CodiceQDD 41
TitoloBayesian first order autoregressive latent variable models for multiple binary sequences
Data2009-01-22
Autore/iGiardina, F.; Guglielmi, A.; Quintana, F. A.; Ruggeri, F.
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AbstractLongitudinal clinical trials often collect long sequences of binary data monitoring a disease process over time. Our application is a medical study conducted by VACURG to assess the effectiveness of a chemioterapic treatment (thiotepa) in preventing recurrence on subjects affected by bladder cancer. We propose a generalized linear model with latent autoregressive structure for longitudinal binary data following a Bayesian approach. We describe a suitable posterior simulation scheme and discuss inference and sensitivity issues for the bladder cancer data.