Mobile menu icon
Mobile menu icon Search iconSearch
Search type

Data analysis and methodologies

We aim to improve analysis methods in epidemiology.

We do this by:

  • Developing new methods when necessary, including:
    • developing rules for stopping a trial early when a treatment has been shown to be futile;
    • developing methods to reduce confounding by unmeasured variables by using incorporating data from multiple sources;
    • exploring modern methods of identifying patterns in temporally rich, novel and complex data using machine learning and text-mining;
    • developing methods to provide evidence that a given propensity score will balance all measured covariates to an acceptable level;
    • examining reweighting methods to estimate expected treatment effects in target populations different from the one in which the treatment effect has been measured.
  • Implementing existing methods in a more user friendly manner to encourage wider use, including:
    • weighted cumulative exposure models;
    • missing cause approach to controlling for unmeasured confounding;
    • balance checking following the application of propensity score methods.

Academic leads