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 Win-BUGS 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
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed m...
Flexible data regression is an important tool for capturing complicated trends in data. One approach...
P-splines are a popular approach for fitting nonlinear effects of continuous covariates in semiparam...
In the investigation of disease dynamics, the effect of covariates on the hazard function is a major...
In the analysis of survival data, it is usually assumed that any unit will experience the event of i...
Modelling survival data with splines is a project supervised by Dr Julian Stander from the Centre fo...
Kauermann G. Penalized spline smoothing in multivariable survival models with varying coefficients. ...
A simple parametrization, built from the definition of cubic splines, is shown to facilitate the imp...
We discuss the use of Bayesian P-spline and of the composite link model to estimate survival functio...
Standard Bayesian methods for time-to-event data rely on Markov chain Monte Carlo (MCMC) to sample f...
In Bayesian statistics, a general and widely used approach to extract information from (complex) pos...
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed m...
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed m...
Number and location of knots strongly impact fitted values obtained from spline regression methods. ...
Number and location of knots strongly impact on fitted values obtained from spline regression method...
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed m...
Flexible data regression is an important tool for capturing complicated trends in data. One approach...
P-splines are a popular approach for fitting nonlinear effects of continuous covariates in semiparam...
In the investigation of disease dynamics, the effect of covariates on the hazard function is a major...
In the analysis of survival data, it is usually assumed that any unit will experience the event of i...
Modelling survival data with splines is a project supervised by Dr Julian Stander from the Centre fo...
Kauermann G. Penalized spline smoothing in multivariable survival models with varying coefficients. ...
A simple parametrization, built from the definition of cubic splines, is shown to facilitate the imp...
We discuss the use of Bayesian P-spline and of the composite link model to estimate survival functio...
Standard Bayesian methods for time-to-event data rely on Markov chain Monte Carlo (MCMC) to sample f...
In Bayesian statistics, a general and widely used approach to extract information from (complex) pos...
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed m...
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed m...
Number and location of knots strongly impact fitted values obtained from spline regression methods. ...
Number and location of knots strongly impact on fitted values obtained from spline regression method...
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed m...
Flexible data regression is an important tool for capturing complicated trends in data. One approach...
P-splines are a popular approach for fitting nonlinear effects of continuous covariates in semiparam...