Multivariate Bayesian meta-analysis: joint modelling of multiple cancer types using summary statistics

Creating area level indices of behaviours impacting cancer in Australia with a Bayesian generalised shared component model

This study develops a model-based index approach called the Generalised Shared Component Model (GSCM) by drawing on the large field of factor models. The proposed fully Bayesian approach accommodates heteroscedastic model error, multiple shared factors and flexible spatial priors. Moreover, unlike previous index approaches, our model provides indices with uncertainty. Focusing on unhealthy behaviors that increase the risk of cancer, the proposed GSCM is used to develop the Area Indices of Behaviors Impacting Cancer product - representing the first area level cancer risk factor index in Australia. This advancement aids in identifying communities with elevated cancer risk, facilitating targeted health interventions.

Multiorientation Simultaneous Computation of Back-Projection CT Image Reconstruction Algorithm in Staging Diagnosis of Bladder Cancer

The objective of this research was to investigate the multidirectional synchronous calculation of the back-projection computed tomography (CT) image reconstruction algorithm (MSBP) in the staging diagnosis of bladder cancer. Sixty patients with bladder cancer admitted to the hospital were selected for enhanced CT scanning, all of which were randomly divided into control group (n = 30) and study group (n = 30). The filtered back-projection (FBP) algorithm was employed to reconstruct the scanned image, and the MSBP was additionally applied to the images of the study group. Fringe artifact (SA), overall mass (OQ), effective radiation dose (ED), CT dose-exponential volume (CTDI), and dose-length product (DLP) of the two groups of images were compared and analyzed. The results showed that the total time of the traditional algorithm was 5.473 s, and the total time of MSBP combined with FBP algorithm was 2.832 s, which was significantly higher than that of the traditional algorithm (P < 0.05). CT scan bladder cancer staging results of all patients were compared with surgical pathological staging results, and the results were evaluated according to the coincidence rate. SA in the study group was lower than that in the control group (P < 0.05), and OQ was not statistically significant. The ED of the study group was significantly lower than that of the control group by 33%. The coincidence rate of postoperative pathological staging results and CT staging results was 96%, and T1, T2a, and T4 coincidence rate was 100%, The coincidence rates of T2b, T3a, and T3b were 90%, 83.3%, and 66.67%, respectively. In summary, using MSBP method combined with FBP algorithm can improve OQ while reducing ED of patients. The introduction of MSBP into CT reconstruction image simplified the pixel location operation of projection calculation, showing an important application value in preoperative staging diagnosis of bladder cancer.

Using Bayesian Meta-Regression to Advance Prevention Science Research: an Introduction and Empirical Illustration

Driven by the movement of evidence-based practices, Bayesian statistical methods have become increasingly popular. This paper introduces a Bayesian approach to meta-regression, focusing on the use and implementation in prevention science research. We first compare Bayesian meta-analysis and meta-regression to a frequentist approach. Thereafter, we illustrate Bayesian methods in meta-regression, highlighting advantages, providing detailed interpretation, and presenting results. The example is completed using several R packages. We also provide annotated R code for readers as a foundation for their own research.