Master in Missing Data

Engaging directly with the complexities of observational and experimental research, this intensive three-day programme provides a rigorous framework for addressing the loss of statistical power and potential bias caused by incomplete datasets. Taught by expert statisticians from the LSHTM Centre for Data and Statistical Science for Health, the course moves beyond standard applications to explore sophisticated methodologies for handling missingness.

Advanced Applied Methodologies

Participants move through a structured curriculum that balances theoretical foundations with hands-on computational practice. The course is specifically designed for:

• Epidemiologists and biostatisticians seeking to formalize their approach to missing data mechanisms.
• Health researchers who require robust strategies for propensity score analysis and prognostic model development.
• Quantitative professionals aiming to integrate sensitivity analysis and non-linear interactions into their existing statistical workflows.

By shifting the focus from simple deletion to principled multiple imputation—utilizing both joint models and fully conditional specification—this programme equips practitioners with the tools to produce valid statistical inferences in challenging research environments.