Statistical Methodology for Longitudinal Studies in Clinical Research

IJoineR 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), aiming to bring modern methods of statistical analysis into mainstream medical research.

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. With the increased usage of automatic data capture information systems, including remote monitoring, it is now common for repeated measurements of biomarkers to be available alongside event history data and a major difficulty is how best to merge information from the two sources, especially as the biomarker is usually irregularly and imperfectly observed. The project aims to develop new statistical methodologies for the analysis of complex longitudinal data structures, implement the methods in corresponding user-friendly software in the R language and disseminate the results to the medical research community

 

Some key outputs from the project:

Joint modelling of longitudinal and competing risks data. Williamson P.R. , Kolamnunnage-Dona R. , Philipson P. and Marson A. G. Statistics in Medicine 2008; 27(30), Pages 6426 - 6438.

Joint Modelling of Repeated Measurements and Time-to-event Outcomes: the Fourth Armitage Lecture. Diggle P., Sousa I. and Chetwynd A. Statistics in Medicine 2008; 27(16), 2981–-2998.

Comparative review of methods for handling drop-out in longitudinal studies. Peter M. Philipson P. M., Ho W. K. and Henderson R. Statistics in Medicine 2008; 27(30), Pages 6276 – 6298.

Joint modelling of time series measures and recurrent events and analysis of the effects of air quality on respiratory symptoms. Zhang H., Ye Y. and Diggle P. and Shi J. Journal of the American Statistical Association 2007; 103, 48-60.

Analysis of longitudinal data with dropout: objectives, assumptions and a proposal (with discussion). Diggle P., Farewell D. and Henderson R. Applied Statistics 2007; 56, 499-550.

The influence of competing risks setting on the choice of hypothesis test for treatment effect. Williamson P. R., Kolamnunnage-Dona R and Tudur Smith C. Biostatistics 2006; 8: 689-694. DOI: 10.1093/biostatistics/kxl040.

More publications and other outputs:

JoineR workshops:
https://www.liv.ac.uk/medstats/courses.htm