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Principal Investigator
Giampiero Marra
University College London
Position Title
Associate Professor in Statistics
About this CDAS Project
PLCO (Learn more about this study)
Project ID
Initial CDAS Request Approval
Nov 14, 2019
Link-based survival additive models with mixed types of censoring.
Existing methods for survival models are limited in that they do not often consider monotonicity constraints on the survival function, flexible covariate effects and different types of censoring mechanisms simultaneously. A methodology is discussed that addresses the three above mentioned problems by allowing for survival outcomes to be modelled using flexible parametric formulations for time-to-event data, the baseline survival function to be modelled using monotonic splines, and covariate effects to be modelled using an additive predictor incorporating several types of covariate effects. The model parameters are estimated using a carefully structured efficient and stable penalized likelihood algorithm. The proposed framework is evaluated using simulated and real data sets. The relevant numerical computations can be easily carried out using the freely available GJRM R package.

- Advance research in the area of survival analysis by developing a flexible generalised survival model capable of handling all types of censoring simultaneously.
- Investigate the causes of cancer and analyse how different covariates affect survival time in cancer patients.


Dr Rosalba Radice - Cass Business School
Davide Lazzaro - University College London