Epidemiological Research Design & Analysis (ERDA)
Epidemiological research design and analysis involves planning studies to investigate disease patterns, risk factors, and health outcomes in populations, using structured methods to ensure validity, minimize bias, and draw reliable inferences.
Common Designs
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Observational studies: Cross-sectional (prevalence at a point in time), cohort (follow groups over time for incidence), case-control (compare cases vs. controls for exposures).
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Experimental studies: Randomized controlled trials (RCTs) with allocation concealment and blinding for causality assessment.
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Mixed methods: Combine quantitative (e.g., surveys, outcomes) and qualitative (e.g., interviews) data, often convergent or sequential, to explain complex health phenomena like chiropractic care utilization.
Key Analysis Steps
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Data collection: Define clear outcomes, exposures, confounders; use standardized tools (e.g., JBI checklists for quality); ensure representative sampling and high response rates.
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Evaluate selection, measurement, confounding, and statistical biases using tools like JBI, ROBINS-I, or MMAT for mixed methods.
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Statistical Methods: Descriptive stats, regression (logistic, Cox), meta-analysis if pooling; handle heterogeneity with SWiM guidelines; report effect estimates (OR, RR, prevalence) with 95% CIs.
Tools and Reporting
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Software: R, Stata, SAS for analysis; PRISMA for systematic reviews, STROBE for observational studies.
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Best Practices: Register protocols (e.g., OSF), assess clinical/methodological heterogeneity, perform sensitivity analyses, and synthesize narratively if meta-analysis unsuitable.