Penalized likelihood estimation of the proportional hazards model for survival data with interval censoring.
Authors
Ma, Jun
Couturier, Dominique-Laurent
Heritier, Stephane
Marschner, Ian C
Publication Date
2022-11-01Journal Title
Int J Biostat
ISSN
2194-573X
Publisher
Walter de Gruyter GmbH
Language
eng
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Ma, J., Couturier, D., Heritier, S., & Marschner, I. C. (2022). Penalized likelihood estimation of the proportional hazards model for survival data with interval censoring.. Int J Biostat https://doi.org/10.1515/ijb-2020-0104
Abstract
This paper considers the problem of semi-parametric proportional hazards model fitting where observed survival times contain event times and also interval, left and right censoring times. Although this is not a new topic, many existing methods suffer from poor computational performance. In this paper, we adopt a more versatile penalized likelihood method to estimate the baseline hazard and the regression coefficients simultaneously. The baseline hazard is approximated using basis functions such as M-splines. A penalty is introduced to regularize the baseline hazard estimate and also to ease dependence of the estimates on the knots of the basis functions. We propose a Newton-MI (multiplicative iterative) algorithm to fit this model. We also present novel asymptotic properties of our estimates, allowing for the possibility that some parameters of the approximate baseline hazard may lie on the parameter space boundary. Comparisons of our method against other similar approaches are made through an intensive simulation study. Results demonstrate that our method is very stable and encounters virtually no numerical issues. A real data application involving melanoma recurrence is presented and an R package 'survivalMPL' implementing the method is available on R CRAN.
Keywords
asymptotic properties, automated smoothing, constrained optimization, interval censoring, semi-parametric proportional hazard model
Embargo Lift Date
2100-01-01
Identifiers
External DOI: https://doi.org/10.1515/ijb-2020-0104
This record's URL: https://www.repository.cam.ac.uk/handle/1810/329848
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.