Course Overview
Meta-analysis has long been the gold standard for synthesizing research evidence. In today’s data-intensive world, however, traditional meta-analytic approaches must evolve to accommodate high-dimensional datasets, machine learning outputs, real-time data streams, and AI-assisted research workflows.
This course bridges classical statistical meta-analysis with modern data science and artificial intelligence techniques. Participants will learn how to conduct rigorous evidence synthesis while leveraging scalable computation, automated extraction pipelines, and AI-assisted modelling.
Designed for data professionals, researchers, public health analysts, and policy-driven organizations, this program equips participants to lead evidence-based decision-making in complex, data-rich environments.
What You Will Learn
1. Foundations of Meta-Analysis
- Fixed vs. random effects models
- Effect size computation (OR, RR, SMD, HR)
- Heterogeneity assessment (I², Q-statistic)
- Publication bias detection
2. Computational & Big Data Extensions
- Handling large-scale datasets
- Distributed data aggregation
- High-dimensional meta-regression
- Scalable workflows using Python/R
3. AI & Machine Learning Integration
- AI-assisted literature screening
- NLP for automated study extraction
- Embedding-based study clustering
- Using ML outputs within meta-analytic frameworks
4. Advanced & Emerging Topics
- Network meta-analysis
- Living meta-analysis pipelines
- Bayesian meta-analysis
- Reproducibility and open science standards
5. Applied Case Studies
- Public health intervention synthesis
- AI model benchmarking studies
- Policy impact meta-evaluations
- Clinical and real-world evidence integration
Who This Course Is For
- Data Analysts and Data Scientists
- Public Health Researchers
- Epidemiologists
- AI/ML Practitioners working with research data
- PhD candidates and research-focused professionals
- Monitoring & Evaluation specialists
Tools & Technologies
Participants will gain hands-on exposure to:
- Python (Pandas, Statsmodels, Scikit-learn)
- R (metafor, meta packages)
- SQL for structured data aggregation
- NLP libraries for automated extraction
- Reproducible workflows and version control
Course Format
- Live virtual sessions
- Applied practical labs
- Real-world datasets
- Capstone project
- Certificate of Completion (Hankali × ISLP)
Key Outcomes
By the end of this course, participants will be able to:
- Conduct rigorous and reproducible meta-analyses
- Integrate machine learning outputs into evidence synthesis
- Automate components of systematic reviews
- Design scalable meta-analytic workflows for big datasets
- Communicate findings to technical and policy stakeholders