0000000000358461
AUTHOR
A.m. Mayoral
Bayesian Design of “Successful” Replications
Replication of experiments is commonin applied research. However, systematic studies of the goals and motivations of a “replication” are rare. As a consequence, there does not seem to be a precise notion of what a “success” when replicating means. This article discusses some of the possible goals for replication; this leads to different (but precise) notions of “success” when replicating. Bayesian hierarchical models allow for a flexible and explicit incorporation of the assumed relationship among the experiments. Bayesian predictive distributions are a natural tool to compute the probability of the replication being successful, and hence to design the replication so that the probability of…
Trends in hospitalizations and deaths in HIV-infected patients in Spain over two decades.
Background The prognosis of HIV infection dramatically improved after the introduction of triple antiretroviral therapy 25 years ago. Herein, we report the impact of further improvements in HIV management since then, looking at all hospitalizations in persons living with HIV (PLWH) in Spain. Methods Retrospective study using the Spanish National Registry of Hospital Discharges. Information was retrieved since 1997 to 2018. Results From 79,647,783 nationwide hospital admissions recorded during the study period, 532,668 (0.67%) included HIV as diagnosis. The mean age of PLWH hospitalized increased from 33 to 51 years-old (p < 0.001). The rate of HIV hospitalizations significantly declined aft…
Bayesian design in queues: An application to aeronautic maintenance
We exploit Bayesian criteria for designing M/M/c//r queueing systems with spares. For illustration of our approach we use a real problem from aeronautic maintenance, where the numbers of repair crews and spare planes must be sufficiently large to meet the necessary operational capacity. Bayesian guarantees for this to happen can be given using predictive or posterior distributions.
Bayesian analysis and design for comparison of effect-sizes
Comparison of effect-sizes, or more generally, of non-centrality parameters of non-central t distributions, is a common problem, especially in meta-analysis. The usual simplifying assumptions of either identical or non-related effect-sizes are often too restrictive to be appropriate. In this paper, the effect-sizes are modeled as random effects with t distributions. Bayesian hierarchical models are used both to design and analyze experiments. The main goal is to compare effect-sizes. Sample sizes are chosen so as to make accurate inferences about the difference of effect-sizes and also to convincingly solve the testing of equality of effect-sizes if such is the goal.