Position Objective: Provide services as a Statistician in support of the overall functions of the National Institute on Minority Health and Health Disparities (NIMHD) within the National Institutes of Health (NIH). Under this task order, the contractor will independently provide epidemiologic and statistical services to satisfy the overall operational objectives of the NIMHD Division of Intramural Research. The primary objective is to provide services and deliverables through the performance of support services. We are seeking one full-time statistician/data analyst/programmer to support the research activities of the NIMHD Division of Intramural Research.
Duties and Responsibilities:
- Perform statistical analysis using novel methods and algorithms.
- Assist researchers with the planning, implementing, and analysis of research projects.
- Perform data analysis, including model building analysis, assessing trends, determining correlations, testing for heterogeneity, and compiling and communicating results to investigators to participate in the interpretation of results and planning of further analyses.
- Provide statistical advice and consultation to the investigators in study design, data management, choice and application of statistical methods, data analysis, and interpretation of statistical results.
- Carry out statistical analyses on issues via descriptive analyses, causal inference, predictive modeling, and other univariate and bivariate and multivariate analytic methods.
- Perform advanced epidemiologic and statistical analyses suitable for studies of health disparities and minority health including (but not limited to): linear and non-linear regression modeling; survival analysis; time series analysis; propensity score matching, weighting, standardization, multiple imputation, missing data weighting, censor weighting, and small area estimation to account for confounding, missing data, loss to follow up, selection bias, and other forms of potential bias in studies. 1
- Conduct statistical analyses; perform data cleaning and formatting, data harmonization, and data analyses; and prepare results for publication from intervention studies, observational studies, and secondary data analysis projects using complex survey data, hospital/medical records, administrative data, or other data sources.
- Advanced epidemiologic and statistical methods suitable for studies of health disparities and minority health including (but not limited to): linear and non-linear regression modeling; survival analysis; time series analysis; propensity score matching, weighting, standardization, multiple imputation, missing data weighting, censor weighting, and small area estimation to account for confounding, missing data, loss to follow up, selection bias, and other forms of potential bias in studies.
- Design and conduct statistical analyses using complex survey data or other secondary data sources that involve sampling weights (e.g., NHANES, BRFSS, National Health Interview Survey [NHIS], Medical Expenditure Panel Survey [MEPS], Current Population Survey and various supplements).
- Meet with data customers inside and outside the DIR to assess dataset requirements.
- Perform statistical analyses of large, complex datasets, preferably using SAS, for population health research using existing NIH and publicly available datasets or data collected by NIMHD investigators.
- Perform data programming, analysis and presentation by preparing charts, tables and graphs using software such as R, SAS and STATA. 2
- Ensure that all data products (dynamic reports, tables, and graphics) are reproducible from the original source data by maintaining clear, commented, and consistent code and organization of files and folders.
- Create interim dynamic reports that weave together text, code, output, tables and graphics and document all procedures and code used for data cleaning and analysis.
- Develop and systematically apply data classification schemes and process and combine data sets for analysis from diverse sources.
- Design and conduct statistical analyses using hospital/medical records, administrative data, and other primary and secondary data sources.
- Design and analyze studies using high-dimensional, longitudinal, clustered, multi-level, and repeated measures data.
- Design and conduct statistical analyses using complex survey data or other secondary data sources that involve sampling weights (e.g., NHANES, BRFSS, National Health Interview Survey [NHIS], Medical Expenditure Panel Survey [MEPS], Current Population Survey and various supplements).
- Develop and implement methods and procedures for the collection, processing, compilation, cleaning, and analysis of data in collaboration with DIR investigators and trainees.
- Research methods in data analysis, revise study forms, graphically display analytic results, collaborate in writing or editing drafts of manuscripts for publication.
- Provide a cross-tabulation, descriptive analysis using standard statistical procedures, rate standardization, stratification of data, and model building.
- Recommend appropriate statistical techniques for analysis of research data and prepare statistical reports, analyze data, and use statistical software packages and programs such as SAS and R.
- Implement and validate cutting-edge algorithms and new statistical methodologies to analyze diverse sources of data to answer research questions.
- Conduct statistical analyses; perform data cleaning and formatting, data harmonization, and data analyses; and prepare results for publication from intervention studies, observational studies, and secondary data analysis projects using complex survey data, hospital/medical records, administrative data, or other data sources. 3
- Generate tables and graphics for abstracts, manuscripts, and presentations.
- Prepare for publication, results from clinical trials, intervention studies, observational studies, and secondary data analysis projects using complex survey data, hospital/medical records, administrative data, or other data sources.
- Interpret and communicate results of analyses in written and oral formats.
- Conduct data collection/entry, management, cleaning, and manipulation activities.
- Creates Data Wokflow Processes.
- Ensure that appropriate variables are captured in the constructed databases.
- Format databases to allow merging of spreadsheets for statistical analyses and to optimize planned analyses.
- Record Data into a format appropriate for processing.
- Apply statistical techniques to produce meaningful tables and graphs using appropriate software.
- Provide support with data sharing, including public repositories.
- Work with staff to prepare and standardize data for the database.
- Perform routine and general data management.
- Prepare tables and figures from data analyses.
- Perform database searches and assemble datasets.
- Analyze studies using high-dimensional, longitudinal, clustered, multi-level, and repeated measures data.
- Clean, condense, merge, and reformat data into files that are appropriate for data analysis and data sharing, including preparing de-identified datasets and documentation for external users.
- Create variables as needed for analyses and document methods and definitions for all variables created (e.g., data dictionary).
- Transfer data between software, dataset creation (merge and concatenation), data cleaning (identify and correct data entry errors and missing values) and data transformation (create and categorize variables and impute data).
- Check and confirm the accuracy of calculations conducted by collaborating programmers, analysts, and presenters to guard against mistakes in design, conduct, or presentation of risk estimates.
- Collect and refine new data and refine existing data sources.
- Create data entry applications to improve data collection and management.
- Enhance data collection strategy and procedures for primary and secondary data sources, including recovered data sources such as scans and microfilms of paper archives.
- Perform data analysis of data sets involving statistical procedures varying in complexity from simple bivariate tests to advanced regression methods for longitudinal data analysis and time-to-event analysis; determine correlations between variables.
- Perform data analysis including cross-tabulation, descriptive analysis using standard statistical procedures, as well as model building (logistic regression, conditional logistic regression).
- Assist staff in conducting evaluations and analyses of programs using appropriate methods and tools and perform data management and carry out statistical analysis for assigned research projects.
- Process and analyze data using blind-source separation techniques.
- Organize, manage and design data files and plans for associated statistical analysis.
- Prepare and/or update data tables and figures, methods sections of manuscripts, reports, and other documents for presentation and/or publication. 4
- Take lead of the storage, tracking, internal review, and retrieval of information, documentation, and datasets for all assigned projects and projects of any subordinates. 5
- Perform data cleaning, formatting, variable recoding, data harmonization, and data quality checks, and data management and manipulation.
- Transfer data between software and create datasets (merge and/or concatenation), data cleaning (identify and correct data entry errors and missing values) and data transformation (create and categorize variables and impute data).
- Review literature and create bibliographies, research methods in data analysis, revise study forms, graphically display analytic results and collaborate with staff on writing and editing drafts of manuscripts for publication.
- Attend all lab meetings, lab check-ins, and other research-related meetings as requested by investigators or trainees.
- Report, either verbally and/or in writing, regular updates on the progress of their work to investigators.
- Provide expertise on epidemiologic and statistical research methods as needed for research projects, protocols, and proposals.
- Train trainees on developing statistical analytic codes to analyze quantitative data to achieve research objectives and interpreting results from different statistical analyses.
- Provide periodic training on contemporary epidemiologic and biostatistics analytics approaches to the NIMHD DIR.
- Provide expertise/advice on advanced study design, statistical analysis, and data presentation methods - Ad-Hoc
- Run Validation - Ad-Hoc
- Meet with lab members to present updates - Ad-Hoc
- Develop and implement methods and procedures for the collection, processing, compilation, cleaning, and analysis of data in collaboration with DIR investigators and research fellows - Ad-Hoc
- Perform data cleaning, formatting, variable recoding, data harmonization, and data quality checks, and data management and manipulation - Ad-Hoc
- Perform statistical analyses of large, complex datasets, preferably using SAS, for population health research using existing NIH and publicly available datasets or data collected by NIMHD investigators - Ad- Hoc
- Prepare for publication, results from clinical trials, intervention studies, observational studies, and secondary data analysis projects using complex survey data, hospital/medical records, administrative data, or other data sources - Ad-Hoc
- Interpret and communicate results of analyses in written and oral formats - Ad-Hoc
- Generate tables and graphics for scientific abstracts, manuscripts, and presentations - Ad-Hoc
- Prepare and/or update data tables and figures, methods sections of manuscripts, reports, and other documents for presentation and/or publication - Ad-Hoc
- Manage the storage, sections of manuscripts, reports, and other documents for presentation and/or publication - Ad-Hoc
- Manage the storage, tracking, internal control, and retrieval of information, data documentation, and datasets for all assigned projects - Ad-Hoc
- Attend research-related and statistical consultation meetings as requested by the senior biostatistician, and DIR investigators and research fellows; and Report, either verbally and/or in writing, regular updates on the progress of their work and any subordinates to DIR investigators - Ad-Hoc
- Working with the NIMHD DIR investigators and trainees to perform data management and data analysis for both primary data collection studies and secondary/publicly available datasets - Ad-Hoc
- Perform other duties as assigned. This job description is not designed to cover or contain a comprehensive list of duties or responsibilities that are required of the candidate for this job. Duties and responsibilities may change at any time with or without notice depending on the studies in the lab. - Ad-Hoc
- Conduct statistical analyses; perform data cleaning and formatting, data harmonization, and data analyses; and prepare results for publication from intervention studies, observational studies, and secondary data analysis projects using complex survey data, hospital/medical records, administrative data, or other data sources - Ad-Hoc
- Conduct data collection/entry, management, cleaning, and manipulation activities - Ad-Hoc
- Prepare and/or update data tables and figures, methods sections of manuscripts, reports, and other documents for presentation and/or publication - Ad-Hoc
- Attend all lab meetings, lab check-ins, and other research-related meetings as requested by investigators or trainees - Ad-Hoc
- Attend all lab meetings, lab check-ins, and other research-related meetings as requested by investigators or trainees - Ad-Hoc
- Train trainees on developing statistical analytic codes to analyze quantitative data to achieve research objectives - Ad-Hoc
- Provide periodic training on contemporary epidemiological and biostatistics analytics approaches to the NIMHD DIR - Ad-Hoc
Basic Qualifications:
- Ph.D. in Biostatistics, Epidemiology, Statistics, Statistics and Decision Science, Computer Programming and Data Processing, Applied Mathematics, Management Information Systems and Statistics, Mathematics and Computer Science, or a related discipline.
- Experience conducting statistical analyses in complex survey data or other secondary data sources that involve sampling weights (e.g., NHANES, National Health Interview Survey [NHIS], and Medical Expenditure Panel Survey [MEPS]).
- Experience with structural equation modeling, including but not limited mediation analysis, effect measure modification, moderated mediation analysis, latent class analysis (LCA), principal components analysis (PCA) and other dimensionality reduction methods (structural equation modeling); non- parametric statistical methods; quasi-experimental statistical analyses (e.g., difference-in-difference)
- Experience in working with students or trainees in teaching data analytic skills.
- Experience conducting statistical analyses in electronic health records (EHR/EMR) and administrative claims.
- Experience in cleaning, condensing, merging, and reformatting data into files that are appropriate for data analysis and data sharing, including preparing de-identified datasets and documentation for external users.
- Experience in creating variables as needed for analyses and documenting methods and definitions for all variables created (e.g., data dictionary).
- Experience in analyzing studies using high-dimensional, longitudinal, clustered, multi-level, and repeated measures data.
- Experience in scientific data analysis, statistical modelling, algorithm development, data visualization, machine learning, linear and non-linear regression, data cleaning, data analysis, epidemiology, and data mining.
- Expertise to perform the duties of the position, which include working with NIMHD DIR investigators and fellows to perform data management and data analysis for both primary data collection studies and secondary/publicly available datasets in a timely manner.
- Expertise in performing statistical analyses using multiple statistical analysis software packages.
- Proficiency in using advanced statistical methods.
- Familiarity with Bayesian statistics and simulation modeling.
- Skilled in MPlus, SUDAAN, ArcGIS, R, SPSS, Python, SAS, STATA, and C++.
Minimum Qualifications:
- Publications in peer reviewed journals is preferred.
- Experience with Health Disparities research is preferred.
- A drive to learn and master new technologies, statistical methods, and techniques.
- Ability to multi-task and pay close attention to detail.
- Excellent analytical, organizational and time management skills.
- Strong communication skills, both oral and written.
*This job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required by this position.
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed above are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
GAP Solutions provides reasonable accommodations to qualified individuals with disabilities. If you need an accommodation to apply for a job us at recruiting@gapsi.com. You will need to reference the requisition number of the position in which you are interested. Your message will be routed to the appropriate recruiter who will assist you. Please note, this email address is only to be used for those individuals who need an accommodation to apply for a job. Emails for any other reason or those that do not include a requisition number will not be returned.
GAP Solutions is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to their race, ethnicity, ancestry, color, sex, religion, creed, age, national origin, citizenship status, disability, medical condition, military and veteran status, marital status, sexual orientation or perceived sexual orientation, gender, gender identity, and gender expression, familial status, political affiliation, genetic information, or any other legally protected status or characteristics.
This position is contingent upon contract award.
