Factors involved in health-related transitions after curative resection for pancreatic cancer. 10-years experience: a multi state model.

BACKGROUND: Pancreatic cancer is one of the least common tumours, nevertheless it is one of the most lethal. This lethality is mainly due to the fact that the vast majority of patients are diagnosed in an advanced stage. The purpose of this study was to investigate how different covariates affect the transition to death or discharge with and without complications after pancreatic resection. METHODS: We analyse the impact of different factors on transitions after pancreatic resection based on a multi state model. RESULTS: Transitions of interest include the transition to death/discharge with/without complications after pancreatic resection. We consider presence of comorbidities, higher age (>60), gender-male, lower hospital volume (<10 cases per year), type of surgery, localization of tumour and transfusion received as covariates with a potentially negative effect on the transition intensities to death with or without complications. CONCLUSIONS: The multi-state model allows for a very detailed analysis of the impact of covariates on each transition, since effects of covariates may change depending on the current state of the patient, thus helping surgeons and patients throughout the surgical process and counselling patients if needed.

Bayesian bivariate meta-analysis of diagnostic test studies using integrated nested Laplace approximations.

For bivariate meta-analysis of diagnostic studies, likelihood approaches are very popular. However, they often run into numerical problems with possible non-convergence. In addition, the construction of confidence intervals is controversial. Bayesian methods based on Markov chain Monte Carlo (MCMC) sampling could be used, but are often difficult to implement, and require long running times and diagnostic convergence checks. Recently, a new Bayesian deterministic inference approach for latent Gaussian models using integrated nested Laplace approximations (INLA) has been proposed. With this approach MCMC sampling becomes redundant as the posterior marginal distributions are directly and accurately approximated. By means of a real data set we investigate the influence of the prior information provided and compare the results obtained by INLA, MCMC, and the maximum likelihood procedure SAS PROC NLMIXED. Using a simulation study we further extend the comparison of INLA and SAS PROC NLMIXED by assessing their performance in terms of bias, mean-squared error, coverage probability, and convergence rate. The results indicate that INLA is more stable and gives generally better coverage probabilities for the pooled estimates and less biased estimates of variance parameters. The user-friendliness of INLA is demonstrated by documented R-code.

Bayesian 2-D deconvolution: a model for diffuse ultrasound scattering.

Observed medical ultrasound images are degraded representations of the true acoustic tissue reflectance. The degradation is due to blur and speckle and significantly reduces the diagnostic value of the images. To remove both blur and speckle, we have developed a new statistical model for diffuse scattering in 2-D ultrasound radio frequency images, incorporating both spatial smoothness constraints and a physical model for diffuse scattering. The modeling approach is Bayesian in nature, and we use Markov chain Monte Carlo methods to obtain the restorations. The results from restorations of some real and simulated radio frequency ultrasound images are presented and compared with results produced by Wiener filtering.

Within-session responding when different reinforcers are delivered in each half of the session.

The present study investigated whether within-session responding was specific to the reinforcer currently being delivered and whether it was determined solely by retrospective factors. In four separate experiments, four rats pressed a lever on a multiple variable interval 60-s variable interval 60-s schedule during 60-min sessions. A different reinforcer (5% liquid sucrose or food pellets) was delivered in each half of the session. Rate of reinforcement in one half of the session varied across conditions. Response patterns in the second half of the session were unaffected by changes in the conditions of reinforcement of the other reinforcer in the first half of the session (specificity). Rate of responding was affected, however. The upcoming reinforcer influenced responding when sucrose was delivered in the first half of the session and food pellets were delivered in the second half, but not when their order was reversed. This effect makes contact with several other areas of research (e.g. behavioral contrast). They also suggest that the leading explanations for within-session changes in responding may be limited or incomplete.