N********************l
FeaturedAbout Candidate
Dear sir/madam,
I am Nirmal Patil, a clinical trial data analyst with a background in Chemistry. I have already upgraded my skills in base and advanced SAS programming, R Programming and Python. I am committed to leveraging my knowledge and expertise to contribute to the advancement of medical research and innovation.
Technical Project Experience
(1)Project Name: Annotation CRF Description: The Annotation CRF project involves annotating submitted datasets to identify the origin of data variables. The annotations need to comply with the SDTM (CDISC) standards. Each unique occurrence of submitted data should be annotated on the Case Report Form (CRF). Additionally, supplemental qualifier variables should be annotated, such as specifying details for 'Other' in the Race variable and including it in the SUPPDM dataset. Repetitive pages, such as Vital signs and Labs, should refer to the original page for annotations.
Roles and Responsibilities:
Annotates the submitted datasets based on SDTM guidelines.
Performs quality checks on the annotations to ensure accuracy and adherence to SDTM standards.
(2) Project Name: Development of SDTM Domain Dataset Description: The project involves creating an SDTM Demography domain dataset by writing independent code in SAS. The dataset is created based on the SDTM IG 3.4, using raw datasets such as DM_IN (for demographic), DS (disposition), RAND (randomization), INCO (informed consent), and TA (trial arm). Some information is derived, and variables are arranged as per SDTM DM.
Roles and Responsibilities:
Writes SAS code to create the SDTM domain dataset based on the provided raw datasets and SDTM IG specifications.
Performs data manipulation and derivation of variables required for the SDTM domain dataset.
Performs quality checks on the created dataset to ensure adherence to SDTM standards.
(3) Project Name: Development of Analysis Dataset ADDCTDA 1.1 Description: The project involves generating a Laboratory Analysis Dataset (ADLB) using raw data from LB. The dataset is used to visualize the change of lab test results based on the overall lab test distribution for the study. Baseline, change, percent change, and visit information are derived from SDTM.LB.
Roles and Responsibilities:
Generates the Laboratory Analysis Dataset (ADLB) by using the raw LB data and deriving the necessary variables.
Performs quality checks on the analysis dataset to ensure accuracy and adherence to requirements.
(4) Project Name: Analysis Dataset Validation Description: The project involves comparing a newly created analysis dataset with a given dataset and checking for differences. Any discrepancies are logged in the validation log. Variables in the analysis dataset include both direct data from the raw dataset and derived variables. In the final step, only required variables are kept and aligned with the compared dataset.
Roles and Responsibilities:
Compares the newly created analysis dataset with the given dataset, identifies differences, and logs them in the validation log.
Performs quality checks on the analysis dataset and the validation log to ensure accuracy.
(5) Project Name: Data Integration for Pharmacokinetic Analysis
Roles and Responsibilities:
Integrates PK concentration data and PK data from various sources, converts files into SAS datasets, and performs the merging process.
Derives new variables, performs data manipulation, and prepares datasets for integration.
Performs quality checks on the integrated dataset and ensures alignment with regulatory requirements.
(6) Project Name: Generating Reports Description (Report 1 - Demography Report): The project involves generating a demography report using independent SAS code and the analysis dataset for demography. The report calculates statistical measures such as n (count), mean, standard deviation, median, minimum, and maximum for the age variable. It also calculates counts and percentages for age group, gender, and race and generates a total column for each filter. The report applies formatting as per the mock shell and is generated in RTF format using the ODS statement.
Roles and Responsibilities:
Writes independent SAS code to generate the demography report based on the analysis dataset and raw dataset. Performs calculations and applies formatting as requirenents.
Performs quality checks on the report to ensure accuracy and adherence to requirements.
(7) Project Name: Advanced Report Generation 1 Description: The project involves generating the 'Summary of Physical Examination (Safety Analysis Set)' report using independent SAS code. The report includes statistics such as the number of subjects with normal findings, abnormal findings, clinically significant cases, and clinically insignificant cases for baseline and week day 8 visits. The report applies formatting as per the mock shell.
Roles and Responsibilities:
Writes independent SAS code to generate the 'Summary of Physical Examination' report based on the specified criteria and analysis datasets.
Performs quality checks on the report to ensure accuracy and adherence to requirements.
Education
pct : 63
pct : 70
Work & Experience
During my internship, I have received comprehensive training in SAS base and advanced programming, equipping me with the necessary tools to effectively analyze and manipulate clinical trial data. Additionally, I have completed specialized training in CDISC (Clinical Data Interchange Standards Consortium) standards, which are widely used in the industry for standardized clinical data management. As part of my traineeship experience, I have actively contributed to projects involving CDISC SDTM (Study Data Tabulation Model) and ADaM (Analysis Data Model) standards. These projects have allowed me to gain hands-on experience in implementing data standards and ensuring compliance in clinical trial data analysis. One of my notable achievements during my traineeship has been the successful generation of TLF (Table, Listing, and Figure) reports. These reports are crucial for presenting summarized and meaningful insights from clinical trial data, enabling stakeholders to make informed decisions. Overall, my traineeship with Kite AI Technologies has provided me with a solid foundation in clinical data analysis, SAS programming, CDISC standards, and report generation. I am grateful for the opportunity to learn and grow in a professional environment.