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. Methdological research in causal inference with longitudinal data

I develop 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 (accepting students):

  • Causal analysis with unmeasured confounding under a Bayesian framework
  • Bayesian causal joint and mixture models
  • Bayesian causal analysis with clustered data

2. Design and analysis of observational study

I am interested in studying and applying statistical methods on 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) allow direct probability summaries, and iv) can incorporate prior clinical/expert beliefs.

Ongoing collaborative projects (accepting students):

  • 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 inference methods to estimate causal effect. Under this topic, my research interests focus on methods for subgroup analysis including identification of patient subgroups and clinical phenotypes that have differential response to treatment.

Ongoing collaborative projects:

  • Missing data in RCT (DSI-SUDS)

Google scholar page