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