Practical considerations when analyzing discrete survival times using the grouped relative risk model

Rachel MacKay Altman () and Andrew Henrey
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Rachel MacKay Altman: Simon Fraser University
Andrew Henrey: Simon Fraser University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2018, vol. 24, issue 3, No 8, 532-547

Abstract: Abstract The grouped relative risk model (GRRM) is a popular semi-parametric model for analyzing discrete survival time data. The maximum likelihood estimators (MLEs) of the regression coefficients in this model are often asymptotically efficient relative to those based on a more restrictive, parametric model. However, in settings with a small number of sampling units, the usual properties of the MLEs are not assured. In this paper, we discuss computational issues that can arise when fitting a GRRM to small samples, and describe conditions under which the MLEs can be ill-behaved. We find that, overall, estimators based on a penalized score function behave substantially better than the MLEs in this setting and, in particular, can be far more efficient. We also provide methods of assessing the fit of a GRRM to small samples.

Keywords: Bias reduction; Discrete survival times; Efficiency; Grouped relative risk model; Penalized score function; Small samples (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10985-017-9410-7

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