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Bayesian first order auto-regressive latent variable models for multiple binary sequences

Giardina, F. and Guglielmi, A. and Quintana, F. A. and Ruggeri, F.. (2011) Bayesian first order auto-regressive latent variable models for multiple binary sequences. Statistical modelling , 11 (6). pp. 471-488.

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Official URL: http://edoc.unibas.ch/47145/

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Abstract

Longitudinal clinical trials often collect long sequences of binary data monitoring a disease process over time. Our application is a medical study conducted in the US by the Veterans Administration Cooperative Urological Research Group to assess the effectiveness of a chemotherapy treatment (thiotepa) in preventing recurrence on subjects affected by bladder cancer. We propose a generalized linear model with latent auto-regressive structure for longitudinal binary data following a Bayesian approach. We discuss inference as well as sensitivity to prior choices for the bladder cancer data. We find that there is a significant treatment effect in the sense that treated patients have much smaller predicted recurrence probabilities than placebo patients
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH)
UniBasel Contributors:Giardina, Federica
Item Type:Article, refereed
Publisher:Arnold
ISSN:1471-082X
Note:Publication type according to Uni Basel Research Database: Journal article
Last Modified:23 Mar 2017 15:10
Deposited On:23 Mar 2017 15:10

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