Kidney size in relation to ageing, gender, renal function, birthweight and chronic kidney disease risk factors in a general population

Background: The relationship of kidney size to ageing, kidney function and kidney disease risk factors is not fully understood. Methods: Ultrasound length and parenchymal kidney volume were determined from a population-based sample of 3972 Sardinians (age range 18-100 years). We then identified the subset of 2256 'healthy' subjects to define age- and sex-specific reference ranges (2.5-97.5 percentile) of kidney volume. Logistic regression (accounting for family clustering) was used to identify the clinical characteristics associated with abnormally large kidneys or abnormally small kidneys. Results: In the healthy subset, kidney volume and length increased up to the fourth to fifth decade of life followed by a progressive decrease in men, whereas there was a gradual kidney volume decrease throughout the lifespan of women. In the whole sample, independent predictors of lower kidney volume (97.5 percentile for age and sex) were younger age, female sex, diabetes, obesity, high height, high waist:hip ratio and lower SCr. Estimated heritability for kidney volume was 15%, and for length 27%; kidney volume correlated strongly with birthweight. Conclusions: Overall, in a general healthy population, kidney measures declined with age differently in men and women. The determinants of kidney parenchymal volume include genetic factors and modifiable clinical factors

On interval and point estimators based on a penalization of the modified profile likelihood

In the presence of a nuisance parameter, one widely shared approach to likelihood inference on a scalar parameter of interest is based on the profile likelihood and its various modifications. In this paper, we add a penalization to the modified profile likelihood, which is based on a suitable matching prior, and we discuss the frequency properties of interval estimators and point estimators based on this penalized modified profile likelihood. Two simulation studies are illustrated, and we indicate the improvement of the proposed penalized modified profile likelihood over its counterparts

Higher-order asymptotics in Bayesian inference

This paper reviews recent developments in higher-order asymptotics for marginal posterior distributions, and related quantities, for practical use in Bayesian analysis. In this respect, we outline how modern asymptotic theory, which provides accurate inferences in a variety of parametric statistical problems even for small sample sizes, may routinely be applied in practice. The focus is on default Bayesian inference in the presence of nuisance parameter

A note on the relationships between Bayesian and non-Bayesian predictive inference

Let us consider a random vector Y distributed according to a statistical model depending on an unknown parameter vector. Suppose we are interested in prediction, where the object of inference is a future or a yet unobserved random variable X, not depending on Y. The aim of this contribution is to provide some theoretical insight into the relationships between Bayesian and non-Bayesian higher-order asymptotic expansions for predictive densities. More precisely, we characterize prior probability distributionswhich guarantee the equivalence between Bayesian asymptotic expansions of the predictive distribution and frequentist accurate refinements of the estimative predictive density. An illustration in the context of the scalar skew-normal model is discussed. As a further result, invoking asymptotic connections between adjustments of the profile likelihood and asymptotic expansions of predictive densities, we illustrate how Bayesian predictive densities allows for the construction of adjusted profile loglikelihoods

A Bayesian adjustment of the modified profile likelihood

We propose an adjustment of the modified profile likelihood based on a suitable matching prior on the parameter of interest only, i.e. a prior for which there is an agreement between frequentist and Bayesian inference.We show that the proposed modified profile likelihood has several desiderable inferential properties. Two examples are illustrated