Performed Data analysis, statistical analysis, generated reports, listings and graphs using SAS/Base, SAS/Macro and SAS/Access, SAS/ODS.

Analyzed data using SAS Statistical Procedures such as Proc Means, Proc Tabulate, Proc Freq, Proc Summary.

Prepared new datasets from raw data files using Import Techniques and modified existing datasets using Data Steps, Set, Merge, Sort, and Update, Formats, Functions and conditional statements.

Developed numerous SAS programs to create summaries and listings.

Extensively Used SAS/ODS for generating different output formats as requested.

Responsible for defining variables, merging datasets, creating derived datasets, data validation before processing.

Worked on generating raw datasets on CDISC SDTM standards and analysis datasets on ADAM Standards.

Involved in Statistical programming and validation of Analysis Datasets and Tables, Listings, Files (TLF'S).

PROJECTS

• CRF Annotations
- Inspected whether all inputs are available.
- Checked whether documentation is available.
- Annotated CRF independently by using requirement specifications
- Annotated the CRF provided for the possible domains using SDTM IG 3.4 as a reference.
- Scanned for the fields available in CRF and to which SDTM variable it is mapped, and annotate accordingly.

• SDTM Dataset Development
- Created a Demography Dataset according to specifications using SDTM IG 3.4 Structure –One record per subject.
with appropriate ISO8601 date formats and informats
- Handled missing values
- Used various mathematical and equational operations to determine particular variable values

• Analysis Data Development
- Created ADaM dataset and metadata standards that support:
 efficient generation, replication, and review of clinical trial statistical analyses, and
 traceability among analysis results, analysis data, and data represented in the SDTM.
 facilitate clear and unambiguous communication
 be readily usable by common software tools
 be accompanied by metadata
 be analysis-ready
- Imported sorted and merged raw data files of various analysis datasets(lb,adsl,adlb)
- Derived various numeric variables like AVISITN for analysis as well as derived base variable using proc SQL
- Created Criteria variables and Criteria flags using conditional processing and do loops
- Created final dataset using keep retain and label statement according to requirement-specific document

• Validation of Analysis Dataset
- Worked on vital sign data and derived parameters were used for descriptive statistics of vital signs and flag variables
- Derived additional variables for criteria for values outside normal ranges
- Sorted the final data according to the specification
- Compared output in the form of listing file as well as created a validation findings document using the template provided

• Data Integration for Pharmacokinetic Analysis
- Integrated PK concentration data and corresponding Pharmacokinetic (PK) data into a clinical database using a logical
flow of steps.
- The project involved reconciling the BAPK excel sheet with the clinical database by converting all raw datasets into SAS
datasets and extracting formats from format catalogs.
- The resulting datasets were then merged using subject number, center number, and other relevant variables.
- The PK merging checklist was followed to ensure accuracy and completeness of the integrated data.
- The resulting dataset was named PKDAR and included information on subject demographics, baseline vital sign values,
dose administration details, and blood collection logs.

• Demographic Report Generation
- Generated a demography report by writing independent code using the analysis dataset for demography.
- Conducted data sorting, calculations, and formatting as per the mockup table.
- Created ODS statements to generate RTF output and used PROC REPORT to generate the final report.
- The project was classified as a Dataset Report titled "Report Generation".

• Report Validation
- Checking the developer's RTF reports for formatting and writing independent SAS code to generate the dataset for
validating the RTF reports/datasets.
- Used input datasets as per the requirement and create the final dataset.
- Compared the final dataset with the developer's report dataset and generated listings of compare result, which I store as
the output.
- Validated RTF reports by writing independent SAS code.

• Report Generation('Summary of Physical examination (Safety Analysis Set)'
- This project involves calling the initial.sas to set up the directory structure, calculating the number of subjects in Safety
Analysis Set in ADSL dataset, and storing it in a macro variable.
- Sorting the ADSL dataset by subject and saving sorted data in adsl, creating PE dataset from Physical Examination
dataset by selecting only 'Body System' data, and sorting PE dataset by subject.
- Furthermore, I create a dataset aepe by merging PE with adsl for matching subjects in both the datasets, storing the total
number of patients in Safety Analysis Set and save it in a macro variable.
- I then calculate the following statistics for baseline visits and Week Day 8 visit:
 number of subjects with Normal findings
 number of subjects with Abnormal findings
 number of Clinically Significant cases
 number of Clinically insignificant cases.
- Finally, I append all output datasets, apply formatting as defined in the mock table, use ODS statements to create RTF
output, write footnotes and titles as mentioned in the mock table, define PROC REPORT, and generate the report.

• Report Generation(‘Summary of Dose limiting toxicities (Safety Population)’)
- Calculated the number of subjects in the Safety Analysis Set in the ADSL dataset, and storing it in a macro variable.
- Sorted the ADSL dataset by subject and save the sorted data in work library adsl dataset.
- Sorted the DL dataset by subject and save the sorted data in dl dataset, sort IP dataset by subject and IPDTC, and save the
Sorted data in the ip dataset. created a dataset dl2 by merging dl with adsl for matching subjects in both the datasets.
- Calculated the count of dose-limiting toxicities, calculate the percentage for the dose-limiting toxicities, and calculate the
95% CI using the exact binomial confidence interval method. I repeat these steps for Dose level of 2ml, 4ml, and 6ml.
- Appended all output datasets, apply formatting as defined in the mock table, use ODS statements to create RTF output,
write footnotes and titles as mentioned in the mock table, define PROC REPORT, and generate the report.

• Report Generation(‘Analysis of Response Criteria for Tumour Assessment (PP Population)’)
- Sorted the ADSL and RS datasets by subject and saved the sorted data in a temporary library respectively. Then, I merged
the rs with adsl for matching subjects in both datasets and created the adrs dataset.
- To calculate the frequency statistics, I calculated the number of patients with complete response (CR), partial response
(PR), stable disease (SD), progressive disease (PD), and overall response rate (ORR).
- Stored the total number of patients in the PP population set in a macro variable and calculated the 95% confidence
interval for ORR by the exact binomial confidence intervals method.
- Appended all output datasets and applied formatting as defined in the mock table.
- To create the RTF output, I used ODS statements, wrote footnotes and titles as mentioned in the mock table, defined
PROC REPORT and generated the report.
- Finally, I deleted all temporary SAS datasets to clean up the workspace.

• Report Generation (Summary of Medical / Surgical History)
- Generated a report in rtf format for medical and surgical history population for Enrolled Population set from medical
history and Subject Level Analysis dataset.
- Calculated the following statistics:
 Frequency and percentages for patients with any medical history
 Frequency and percentages classified by system organ class
 Frequency and percentages classified by Preferred Term associated with respective System Organ Class.
- Appended all output datasets and applied all necessary formats for final output