Introduction to Quantitative Bias

Join us for a two-hour workshop (lecture and practical) with Dr Rachel Hughes as she introduces the Quantitative Bias Analysis (QBA) framework, and shows how to apply it for problems of unobserved confounders in observational studies. QBA is an essential framework to assess the robustness of findings to common assumption violations. Designed for researchers and students new to sensitivity analysis, this session will emphasise intuition and practical applications.

Contributors

• Dr Rachael A. Hughes is a Senior Research Fellow in Medical Statistics at the University of Bristol’s Medical School (Population Health Sciences), affiliated with the MRC Integrative Epidemiology Unit. Her research spans longitudinal modelling in life-course epidemiology, causal inference, missing-data methods, instrumental variable analysis, selection bias, and the clinical epidemiology of HIV and AIDS in the era of antiretroviral therapy. Dr Hughes is especially recognised for her contributions in quantitative bias analysis (QBA), particularly methods to address unmeasured confounding, measurement error, misclassification, and selection bias, and for advancing their accessibility through practical software tools and reviews. Her recent methodological work includes co-authoring a BMC Medical Research Methodology review on QBA software for mismeasured variables.
• Professor Jose Pina-Sánchez is Professor in Quantitative Criminology at the University of Leeds and Director of Advanced Quantitative Methods for the White Rose DTP.