Project 1:- CRF Annotation

• Annotated CRF independently by using requirement specifications
• Annotated the CRF provided for the possible domains using SDTM IG 3.2 as a reference.
• Checked for the fields available in CRF and to which SDTM variable it is mapped, and annotate accordingly.
• Annotated every variable as domain name followed by the variable name e.g. DM.BRTHDTC for birth date field of demographics (DM) domain.
• Followed CDISC-controlled terminologies, Codelist, and formats referring to SDTMIG.

Project 2:- SDTM Project

• Sorted the following datasets by subject: DM_IN.sas7bdat DS.sas7bd INCO.sas7bdat RAND.sas7bdat RD. .sas7bdat TA. .sas7bdat
• Created DM dataset, add the variables in the dataset, and assign values as specified in the DM_Specs.xlsx.
• Kept only the variables listed in the DM_Specs.xlsx.
• Sorted by USUBJID (Column order in which the output should be sorted.)

Project 3:- Analysis Dataset Development

• Selected only the required lab tests that are mentioned in the labtests.xls sheet from the LB dataset.
• Merged the lab data with ADSL and took all the records from the lab dataset only.
• Created new LAB codes, (for each unique combination of LBCAT, LBSPEC, LBTESTCD, and LBCONU) both character (PARAMCD) and numeric (PARAMEN).
• Derived visit information (AVISIT) as follows – for nonmissing LBDY.

Project 4:- Validation for Analysis Dataset Development

• Sorted the AIDENT and APRDTRT datasets by SUBJID STUDYID and USUBJID.
• Merged the datasets by SUBJID STUDYID and USUBJID.
• Removed the duplicate observations from the merged dataset AIVS.
• Transposed AIVS by all the variables and made sure that there is no missing observations.
• Written independent codes for the calculations for VSNCODES.
• Written independent codes for the flags to be generated in the output dataset.

Project 5:- Data Integration for Pharmacokinetic Analysis

• Converted the BAPK excel sheet into a SAS dataset and converted all the input SAS transport data sets into SAS datasets (.sas7bdat).
• Merged the BAPK SAS dataset with DMG dataset by subject number and center number as PKDMG.
• Checked that all records are matching and stored in resulting file.
• Merged the PKDMG dataset with VSN dataset by subject number and center number as PKDMGV.
• Checked that all records are matching and stored in resulting file.
• Merged the PKDMGV dataset with BCL dataset by subject number, center number and sample number as PKBCL.
• Before merging, checked the uniqueness of the variables used for identification of samples (e.g. subject, PK collection number, sampling number).
• Checked that all records are matching and stored in resulting file.
• Merge the PKBCL dataset with DAR dataset by subject number, center number and cycle (noted that the variable REC1N from DAR is equivalent to cycle) as PKDAR.