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Background Frailty is a dynamic state in older adults. Current evidence, mostly in high-income countries, found that improving frailty is more likely in mild states (prefrailty). We aimed to determine the probability of frailty transitions and their predictors. Methods Participants were adults aged 50 years or over from the Study on Global Ageing and Adult Health in Mexico during 4 waves (2009, 2014, 2017, and 2021). We defined frailty with the frailty phenotype and we used multinomial logistic models to estimate the probabilities of frailty transitions and determine their predictors. Results For the 3 analyzed periods (2009–2014, 2014–2017, and 2017–2021), transition probabilities from frail to robust were higher for the younger age group (50–59 years) at 0.20, 0.26, and 0.20, and lower for the older age group (≥80 years), 0.03, 0.08 and 0.04. Transitioning from prefrail to robust had probabilities of 0.38, 0.37, and 0.35, for the younger age group, and 0.09, 0.18, and 0.10, for the older age group. The probabilities of transitioning to frail and to death were lower for the younger age group and for the robust at baseline; but higher for the older age group and for the frail at baseline. We identified age, disability, and diabetes as the most significant predictors of frailty transitions. Conclusions These findings show that frailty has a dynamic nature and that a significant proportion of prefrail and frail individuals can recover to a robust or prefrail state. They also emphasize that prefrailty should be the focus of interventions. [ABSTRACT FROM AUTHOR]

We provide a novel characterization of mutual independence of random variables based on the checkerboard approximation to a multivariate copula. [ABSTRACT FROM AUTHOR]

We consider a robust Bayesian approach to the analysis of item response models, using the inverse of an asymmetric exponential power cumulative distribution function as a link function. This provides greater flexibility with respect to classic link functions such as the probit and the logit. We conduct a simulation study to evaluate the performance of our model. In order to draw samples from the posterior distribution of the parameters, we implement a Markov chain Monte Carlo scheme by means of the JAGS software. We also implement a posterior predictive model-checking method to assess the fit and relative performance of the various submodels. Finally, we provide a real-data example to illustrate the modeling approach proposed. [ABSTRACT FROM AUTHOR]

We propose an extension of parametric product partition models. We name our proposal nonparametric product partition models because we associate a random measure instead of a parametric kernel to each set within a random partition. Our methodology does not impose any specific form on the marginal distribution of the observations, allowing us to detect shifts of behaviour even when dealing with heavy-tailed or skewed distributions. We propose a suitable loss function and find the partition of the data having minimum expected loss. We then apply our nonparametric procedure to multiple change-point analysis and compare it with PPMs and with other methodologies that have recently appeared in the literature. Also, in the context of missing data, we exploit the product partition structure in order to estimate the distribution function of each missing value, allowing us to detect change points using the loss function mentioned above. Finally, we present applications to financial as well as genetic data. [ABSTRACT FROM AUTHOR]

It is well known that Jeffreys’ prior is asymptotically least favorable under the entropy risk, i.e. it asymptotically maximizes the mutual information between the sample and the parameter. However, in this paper we show that the prior that minimizes (subject to certain constraints) the mutual information between the sample and the parameter is natural conjugate when the model belongs to a natural exponential family. A conjugate prior can thus be regarded as maximally informative in the sense that it minimizes the weight of the observations on inferences about the parameter; in other words, the expected relative entropy between prior and posterior is minimized when a conjugate prior is used. [ABSTRACT FROM AUTHOR]