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FeaturedAbout Candidate
As a future clinical SAS programmer, I offer hands on training and practical SAS programming experience to facilitate efficient clinical data analysis. Skilled in using data modification and validation to turn unprocessed data into meaningful insights. I’m eager to use my abilities in a demanding position that promotes clinical research. My commitment to precision and astute observation of details position me as a results-oriented individual prepared to make significant contributions to the industry.
Technical Project Experience
Meticulously carried out pharmacokinetic (PK) analysis, using efficient data standardisation strategies to ensure accuracy. skillfully combined and verified unprocessed laboratory data, using variable coding in the ADaM dataset and sorting/merging techniques. Led the creation of SDTM domain datasets and coordinated the development of DM datasets, ensuring adherence to CDISC principles and producing standardised demographic data. Demonstrated ability to precisely align CDISC terminology, the SDTM Guide, the Analysis Plan, and the Study Protocol while annotating Blank CRFs. oversaw the process of combining subject-level analysis data with raw lab data to create the Laboratory Analysis Dataset (ADLB), employing structured mapping for variable coding.
Education
The path I took to earn my bachelor's degree in pharmacy was an exciting one that set the groundwork for my thorough comprehension of the pharmaceutical sciences. My direct involvement in a clinical trial aimed at examining the bioavailability of a novel medicine formulation was a crucial experience. I made a significant contribution to the pharmacokinetic and pharmacodynamic evaluations and obtained invaluable practical expertise in gathering and interpreting clinical data. Through this comprehensive experience, which highlighted the crucial interaction between pharmacokinetics and pharmacodynamics, I was also exposed to the complexities of drug development and gained a deeper grasp of clinical research procedures.
Work & Experience
I carefully analyzed and annotated the Clinical Research Form for the Blank CRF Annotation Project, placing a premium on quality and completeness to enable seamless integration into the research database. My constant commitment to accuracy greatly increased the data's dependability, strengthening the groundwork for strong clinical study results. I was crucial in maintaining the accuracy of the data, adhering to the strictest guidelines of CDISC, and boosting the project's overall performance with my meticulous attention to detail. My professional approach to furthering the field of clinical research is emphasized by my unwavering commitment to excellence.
For the SDTM domain dataset development program, I conducted the meticulous generation of the DM dataset, assuring the exact structuring and organization of clinical trial data in full adherence with rigorous CDISC requirements. The end output is a standardized and comprehensive representation of demographic data, precisely constructed to fulfil specific specifications. This endeavor demonstrates my commitment to upholding the highest industry standards, thereby contributing to the overall integrity and excellence of the clinical trial data infrastructure.
I took the lead in directing the production of the Laboratory Analysis Dataset (ADLB) by effectively merging raw lab data (LB.sas7bdat) with subject-level analysis data (ADSL.sas7bdat). I showed proficiency by painstakingly structuring and mapping laboratory tests based on the provided list (labtests.xls) and parameter information (adlb_paramcd.csv). My expertise in data transformation and dataset construction enabled a seamless generation that emphasized precision and conformity to industry norms. This strategic endeavor demonstrated not only my ability to orchestrate complicated data integration procedures but also my dedication to delivering high-quality, well-structured datasets required for extensive analysis in the fields of laboratory analysis and clinical research.
Demonstrated capacity to acquire data for Pharmacokinetic (PK) analysis, use efficient methods for integrating data, and maintain accuracy and reliability at all times. The combined effort produced a significant dataset that had been meticulously created for vital and insightful PK investigation.