clinical

# Clinical Research ## Overview Clinical study design, statistical analysis, and regulatory compliance for medical research. ## Study Designs | Design | Level of Evidence | Best For | |--------|------------------|----------| | RCT | I | Treatment efficacy | | Cohort (prospective) | II | Risk factors, prognosis | | Cohort (retrospective) | III | Exposure-outcome associations | | Case-control | III | Rare diseases, risk factors | | Cross-sectional | IV | Prevalence, correlations | | Case report/series | V | Novel observations | ## Common Analyses - **Survival analysis**: Kaplan-Meier curves, Log-rank test, Cox regression - **Diagnostic accuracy**: Sensitivity, specificity, ROC curve, AUC - **Meta-analysis**: Fixed/random effects, forest plot, heterogeneity (I-squared) - **Propensity score matching**: For observational study confounding - **Nomogram**: Predictive model visualization for clinical use ## Sample Size Calculation - Define: alpha (usually 0.05), power (usually 0.80), effect size, outcome type - Tools: G*Power, R `pwr` package, Python `statsmodels` - Report: formula used, assumptions, expected dropout rate ## Regulatory Compliance - **IRB/Ethics committee**: Required for all human subjects research - **Informed consent**: Written, voluntary, comprehensive - **ClinicalTrials.gov**: Registration before enrollment (ICMJE requirement) - **GDPR/HIPAA**: Data privacy for patient information - **GCP (Good Clinical Practice)**: ICH E6 guidelines ## Reporting Guidelines | Guideline | Study Type | |-----------|-----------| | CONSORT | Randomized controlled trials | | STROBE | Observational studies | | PRISMA | Systematic reviews / meta-analyses | | STARD | Diagnostic accuracy | | TRIPOD | Prediction models | | SPIRIT | Study protocols |