Trinath Panda

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Creation of BDS Datasets- Day 05 of Onco-ADaM Project

Introduction The Basic Data Structure (BDS) is a fundamental element within the Analysis Data Model (ADaM), which is a significant component of the Clinical Data Interchange Standards Consortium (CDISC). BDS is the standard domain structure in ADaM, designed to facilitate the creation of analysis-ready datasets. This structure allows for one or more records per subject per analysis parameter per analysis time point. All the BDS datasets usually follow same variables, with a sprinkle of dataset specific suffix's. Examples are ADVS, ADLB, ADEG. Some of the common variables in BDS datasets Common variables in BDS datasets: STUDYID USUBJID SUBJID SITEID TRTP TRTPN TRTA TRTAN SCRNFL ENRLFL ITTFL SAFFL PKFL ADT ADTM ATM ADY VISIT VISITNUM AVISIT AVISITN PARAM PARAMCD AVAL DTYPE ABLFL BASE CHG EPOCH APHASE ANL01FL —ORRES —STRESC —STRESN —STRESU —SEQ —DTC —DY AVALCAT01 variables in ADLB dataset: LBSTAT LBNAM LBSPEC LBNRIND LBSTNRLO LBS...

Creation of OCCDS-ADAE Dataset- Day 04 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 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 ...

Date & Time Imputation Variable

Introduction In this post, I will elucidate the concept of Date and Time imputation. Date imputation involves the process of converting a partial date in ADCM, ADAE, and ADMH datasets to complete dates. This is necessary because the data are often collected based on subjects' memory, which can lead to incomplete entries. While in SDTM, the original data are retained, in ADaM, it becomes a critical step in data analysis. In dealing with Date and Time imputation, there exist numerous rules, particularly with regard to SAP requirements for date imputation in studies. Typically, the start and end dates and times are imputed. Furthermore, We always convert the character date into numeric when programming for ADaM. Before delving into this topic, I want to clarify that I haven't had exposure to time imputation yet. However, I assure you that I will provide insights on it as I gain exposure. Let's proceed with the discussion. For Start Date (—STDTC) If the day is missing ...

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. Concomitant Med Related Variable CMTRT CMDECOD CMCAT CMSCAT CMINDC CMDOSE CMDOSU CMDOSFEQ CMROUTE CMSTDTC CMENDTC Analytical Therapeutical Chemical (ATC) code ATC1C ATC1T Date, Time & Impu...

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...