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Penalised Maximum Likelihood for Survival Analysis Models

survivalMPL-package.Rd

Simultaneously estimate regression coefficients and provide a smooth non-parametric baseline hazard for proportional hazards Cox models using maximum penalised likelihood (MPL). Right, left, and interval censoring are supported.

Details

Optimisation combines Newton updates for regression parameters with a multiplicative scheme for baseline hazards while enforcing non-negativity (Ma, Couturier, Heritier and Marschner, 2021). Inference is available for regression coefficients, baseline hazard, cumulative hazard, and survival functions, as well as their predictions.

This software is provided without warranties or guarantees of any kind.

References

Ma, J., Couturier, D.-L., 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., 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.

See also

Useful links:

Author

Maintainer: Dominique-Laurent Couturier dominique.couturier@mrc-bsu.cam.ac.uk (ORCID)

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