In Progress

 

Principal Investigator: 

Dr Margarita Moreno-Betancur

Postdoctoral Biostatisticians: 

Dr Daisy Shepherd

Dr Sarah Arnup

Dr Ghazaleh Dashti

Dr Marnie Downes

PhD Students: 

Ms Rose Smart

Ms Susan Ellul

Ms Jiaxin Zhang

Collaborators:

Prof. John Carlin
Prof Stijn Vansteelandt

Causal inference in health data science: advancing understanding and methods

The ultimate goal of medical and health research is to improve patient outcomes and population health. As a result, the overwhelming majority of clinical and public health research studies ask “causal” questions, concerning the effect of treatments, policies, behaviours and other exposures on health outcomes. In many cases, especially in the current era of data deluge, these studies rely on observational (non-randomised) data to address causal questions. As a result, recent understanding of the challenges of making causal inferences, and related analytical methods, are critical to modern health data science.

This program of research aims to develop, disseminate and promote the adoption of modern causal thinking and related methods in medical and health research, through research within the following strands (sometimes overlapping):

  • Practice of causal inference, especially in longitudinal life-course epidemiological studies
  • Mediation analysis methods
  • Data-adaptive methods for causal inference
  • Missing data in causal inference (see also here)

 

Further resources: 

o   Analysis plan template for life-course cohort studies: https://figshare.com/articles/_/12471380

o   Software: https://github.com/moreno-betancur

o   More resources: https://moreno-betancur.github.io/

 

     

    Related People

    Prof John Carlin
    Lead Investigator
    Post-doctoral Biostatistician
    Dr Sarah Arnup
    Post-doctoral Biostatistician
    Post-doctoral Biostatistician
    Ghazaleh Dashti
    Post-doctoral Biostatistician
    PhD Student
    PhD Student
    Jiaxin Zhang
    PhD Student