Skip to main content
What types of page to search?

Popular pages: Undergraduate Postgraduate PhD Accommodation Research International students

Alternatively use our A-Z index.
JoineR & JoineR-M

JoineR and JoineR-M

Emerging data collection protocols in medical research introduce complexities which are either not covered by existing generally available software or, more fundamentally, require further statistical methodological development.

A particular example is the need for joint modelling of combined repeated measurements and event-time data. A major difficulty is how best to merge information from the repeated measurements and event history data, especially as the longitudinal data is usually irregularly and imperfectly observed.

Longitudinal data are prevalent throughout the medical literature, but joint modelling methods are not routinely used. Often, simpler approaches are used, for example separate analyses of longitudinal and time-to-event data, because of the ready availability of standard software. These methods potentially suffer from inefficiency or, worse, severe bias through misspecification, for example by failing to take account of informative dropout during the intended follow-up period.

JoineR

JoineR is a collaborative research project between the universities of Liverpool (PI: Professor Paula Williamson), Lancaster (PI: Professor Peter Diggle) and Newcastle (PI: Professor Robin Henderson), which ran from 2005 to 2010. The JoineR project delivered on its objectives to:

  1. Development of new statistical methods for the analysis of complex longitudinal data structures.
  2. Implementation of user-friendly software for new statistical methods.
  3. Dissemination of the modern methods of statistical analysis to the medical research community.

JoineR-M

JoineR-M project builds on the original JoineR project by considering multivariate longitudinal data and multivariate time-to-event data. The aims and research objectives of the JoineR-M project are:

  1. Development of a novel but sufficiently flexible joint modelling approach for multiple longitudinal biomarkers and correlated event time outcomes aiming to:
    1. estimate the association between multiple dimensions of the two outcome processes; and
    2. provide individual-specific predictions of clinical events.
  2. Exploit a novel application of multivariate longitudinal-event time joint modelling in pharmacogenetic association studies allowing for the influence of dose and adherence.
  3. Construct user-friendly, freely available software for the implementation of proposed multivariate joint model which can provide solutions in real-time.
  4. Ensure practical relevance of the proposed methods of statistical analyses for the medical research community, with focus on two collaborative research projects:
    1. biomarkers for early diagnosis of sepsis; and
    2. genetic predictors of response to warfarin treatment.
  5. Bring the proposed methods of statistical analysis into mainstream medical research.

JoineR-M is a collaboration between the universities of Liverpool (PIs: Dr Ruwanthi Kolamunnage-Dona and Dr Andrea Jorgensen) and Northumbria (PI: Dr Pete Philipson). This project also includes statistical collaborators from the universities of Newcastle (Professor Robin Henderson) and Liverpool (Professor Paula Williamson), and clinical collaborators from the University of Liverpool (Professor Cheng-Hock Toh and Professor Munir Pirmohamed) who are internationally recognised consultants within the fields of haematology and pharmacogenetics respectively.

Both JoineR and JoineR-M are MRC funded projects.