Xiaoqin Wang

Xiaoqin Wang is a Lecturer in Statistics at the University of Gävle, Sweden, with an active research program spanning theoretical statistics and applied biostatistics. Her work bridges mathematical rigor with real-world health applications, particularly in cancer epidemiology and pandemic response modeling.

Her research concentrates on causal inference methodologies, specializing in G-formula extensions for sequential treatment effects and diagnostic timing analysis. Key contributions include frameworks for estimating point effects of early cancer diagnosis on survival outcomes and statistical evaluations of pandemic interventions. She consistently addresses methodological challenges in observational data, such as time-varying confounding and treatment interaction effects.

Analysis of her 2015-2024 publications reveals a strategic shift toward high-impact health applications while maintaining theoretical depth. Her work increasingly integrates multi-institutional collaborations (notably with L. Yin) to tackle urgent public health questions, including Swedish COVID-19 strategy evaluation and cardia cancer diagnostic pathways. The consistent focus on estimand definition and robust statistical frameworks demonstrates methodological leadership.

Wang maintains active scholarly engagement through publications in Statistical Methods in Medical Research, Biometrical Journal, and Annals of Statistics, with recent work emphasizing practical implementation of causal methods for medical researchers.