture systems) and Data Standard agnostic
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solution that enables the pharmaceutical programming community to
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develop CDISC (Clinical Data Interchange Standards Consortium) SDTM
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(Study Data Tabulation Model) datasets in R. The reusable algorithms
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concept in 'sdtm.oak' provides a framework for modular programming. We
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plan to develop a code generation feature based on a standardized SDTM
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+
specification format, which has the potential to automate the creation
- Raw Data Structure: Data from different EDC systems come in varying
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-
structures, with different variable names, dataset names, etc.
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+
-Raw Data Structure: Data from different EDC systems come in varying
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+
structures, with different variable names, dataset names, etc.
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-
- Varying Data Collection Standards: Despite the availability of CDASH (Clinical Data Acquisition Standards Harmonization), pharmaceutical companies still create different eCRFs using CDASH standards.
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+
- Varying Data Collection Standards: Despite the availability of CDASH
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+
(Clinical Data Acquisition Standards Harmonization), pharmaceutical
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+
companies still create different eCRFs using CDASH standards.
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Due to the differences in raw data structures and data collection
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standards, it may seem impossible to develop a common approach for
@@ -43,9 +58,9 @@ programming SDTM datasets.
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'sdtm.oak' aims to address this issue by providing an EDC-agnostic,
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standards-agnostic solution. It is an open-source R package that offers
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a framework for the modular programming of SDTM in R. With future
46
-
releases; it will also strive to automate the creation of SDTM datasets
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-
based on the metadata-driven approach using standard SDTM
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-
specifications.
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+
releases; we plan to develop a code generation feature based on a
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+
standardized SDTM specification format, which has the potential to
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+
automate the creation of SDTM datasets.
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## Scope
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@@ -62,57 +77,60 @@ as the raw dataset and variable name(s) exist, 'sdtm.oak' will execute
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the SDTM mapping using the selected function. It’s important to note
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that 'sdtm.oak' may not handle sponsor-specific details related to
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managing metadata for LAB tests, unit conversions, and coding
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-
information, as many companies have unique business processes. With
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-
subsequent releases, strive to automate SDTM creation using a
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-
metadata-driven approach based on a standard SDTM specification format.
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+
information, as many companies have unique business processes.
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## Road Map
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## This Release
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This Release: The V0.1.0 release of 'sdtm.oak' users can create the
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majority of the SDTM domains. Domains that are NOT in scope for the
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V0.1.0 release are DM (Demographics), Trial Design Domains, SV (Subject Visits), SE (Subject Elements), RELREC (Related Records), Associated
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Person domains, and EPOCH Variable across all domains.
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With the V0.2.0 release of 'sdtm.oak' users can now efficiently
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create the DM domain and various SDTM domains, encompassing Findings,
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Events, Findings About, and Intervention classes. However, the V0.2.0 release does NOT cover Trial Design Domains, SV (Subject Visits), SE (Subject Elements), RELREC (Related Records), Associated Person domains, or the EPOCH Variable across all domains.
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## Road Map
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Subsequent Releases: We are planning to develop the below features in
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-
the subsequent releases.
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-
- Functions required to derive reference date variables in the DM
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-
domain.
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-
- Metadata driven automation based on the standardized SDTM
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-
specification.
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-
- Functions required to program the EPOCH Variable.
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-
- Functions to derive standard units and results based on metadata.
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+
the subsequent releases.\
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+
- Metadata driven code generation based on the standardized SDTM
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+
specification.\
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+
- Functions required to program the Domains SV (Subject Visits), SE (Subject Elements) and the EPOCH Variable.\
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+
- Functions to derive standard units and results based on metadata.
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+
- Additional features to be developed based on the user feedback.
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