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
You might also like
You can evaluate unfamiliar locations objectively against your specific production needs—whether that's crane access, parking capacity, noise restrictions, or permit requirements. The skill generates comprehensive scout reports that compare multiple sites using weighted evaluation matrices, flag deal-breakers early, and document infrastructure limitations so your production team makes informed decisions before committing resources to visits.
by skillslib
You'll design robust SCADA system architectures, configure industrial protocols (Modbus, Profibus, OPC-UA, DNP3), and diagnose connectivity and performance issues across distributed control networks. Get step-by-step configuration guidance, integration workflows, and troubleshooting decision trees tailored to your specific hardware and protocol stack.
by skillslib
You can establish systematic color grading workflows that accelerate project delivery, ensure visual consistency across episodes and projects, and maintain broadcast-quality standards. Claude generates reusable templates, quality control checklists, and grading decision frameworks tailored to your project's color science and deliverable requirements.
by skillslib
Generate customized legal documents and client communications automatically from your MyCase case data. This skill extracts case details, client information, and matter status to populate document templates, send email updates, and create batch processing workflows. Reduce manual data entry and standardize your document generation process while maintaining client communication consistency.
by skillslib
You can convert raw ESG metrics and narratives into professionally structured impact reports that satisfy regulatory and investor requirements. The skill automatically verifies alignment with SASB materiality categories, GRI standards, and TCFD recommendations; constructs defensible materiality matrices from stakeholder feedback; standardizes impact metrics to IRIS+ and SDG frameworks; and identifies compliance gaps between your draft and mandatory disclosure requirements.
by skillslib
Use Claude to automate your entire collection workflow, from first touch to recovery, with legally sound escalation protocols. You'll recover more money in less time, reduce bad debt write-offs by 25-40%, and build defensible collection records that withstand legal scrutiny. Claude handles customized demand letters, compliance validation, debtor profiling, and escalation strategies tailored to account age, debtor behavior, and jurisdiction.
by skillslib
You can quantify how interest rate changes will affect your bond holdings through duration and convexity analysis, calculate precise hedge ratios for Treasury futures and other instruments, and stress-test portfolios across multiple yield curve scenarios. This skill handles complex securities including callable bonds and mortgage-backed securities, enabling you to compare different portfolio structures and evaluate positioning against interest rate forecasts.
by skillslib
This skill helps you troubleshoot Wwise integration issues, diagnose audio performance bottlenecks, and generate comprehensive documentation for interactive audio specifications. You'll get step-by-step fixes for common implementation problems, performance optimization strategies, and best practices for audio systems across different game engines and platforms.
by skillslib