NDLI: Tests for Hazard Transformation

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Tests for Hazard Transformation

Content Provider	Springer Nature Link
Author	Ho, Weang Kee Henderson, Robin Philipson, Peter M.
Copyright Year	2010
Abstract	The semiparametric Cox proportional hazards model is routinely adopted to model time-to-event data. Proportionality is a strong assumption, especially when follow-up time, or study duration, is long. Zeng and Lin (J. R. Stat. Soc., Ser. B, 69:1–30, 2007) proposed a useful generalisation through a family of transformation models which allow hazard ratios to vary over time. In this paper we explore a variety of tests for the need for transformation, arguing that the Cox model is so ubiquitous that it should be considered as the default model, to be discarded only if there is good evidence against the model assumptions. Since fitting an alternative transformation model is more complicated than fitting the Cox model, especially as procedures are not yet incorporated in standard software, we focus mainly on tests which require a Cox fit only. A score test is derived, and we also consider performance of omnibus goodness-of-fit tests based on Schoenfeld residuals. These tests can be extended to compare different transformation models. In addition we explore the consequences of fitting a misspecified Cox model to data generated under a true transformation model. Data on survival of 1043 leukaemia patients are used for illustration.
Starting Page	41
Ending Page	64
Page Count	24
File Format	PDF
ISSN	18671764
Journal	Statistics in Biosciences
Volume Number	2
Issue Number	1
e-ISSN	18671772
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-06-08
Publisher Institution	International Chinese Statistical Association
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Box–Cox transformation Diagnostics Score test Schoenfeld residuals Misspecification Survival analysis Theoretical Ecology/Statistics Biostatistics Statistics for Life Sciences, Medicine, Health Sciences
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Biochemistry, Genetics and Molecular Biology

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