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What is ADaM- Day:01 of Onco-ADaM project

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...

Creation of OCCDS-ADCM Dataset - Day:03 of Onco-ADaM Project

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

ADCM stands for Concomitant Medications Analysis Dataset. The base datasets for this OCCDS is ADaM.ADSL and SDTM.CM.

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.

  1. Concomitant Med Related Variable
    • CMTRT
    • CMDECOD
    • CMCAT
    • CMSCAT
    • CMINDC
    • CMDOSE
    • CMDOSU
    • CMDOSFEQ
    • CMROUTE
    • CMSTDTC
    • CMENDTC
  2. Analytical Therapeutical Chemical (ATC) code
    • ATC1C
    • ATC1T
  3. Date, Time & Imputation
    • ASTDT
    • ASTDTF
    • AENDT
    • AENDTF
  4. Study Day Variable
    • ASTDY
    • AEENDY
  5. Flag Variables
    • PRIORFL
    • CONCOMFL
    • FUPFL

Basic Process

First identify the dataset required, usually ADaM.ADSL and SDTM.CM. If there any SUPPCM dataset then transpose it and merge with CM and start creating variables that is just carry forwarded from the raw with just other name and just follow the steps mentioned below.

Finally keep all the required variables and save it to the destination location.

Steps:

  1. Identify the SDTM datasets required for programming ADCM.
  2. Mark the variables that is carry forwarded from the ADSL dataset.
  3. Identify the variables that is as same as in SDTM datasets

From here the programming starts

  1. ATC (Analytical Therapeutical Chemical) code variables & WHO-DD version variable usually present in the SUPPCM datasets.
    1. Create a rename the IDVAR value to, which is also, CMSEQ (Also act as a common variable).
    2. Sort by USUBJID & CMSEQ.
    3. Transpose the SUPPCM dataset.
    4. merge them with the CM dataset with common variables.
    5. Assign the ATC level code variables and WHODDVR variable.
  2. Merge the CM with the ADSL and create the necessary variables (I call them renaming variables).
  3. Date & Time and Imputation variable
    1. Creation of start date with imputation of partial date
    2. Creation of end date with imputation of partial date
  4. Creation of Study day variable
    1. Study start day variable
    2. Study end day variable
  5. Creation of Medication flag
    1. Prior medication flag
    2. Concomitant medication flag
    3. Follow-up Medication flag
  6. Final keeping required variable and saving the file to the destination.

Key-notes

  • We create both start & End date imputation in OCCDS.ADCM.
  • Flags created in ADCM are, but not limited to: Prior Medication Flag (PRIORFL), Concomitant Medication Flag (CONCOMFL) & Follow-up Medication Flags (FUPFL).

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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Creation of ADSL Dataset - Day:02 of Onco-ADaM Project

 Introduction Hey everyone, welcome to day 2 of the Onco-ADaM project! Today, I'll be working on creating the ADSL dataset, which is similar to DM for SDTM. Let's keep it short and simple. ADSL stands for Subject-Level Analysis Dataset, containing one record per subject regardless of the clinical trial design. The main dataset for creating ADSL is SDTM.DM. We won't include every variable, as that's not the purpose of ADSL. It's designed to include variables necessary for analysis only. ADSL includes demographic information, key date variables, randomization factors, planned and actual treatment variables, subject-level population flag, subgrouping variables, and baseline values. Variables in ADSL I am not including the general variables like USUBJID, SITEID, AGE, SEX, etc. Note that the variables I mention is not limited to this. a. Key Date variables RFICDT (Informed Consent Date) RANNDT (Randomization Date) TRTSDT (Treatment Start Date) TRTEDT (Treatment...