The University of Southampton
University of Southampton Institutional Repository

On the equivalence of posterior inference based on retrospective and prospective likelihoods: application to a case-control study of colorectal cancer

On the equivalence of posterior inference based on retrospective and prospective likelihoods: application to a case-control study of colorectal cancer
On the equivalence of posterior inference based on retrospective and prospective likelihoods: application to a case-control study of colorectal cancer
The paper develops a class of priors that leads to equivalent posterior inference for odds ratio parameters based on prospective and retrospective models for categorical response data. The results are applicable to both unmatched and matched case-control studies. The results hold for a general class of link functions for categorical response. The proposed method can accommodate multiple and possibly ordered disease states. The results are applied to the analysis of discrete subtypes in an ongoing case-control study of colorectal cancer. A simulation study illustrates the need for carefully considering prior choices in Bayesian analysis of data collected under retrospective design.
0277-6715
2196-2208
Ghosh, M.
bb05afba-621b-42ba-b793-3cdc7d84e730
Song, J.
e82ff981-9c67-4ff5-8971-cb518bd1b3db
Forster, J.J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Mitra, Robin
2b944cd7-5be8-4dd1-ab44-f8ada9a33405
Mukherjee, B.
717010dc-5f0d-45e0-a031-2cfb85bc29b1
Ghosh, M.
bb05afba-621b-42ba-b793-3cdc7d84e730
Song, J.
e82ff981-9c67-4ff5-8971-cb518bd1b3db
Forster, J.J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Mitra, Robin
2b944cd7-5be8-4dd1-ab44-f8ada9a33405
Mukherjee, B.
717010dc-5f0d-45e0-a031-2cfb85bc29b1

Ghosh, M., Song, J., Forster, J.J., Mitra, Robin and Mukherjee, B. (2012) On the equivalence of posterior inference based on retrospective and prospective likelihoods: application to a case-control study of colorectal cancer. Statistics in Medicine, 31 (20), 2196-2208. (doi:10.1002/sim.5358). (PMID:22495822)

Record type: Article

Abstract

The paper develops a class of priors that leads to equivalent posterior inference for odds ratio parameters based on prospective and retrospective models for categorical response data. The results are applicable to both unmatched and matched case-control studies. The results hold for a general class of link functions for categorical response. The proposed method can accommodate multiple and possibly ordered disease states. The results are applied to the analysis of discrete subtypes in an ongoing case-control study of colorectal cancer. A simulation study illustrates the need for carefully considering prior choices in Bayesian analysis of data collected under retrospective design.

Text
statmed2012.pdf - Version of Record
Restricted to Repository staff only
Request a copy

More information

Published date: 10 September 2012
Organisations: Statistical Sciences Research Institute

Identifiers

Local EPrints ID: 351748
URI: http://eprints.soton.ac.uk/id/eprint/351748
ISSN: 0277-6715
PURE UUID: b543e5f9-3bfc-4c79-b40b-98f99618fe60
ORCID for J.J. Forster: ORCID iD orcid.org/0000-0002-7867-3411

Catalogue record

Date deposited: 30 Apr 2013 12:17
Last modified: 15 Mar 2024 02:46

Export record

Altmetrics

Contributors

Author: M. Ghosh
Author: J. Song
Author: J.J. Forster ORCID iD
Author: Robin Mitra
Author: B. Mukherjee

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×