Epidemiological Study Design & Statistical Analysis Planning
Design epidemiological studies and statistical analysis plans
What You Can Do
You can systematically design epidemiological studies using evidence-based frameworks for study design selection, develop comprehensive statistical analysis plans with rigorous pre-specification, and assess potential biases and confounding in your study architecture. This skill helps you specify appropriate regression models, calculate sample sizes, plan sensitivity analyses, and document methodological decisions that align with epidemiological best practices.
Features
Compare cohort, case-control, cross-sectional, and experimental designs. Get guidance on selecting the optimal design for your research question, considering feasibility, exposure timing, and outcome measurement.
Create detailed pre-specified SAPs that outline primary analyses, secondary objectives, subgroup analyses, and post-hoc exploration strategies. Reduces analytic flexibility and p-hacking.
Systematically identify selection bias, information bias, confounding, and reverse causation in your study design. Receive specific mitigation strategies for each threat to validity.
Specify appropriate regression models (linear, logistic, Cox proportional hazards, Poisson) with clear rationale for functional forms, interaction terms, and covariate adjustment sets.
Calculate required sample sizes for your primary objective, considering effect sizes, study design, significance levels, and anticipated loss to follow-up or missing data.
Use directed acyclic graphs to identify confounder, mediator, and collider structures. Determine which variables to include or exclude from regression models based on causal structure.
Pre-specify sensitivity analyses to assess robustness of findings to unmeasured confounding, selection bias, measurement error, and alternative analytical choices.
Example Output
Example 1: Study Design Summary
Research Question: Does occupational pesticide exposure increase lung cancer risk?
Recommended Design: Case-control (nested within cohort if possible)
- Cases: Incident lung cancer diagnoses (1990-2025)
- Controls: Cancer-free individuals, stratified by age and gender
- Exposure: Occupational history via employment records
- Confounders: Smoking, family history, other occupational exposures
Example 2: Statistical Analysis Plan
Primary Analysis:
Logistic regression: ln(odds of lung cancer) ~ pesticide exposure + age +
gender + smoking
Model Equation:
log(odds) = β₀ + β₁(exposure) + β₂(age) + β₃(female) + β₄(smoking_pack-years)
Sensitivity Analyses:
1. Restrict to never-smokers only
2. Unmeasured confounding assessment (E-value calculation)
3. Alternative exposure classification (quartiles vs continuous)
4. Multiple imputation for missing smoking data (20 imputations)
Example 3: Bias Assessment Report
Identified Threat: Recall bias in occupational exposure assessment
Severity: Moderate (differential recall possible by case status)
Mitigation Strategy:
- Primary: Use company employment records as gold standard
- Validation: Compare questionnaire response with records
- Sensitivity: Stratified analysis by exposure recall quality
What's Included
- Study Design Decision Tree: Interactive framework to select the optimal design based on your research question, study population, resource constraints, and exposure/outcome timing.
- Statistical Analysis Plan Template: Detailed SAP template with sections for primary objective, statistical methods, subgroup analyses, sensitivity analyses, and model specification rationale.
- Bias Assessment Framework: Structured checklist for identifying selection bias, information bias, confounding bias, and reverse causation with tailored mitigation strategies for each threat.
- Regression Model Specification Guide: Step-by-step guidance for choosing and specifying appropriate models with clear documentation of assumptions, functional forms, interaction terms, and covariate adjustment sets.
- Power and Sample Size Worksheet: Interactive worksheet for calculating sample size requirements with inputs for effect size, study design, significance level, power, and anticipated loss to follow-up.
- Sensitivity Analysis Checklist: Pre-built checklist of sensitivity analyses including unmeasured confounding (E-values), selection bias, measurement error, and alternative analytical approaches.
Who It's For
- Epidemiologists designing new studies
- Biostatisticians developing statistical analysis plans
- Public health researchers planning research protocols
- Clinical trial designers working on safety and efficacy studies
- Academic researchers preparing grant proposals
Best For
- Designing new epidemiological studies from scratch
- Writing pre-specified statistical analysis plans for protocols
- Conducting peer review of study designs and protocols
- Assessing confounding and bias in existing study designs
- Specifying regression models for causal inference studies
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