Competing Hazards Regression Parameter Estimation Under Different Informative Priors

In the analysis of survival data, cause specific quantities of competing risks get considerable attention as compared to latent failure time approach. This article focuses on parametric regression analysis of survival data using cause specific hazard function with Burr type XII distribution as a baseline model. We obtained maximum likelihood and Bayes estimates of cumulative cause specific hazard functions under competing risk setup. For Bayesian point of view we proposed a class of informative priors for parameters to observe the comprehensive compatibility and their effectiveness under two different loss functions. The appropriateness of model is measured by the simulation study. Finally, we illustrate the proposed methodologies using bone marrow transplant data from the Princess Margaret Hospital Ontario, Canada.

Training of intensive care nurses to handle continuous hemodialysis: a latent condition for safety

ABSTRACT Objective: To analyze the repercussions of the training of nurses working in the Intensive Care Unit for the management of continuous hemodialysis regarding the safety of critical patients with acute renal failure. Method: Qualitative research developed considering James Reason's reference in the Intensive Care Unit of a private hospital. The data was collected with 23 nurses who worked for more than three months in the management of continuous hemodialysis through a semi-structured interview, and analyzed with the thematic content analysis technique. Results: There are weaknesses in the training of intensive care nurses for the management of continuous hemodialysis that become a latent failure. Such a failure results in difficulties in handling hemodialysis, with risks for these professionals to commit active failures. Final considerations: The training program in service for the management of continuous hemodialysis must be perfected, to develop skills and competencies in nurses and improve their performance.