Inactive

See attached JOFOC. Summary below. The contractor is expected to coordinate data management/analysis and laboratory coordination activities amongst all seven recipients at the direction of the ATSDR P... See attached JOFOC. Summary below. The contractor is expected to coordinate data management/analysis and laboratory coordination activities amongst all seven recipients at the direction of the ATSDR PI and in accordance with the approved protocol. • Data management includes variable review, manipulation, creation of derived variables per ATSDR specification and implementation of the QA/QC procedures with the goal to build the study dataset that has research variables to be used in the statical data analyses of data preparation of reports and study peer-reviewed manuscripts. Increased competition • The raw data capture often results in variables that are not useful for data analyses. The goal of the task is to create comprehensive data with all variables properly labeled and derived variable descriptions and notations which shall be captured in the MSS data dictionary. The draft data dictionary is a ‘live’ document that the contractor shall update in coordination and under direction of the ATSDR until the document is finalized. The MSS data dictionary shall be based on Pease data dictionary and adjusted as needed in coordination with ATSDR. • The contractor shall assign well qualified and properly trained personnel with experience in data management of large epidemiologic studies using extensive exposure data, health outcomes, and clinical test and biomarkers data. In addition, the contractor should plan for extensive labor requirements as the data captured by implementing the core protocol and analyzing collected biospecimen generate about 4,500 variables. There are also about 2,000 derived variables to be created and modified per ATSDR specifications, as well as an estimated several hundred site specific variables for each of seven recipients. Insight on effective contract structure • Proficiency in SAS and/or R programming and experience with data management tasks is essential to complete these tasks. • Insight on metrics related to cost, schedule, and performance in the delivery of services (e.g., considerations such as adequate application of skill mix, efficiency in processes to establish cost avoidance, and reduced rework) • The Contractor shall follow data management and QC and QA plans to be provided by CDC/ATSDR. Additional QA/QC checks may be performed by CDC/ATSDR. A quality control checklist was provided to the Contractor. The Contractor shall be responsible for resolving issues with missing or incorrect data and periodically (monthly) reviewing the nature and pattern of QC check with ATSDR. The Contractor shall program study instruments in REDCap and adjust data quality checks that allow for correction of errors or inconsistencies as needed and as directed by ATSDR. • As data collection is ongoing, the Contractor shall also conduct key diagnostic checks regularly, including batch running data quality rules to identify discrepancies and creating and running customized SAS code or R scripts that check for missing, inconsistent, or erroneous values in key study variables • The Contractor shall then run the data through a customized R script or SAS program before each data delivery to clean data. • Data cleaning steps shall include applying formats and labels, recording values (as needed), and creating derived variables as requested by CDC/ATSDR.