A Bayesian analysis for the bivariate geometric distribution in the presence of covariates and censored data

In this paper, we introduce a Bayesian analysis for bivariate geometric distributions applied to lifetime data in the presence of covariates and censored data using Markov Chain Monte Carlo (MCMC) methods. We show that the use of a discrete bivariate geometric distribution could bring us some computational advantages when compared to standard existing bivariate exponential lifetime distributions introduced in the literature assuming continuous lifetime data as for example, the exponential Block and Basu bivariate distribution. Posterior summaries of interest are obtained using the popular OpenBUGS software. A numerical illustration is introduced considering a medical data set related to the recurrence times of infection for kidney patients

N-Terminal Pro-B-Type Natriuretic Peptide-Guided Therapy in Chronic Heart Failure Reduces Repeated Hospitalizations-Results From TIME-CHF

Background: Although heart failure (HF) patients are known to experience repeated hospitalizations, most studies evaluated only time to first event. N -Terminal B-type natriuretic peptide (NT-proBNP) guided therapy has not convincingly been shown to improve HF-specific outcomes, and effects on recurrent all-cause hospitalization are uncertain. Therefore, we investigated the effect of NT-proBNP guided therapy on recurrent events in HF with the use of a time-between-events approach in a hypothesis-generating analysis. Methods and Results: The Trial of Intensified Versus Standard Medical Therapy in Elderly Patients With Congestive Heart Failure (TIME-CHF) randomized 499 HF patients, aged >= 60 years, left ventricular ejection fraction = 2 all-cause hospitalization events. Regarding HF hospitalization, 132 patients (57 NT-proBNP guided, 75 symptom-guided) experienced 1 and 122 patients (57 NT-proBNP guided, 65 symptom-guided) experienced events. NT-proBNP guided therapy was significant in preventing 2nd all-cause hospitalizations (hazard ratio [HR] 0.83; P =.01), in contrast to nonsignificant results in preventing 1st all-cause hospitalization events (HR 0.91; P =.35). This was not the case regarding HF hospitalization events (HR 0.85 [P =.14] vs HR 0.73 [P =.01]) The beneficial effect of NT-proBNP guided therapy was seen only in patients aged <75 years, and not in those aged years (interaction terms with P =.01 and P =.03 for all-cause hospitalization and HF hospitalization events, respectively). Conclusion: NT-prciBNP guided therapy reduces the risk of recurrent events in patients <75 years of age. This included all-cause hospitalization by mainly reducing later events, adding knowledge to the neutral effect on this end point when shown using time-to-first-event analysis only

Tumour infiltrating lymphocytes and survival after adjuvant chemotherapy in patients with gastric cancer:post-hoc analysis of the CLASSIC trial

Background: Only a subset of gastric cancer (GC) patients with stage II–III benefits from chemotherapy after surgery. Tumour infiltrating lymphocytes per area (TIL density) has been suggested as a potential predictive biomarker of chemotherapy benefit. Methods: We quantified TIL density in digital images of haematoxylin-eosin (HE) stained tissue using deep learning in 307 GC patients of the Yonsei Cancer Center (YCC) (193 surgery+adjuvant chemotherapy [S + C], 114 surgery alone [S]) and 629 CLASSIC trial GC patients (325 S + C and 304 S). The relationship between TIL density, disease-free survival (DFS) and clinicopathological variables was analysed. Results: YCC S patients and CLASSIC S patients with high TIL density had longer DFS than S patients with low TIL density (P = 0.007 and P = 0.013, respectively). Furthermore, CLASSIC patients with low TIL density had longer DFS if treated with S + C compared to S (P = 0.003). No significant relationship of TIL density with other clinicopathological variables was found. Conclusion: This is the first study to suggest TIL density automatically quantified in routine HE stained tissue sections as a novel, clinically useful biomarker to identify stage II–III GC patients deriving benefit from adjuvant chemotherapy. Validation of our results in a prospective study is warranted.</p