survivalMPL-package: Penalised Maximum Likelihood for Survival Analysis Models
In survivalMPL: Penalised Maximum Likelihood for Survival Analysis Models
survivalMPL-package R Documentation
Penalised Maximum Likelihood for Survival Analysis Models
Description
Simultaneously estimate the regression coefficients and provide a
'non-parametric' smooth estimate of the baseline hazard function for
proportional hazard Cox models using maximum penalised likelihood (MPL).
Details
This package allows to perform simultaneous estimation of the regression
coefficients and baseline hazard function in Cox proportional hazard models,
with right, left and interval censored data and independent censoring, by maximising a penalised
likelihood, in which a penalty function is used to smooth the baseline hazard
estimate.
Optimisation is achieved using a new iterative algorithm, which combines
Newton's method and the multiplicative iterative algorithm by Ma (2010), and
respects the non-negativity constraints on the baseline hazard estimate
(refer to Ma, Couturier, Heritier and Marschner (2021)).
Valid inferences for the regression coefficients and the baseline hazard,
cumulative baseline hazard and survival functions as well as for their
predictions are available.
This software is accepted by users "as is" and without warranties or guarantees
of any kind.
Author(s)
Dominique-Laurent Couturier, Jun Ma, Stephane Heritier, Maurizio Manuguerra.
Maintainer: Dominique-Laurent Couturier dominique-laurent.couturier@cruk.cam.ac.uk.
References
Ma, J. and Couturier, D.-L., and Heritier, S. and Marschner, I.C. (2021), Penalized likelihood estimation of the proportional hazards model for survival data with interval censoring. International Journal of Biostatistics,doi:10.1515/ijb-2020-0104.
Ma, J. and Heritier, S. and Lo, S. (2014), On the Maximum Penalised
Likelihood Approach for Proportional Hazard Models with Right Censored
Survival Data. Computational Statistics and Data Analysis 74, 142-156.
Ma, J. (2010), Positively constrained multiplicative iterative algorithm for
maximum penalised likelihood tomographic reconstruction.
IEEE Transactions On Signal Processing 57, 181-192.
survivalMPL documentation built on Nov. 21, 2022, 5:07 p.m.
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| survivalMPL-package | R Documentation |
Penalised Maximum Likelihood for Survival Analysis Models
Description
Simultaneously estimate the regression coefficients and provide a 'non-parametric' smooth estimate of the baseline hazard function for proportional hazard Cox models using maximum penalised likelihood (MPL).
Details
This package allows to perform simultaneous estimation of the regression coefficients and baseline hazard function in Cox proportional hazard models, with right, left and interval censored data and independent censoring, by maximising a penalised likelihood, in which a penalty function is used to smooth the baseline hazard estimate.
Optimisation is achieved using a new iterative algorithm, which combines Newton's method and the multiplicative iterative algorithm by Ma (2010), and respects the non-negativity constraints on the baseline hazard estimate (refer to Ma, Couturier, Heritier and Marschner (2021)).
Valid inferences for the regression coefficients and the baseline hazard, cumulative baseline hazard and survival functions as well as for their predictions are available.
This software is accepted by users "as is" and without warranties or guarantees of any kind.
Author(s)
Dominique-Laurent Couturier, Jun Ma, Stephane Heritier, Maurizio Manuguerra.
Maintainer: Dominique-Laurent Couturier dominique-laurent.couturier@cruk.cam.ac.uk.
References
Ma, J. and Couturier, D.-L., and Heritier, S. and Marschner, I.C. (2021), Penalized likelihood estimation of the proportional hazards model for survival data with interval censoring. International Journal of Biostatistics,doi:10.1515/ijb-2020-0104.
Ma, J. and Heritier, S. and Lo, S. (2014), On the Maximum Penalised Likelihood Approach for Proportional Hazard Models with Right Censored Survival Data. Computational Statistics and Data Analysis 74, 142-156.
Ma, J. (2010), Positively constrained multiplicative iterative algorithm for maximum penalised likelihood tomographic reconstruction. IEEE Transactions On Signal Processing 57, 181-192.
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