From Database Extraction to Statistical Analysis and Publication-Ready Reporting
Target audience: Clinical doctors, residents, fellows, public health practitioners, epidemiologists, medical researchers, and healthcare professionals.
Healthcare professionals generate and encounter important clinical questions every day. Yet many have limited practical training in how to transform those questions into well-designed studies using existing health databases.
This program equips healthcare professionals with practical skills to conduct secondary-data research using real-world clinical, epidemiological, population health, and cancer datasets. Participants will learn how to formulate research questions, identify appropriate databases, extract relevant data, construct study cohorts, prepare analytical datasets, conduct statistical analyses in Stata, interpret findings, and prepare research reports and manuscripts.
The training focuses on practical research workflows rather than software demonstrations alone. Participants will work through guided examples inspired by real research questions in critical care, cardiovascular disease, diabetes, mental health, cancer epidemiology, healthcare utilization, and population health.
The program introduces databases such as NHANES, NAMCS, NHAMCS, MIMIC-IV, SEER, TCGA, CuratedMetagenomicData Repository, Canadian Community Health Survey (CCHS) Public-Use Microdata, and CDC WONDER, with additional datasets introduced where appropriate. Participants will learn that each database has its own structure, documentation, sampling design, access requirements, and analytical considerations.
The central goal is to help participants move from a research question to a defensible, reproducible analysis and a well-structured research report.
Software licenses, third-party cloud charges, and fees associated with restricted data access are not included in the course fee unless expressly stated during enrollment.
Artificial intelligence can help researchers brainstorm questions, explain statistical concepts, draft code, and improve manuscript language. However, AI-generated outputs are not automatically methodologically correct, reproducible, or suitable for clinical research.
Researchers still need to know how to select an appropriate dataset, define a valid study population, identify the correct variables, handle missing data, choose suitable statistical methods, assess potential bias, and interpret results in their clinical context.
These skills are especially important when working with complex health surveys, longitudinal clinical records, and restricted-access databases. For example, NHANES analyses must account for appropriate sampling weights and survey design when making population-level inferences. MIMIC-IV access and data use are also governed by specific credentialing and data-use requirements.
This course helps participants develop the judgment to use AI as a supporting tool while retaining responsibility for their data, methods, statistical results, and scientific conclusions.
The objective is not simply to produce a manuscript faster. It is to produce research that is more transparent, reproducible, and scientifically defensible.
Participants develop a research question and identify a suitable database, the variables required, the target population, and the proposed study design.
A concise research concept and preliminary analysis plan.
Participants follow a guided extraction workflow using an appropriate public-use dataset or authorized clinical database environment.
A documented extraction workflow and a preliminary study cohort.
Note: Access to restricted databases such as MIMIC-IV is subject to individual approval and applicable data-use agreements. Participants without access will use approved training materials and suitable alternative datasets for the relevant exercises.
Participants prepare a dataset for analysis, document exclusions and recoding decisions, and run data-quality checks.
A cleaned analytical dataset with documented preparation steps.
Participants analyze their prepared dataset using a structured Stata workflow, select suitable statistical methods, and interpret the results.
A reproducible Stata analysis file and an initial set of results tables.
Participants develop a results narrative and draft the methods and results sections of their research report.
A structured analytical report with tables, figures, and an interpretation of findings.
Participants consolidate their work into a final research report or draft manuscript and present their research workflow, methods, and principal findings.
A completed capstone research report or manuscript draft, accompanied by the relevant analytical code and supporting tables or figures.
Stata is the required primary statistical analysis environment for this program. Participants will use it for data management, descriptive statistics, hypothesis testing, regression analysis, relevant survey-weighted procedures, selected survival analyses, visualization, and reproducible reporting.
Participants will be introduced to SQL-based querying and Google BigQuery for appropriate large clinical datasets, particularly eligible MIMIC-IV data hosted in BigQuery.
R will be introduced where useful for accessing, transforming, or preparing data from particular sources. The course does not aim to train participants as general-purpose R programmers.
The emphasis is on selecting the appropriate tool for each stage of the research workflow while retaining Stata as the central environment for statistical analysis.
Software note: A licensed Stata installation is recommended. Software licenses, third-party cloud charges, and fees associated with restricted data access are not included in the course fee unless expressly stated during enrollment.
The main teaching examples will draw from the following databases, subject to data availability and access requirements:
Health examination, laboratory, nutrition, and population health research.
Physician office visits, prescribing patterns, and ambulatory care.
Emergency department and hospital outpatient research.
Critical care, hospital outcomes, and clinical cohort studies.
Cancer incidence, stage, survival, and disparities.
Mortality trends and public health surveillance.
Health conditions, behaviors, and healthcare access.
Healthcare utilization, expenditures, insurance, and access.
The course will prioritize a manageable number of complete workflows rather than attempt to cover every database in equal depth. Additional databases may be introduced through demonstrations and supplementary materials.
By the end of the program, participants should be able to:
This course is designed for:
Participants do not need to be experienced programmers. Basic familiarity with research concepts and a willingness to engage in practical exercises are recommended.
The program combines instructor-led demonstrations, guided practical exercises, case-based discussions, assignments, and a final applied project.
Participants will learn through research questions similar to those encountered in clinical and public health practice, including:
Factors associated with uncontrolled hypertension among adults receiving treatment.
Associations between medication use and metabolic outcomes.
Predictors of clinical outcomes among critically ill patients.
Cancer stage, treatment patterns, and survival.
Healthcare utilization and socioeconomic disparities.
Associations between lifestyle factors and chronic disease outcomes.
These are illustrative training topics. Final project feasibility depends on the available variables, dataset documentation, sample size, and study design.
Duration: Six weeks.
The program is designed for working healthcare professionals and can be delivered through scheduled online practical sessions, guided assignments, demonstrations, and access to supporting learning materials.
The final timetable and weekly session schedule will be communicated before the cohort begins.
By the end of the program, each participant who completes the required coursework should have developed:
A research question and analysis plan.
A documented data extraction and cohort selection workflow.
A cleaned analytical dataset or an appropriately documented dataset-preparation workflow.
A reproducible Stata do-file.
Statistical results tables and figures.
An interpretation of findings and limitations.
A structured research report or draft manuscript.
The final output depends on the selected dataset, project scope, and participant progress. Journal acceptance or publication is not guaranteed.
Assessment will be based on practical assignments, participation in guided exercises, and completion of the capstone project.
Participants who satisfy the Academy's completion and assessment requirements will receive a certificate of completion from Trakanalytica Academy.
Trakanalytica Academy focuses on practical analytics, statistical reasoning, research methods, and the application of analytical tools to real-world problems.
The course is informed by practical experience supporting clinical research projects across multiple areas of medicine and public health. Its focus is on helping healthcare professionals understand the complete analytical process, from identifying the right data to interpreting the results and presenting findings responsibly.
You will not simply learn which commands to run. You will learn why particular analytical decisions matter, how to assess the quality of your findings, and how to document the work so that other researchers can understand and evaluate it.
Have a clinical research question you want to investigate? Want to strengthen your ability to analyze existing health datasets and prepare a research report?
Join Clinical Research Using Secondary Health Data and develop practical skills for evidence-based clinical and public health research.
Practical Skills. Real-World Data. Professional Growth.