Dalla Lana School of Public Health

Faculty Member

Kuan Liu PhD, MMath

Research Interests

My primary research focuses on developing methodology for statistical inference with complex longitudinal data in comparative effectiveness research. My areas of methodological interest include causal inference, Bayesian statistics, longitudinal data analysis, measurement errors and bias analysis, and semi-parametric/parametric joint modelling.

Research themes

1. Methodological research in causal inference with longitudinal data

Bayesian estimation methods that permit causal inference in longitudinal observational studies using administrative databases with the following features, repeated measures, high-dimensional confounding, latent variables, and multiple outcomes.

Ongoing projects under this theme:

• Bayesian spatial causal inference
• Bayesian causal joint and mixture models
• Bayesian transfer learning

2. Design and analysis of observational study

I am interested in studying and applying statistical methods to the design and analysis of clinical and public health studies of rare diseases and chronic conditions. Under this theme, Bayesian inference is an appealing framework: it i) provides a flexible framework for data augmentation and adaptive designs, ii) propagates estimation uncertainty and enables the modelling of latent variables, iii) allows direct probability summaries, and iv) can incorporate prior clinical/expert beliefs.

Ongoing collaborative projects:

• Longitudinal trajectory analysis of multiple repeatedly measured cognitive features in dementia
• Bayesian profile analysis quantifying the impact of school closure during and post-pandemic
• Bayesian causal analysis in pediatric and critical care medicine

3. Causal inference methods for randomized controlled trials

Causal inference methods have been applied to traditional RCT data to adjust for non-compliance. Newer trial designs, such as pragmatic trials, with a focus on providing timely efficacy evidence, often do not feature complete treatment randomization and thus require causal design and methods to estimate treatment effect. Under this topic, my research interests focus on methods for subgroup analysis, including the identification of patient subgroups and clinical phenotypes that have differential responses to treatment.

• Bayesian dynamic borrowing
• Bayesian adaptive trials
• Bayesian heterogeneous treatment effects

Education & Training History

• PhD, Biostatistics, University of Toronto
• MMath, Statistics – Biostatistics, University of Waterloo
• BSc Honours, Statistics, University of Alberta

Other Affiliations

Assistant Professor, Health Services Research Program (outcomes and evaluation methods emphasis), Institute of Health Policy, Management and Evaluation

Teaching

• HAD5314H Applied Bayesian Methods in Clinical Epidemiology and Health Care Research
• HAD5319H Biostatistics III: Advanced Biostatistical Techniques for Observational Studies
• HAD7002H Causal Inference

Publications

See Google Scholar for the full list of publications.