Regularized bidimensional estimation of the hazard rate

Goepp Vivien (), Thalabard Jean-Christophe, Nuel Grégory and Bouaziz Olivier
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Goepp Vivien: MAP5, CNRS UMR 8145, 45, rue des Saints-Pères, 75006, Paris, France
Thalabard Jean-Christophe: MAP5, CNRS UMR 8145, 45, rue des Saints-Pères, 75006, Paris, France
Nuel Grégory: LPSM, CNRS UMR 8001, 4, Place Jussieu, 75005, Paris, France
Bouaziz Olivier: MAP5, CNRS UMR 8145, 45, rue des Saints-Pères, 75006, Paris, France

The International Journal of Biostatistics, 2022, vol. 18, issue 1, 263-277

Abstract: In epidemiological or demographic studies, with variable age at onset, a typical quantity of interest is the incidence of a disease (for example the cancer incidence). In these studies, the individuals are usually highly heterogeneous in terms of dates of birth (the cohort) and with respect to the calendar time (the period) and appropriate estimation methods are needed. In this article a new estimation method is presented which extends classical age-period-cohort analysis by allowing interactions between age, period and cohort effects. We introduce a bidimensional regularized estimate of the hazard rate where a penalty is introduced on the likelihood of the model. This penalty can be designed either to smooth the hazard rate or to enforce consecutive values of the hazard to be equal, leading to a parsimonious representation of the hazard rate. In the latter case, we make use of an iterative penalized likelihood scheme to approximate the L0 norm, which makes the computation tractable. The method is evaluated on simulated data and applied on breast cancer survival data from the SEER program.

Keywords: adaptive ridge procedure; age-period-cohort analysis; penalized likelihood; piecewise constant hazard; survival analysis (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/ijb-2019-0003

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