ABOUT YOU

You will have a PhD (or equivalent) in a relevant discipline. Knowledge of methodology for prognostic models, survival analysis and competing risks would be highly beneficial for this role as well as experience of analysing electronic health records.

The Biostatistics Research Group are looking to appoint a Research Associate interested in statistical methodology to undertake a project on calibration drift in prognostic models.
The research team has previously developed temporal recalibration1,2, a method which takes account of improvements in survival over time to ensure that the predictions that prognostic models produce are as up-to-date as possible and relevant to current patients.
The aim of this project is to maximise patient benefit from prognostic models by extending and making temporal recalibration methods easily accessible for researchers.

Supported by the project leads and research team, the Research Associate will:

• Use simulation studies to determine how best to internally validate models developed using temporal recalibration, with and without competing risks
• Develop software packages (in Stata and/or R) and web-based tutorials to aid with the uptake of temporal recalibration methodology

They will also have the opportunity to present research findings at international conferences and attend specialist training courses.
References:
1 Booth S, Riley RD, Ensor J, Lambert PC, Rutherford MJ. Temporal recalibration for improving prognostic model development and risk predictions in settings where survival is improving over time. Int J Epidemiol. 2020;49(4):1316-1325.
2 Booth S, Mozumder SI, Archer L, Ensor J, Riley RD, Lambert PC, Rutherford MJ. Using temporal recalibration to improve the calibration of risk prediction models in competing risk settings when there are trends in survival over time. Stat Med. 2023;42(27):5007-5024