ADaM, short for Analysis Data Model, transforms raw data into analysis-ready datasets, crucial for clinical research. While SDTM standardizes data, ADaM prepares it for analysis. Dive into the world of ADaM with me! Understanding its three key structures: 1. ADSL (Subject Level Analysis Dataset) ADSL: The backbone of analysis. 2. BDS (Basic Data Structure) ADLB, ADEX, ADEG, and ADVS: Building blocks for insightful analysis. 3. OCCDS (Occurrence Data Structure) ADCM, ADAE: Uncover critical occurrences for thorough analysis. To kickstart ADaM programming, essential documents include: 1. ADaM Implementation Guide ( ADaMIG ) 2. Protocol : The blueprint of study design and objectives. 3. CRF : Capturing crucial data points. 4. SAP : Guiding the analysis journey. 5. Mockup Shells : Visualizing dataset structures. 6. ADaM Specification : Navigating dataset creation. 7. Raw SDTM dataset : The foundation of analysis. In this project, armed with these documents, I started the journey by: 1. Prot...
Introduction
This is a statistical analysis data structure that describes the general data structure and content typically found in occurrence analysis.
For example like adverse events, concomitant medications, and medical history data structure.
Some required variables for OCCDS are:
- Identifier Variables: STUDYID, USUBJID, — SEQ
- MedDRA Dictionary Coding Variables: — TERM
- Adverse Event Descriptive Variables: AESER(Serious Event)
- WHO Drug Dictionary Coding Variables: CMTRT
ADAE stands for Adverse Events Analysis Dataset. The base datasets for ADAE is ADaM.ADSL, SDTM.AE, & SDTM.SUPPAE.
Variables
I am not including the general variables like USUBJID, SITEID, AGE, SEX, etc., and also not from ADSL. Note that the variables i mention is not limited to this.
- Adverse Event related variables
- AESPID
- AETERM
- AEDECOD
- ADECOD
- AEBODSYS
- AEBDSYCD
- AESTDTC
- AEENDTC
- AEACN
- AESHOSP
- AECONTRT
- AEREL
- AREL
- AEOUT
- AEENRF
- ASEV
- ASEVN
- AETOXGR
- AEDLTTOX
- AECMEDID
- Date & Time Imputation
- ASTDT
- ASTDTF
- AENDT
- AENDTF
- Study Day Variable
- ASTDY
- AENDY
- Flag Variables
- TRTEMFL
- FUPFL
Steps:
- Identify the SDTM datasets required for programming ADAE.
- Mark the variables that is carry forwarded from the ADSL dataset.
- Identify the variables that is as same as in SDTM datasets
- Transposing Supplemental dataset
- Create a variable AESEQ with the value of IDVARVAL in numeric format.
- Sort the above dataset by USUBJID and AESEQ
- Transpose the supplementary dataset.
- Merge with the main dataset AE.
- Merge the ADAE to ADSL dataset.
- Now create all the variables that are that is same but just the variable name is different. (for ex., ADECOD=AEDECOD)
- Creating Causality and severity related variable
- Date & Time and Imputation variable
- Creation of start date with imputation of partial date
- Creation of end date with imputation of partial date
- Creation of Study day variable
- Study start day variable
- Study end day variable
- Creating flag variables
- Creating Treatment Emergent Flag
- Creating Follow-up flag
- Final, keeping required variable and saving the file to the destination.
Key-notes
- If any AE is started on or after the treatment start date, or on or after, a specific date of, treatment end date, we populate the treatment emergent flag as ‘Y’.
- If any appear after the treatment end date, then we populate the follow-up flag as ‘Y’.
Important
If this the first post you read, then i highly recommend to read my last blogs on this series, link is below down:
Thank You for Visiting :)
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