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Model checking techniques for regression models in cancer screening

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Author Info
Debashis Ghosh (University of Michigan)
Abstract

There has been much work on developing statistical procedures for associating tumor size with the probability of detecting a metastasis. Recently, Ghosh (2004) developed a unified statistical framework in which equivalences with censored data structures and models for tumor size and metastasis were examined. Based on this framework, we consider model checking techniques for semiparametric regression models in this paper. The procedures are for checking the additive hazards model. Goodness of fit methods are described for assessing functional form of covariates as well as the additive hazards assumption. The finite-sample properties of the methods are assessed using simulation studies.

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File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1036&context=umichbiostat
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Publisher Info
Paper provided by Berkeley Electronic Press in its series The University of Michigan Department of Biostatistics Working Paper Series with number 1036.

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Date of creation: 11 Jul 2004
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Handle: RePEc:bep:mchbio:1036

Note: oai:bepress.com:umichbiostat-1036
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Related research
Keywords: additive risk; empirical process; interval censoring; regression diagnostic; right censoring;

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References listed on IDEAS
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  1. D. Y. Lin & L. J. Wei & I. Yang & Z. Ying, 2000. "Semiparametric regression for the mean and rate functions of recurrent events," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 711-730. [Downloadable!] (restricted)
  2. Debashis Ghosh, 2004. "Nonparametric and semiparametric inference for models of tumor size and metastasis," The University of Michigan Department of Biostatistics Working Paper Series 1035, Berkeley Electronic Press. [Downloadable!]
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This page was last updated on 2009-12-15.


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