Abstract
Methods for fitting survival regression models with a penalized smoothed hazard function have been recently discussed, even though they could be cumbersome. A simpler alternative which does not require specific software packages could be fitting a penalized piecewise exponential model. In this work the implementation of such strategy in WinBUGS is illustrated, and preliminary results are reported concerning the application of Bayesian P-splines techniques. The technique is applied to a pre-specified model in which the number and positions of knots were fixed on the basis of clinical knowledge, thus defining a non-standard smoothing problem.
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Marano, G., Boracchi, P., Biganzoli, E.M. (2015). Estimation of a Piecewise Exponential Model by Bayesian P-splines Techniques for Prognostic Assessment and Prediction. In: DI Serio, C., Liò, P., Nonis, A., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2014. Lecture Notes in Computer Science(), vol 8623. Springer, Cham. https://doi.org/10.1007/978-3-319-24462-4_16
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DOI: https://doi.org/10.1007/978-3-319-24462-4_16
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