scispace - formally typeset
M

Maria Giovanna Ranalli

Researcher at University of Perugia

Publications -  9
Citations -  278

Maria Giovanna Ranalli is an academic researcher from University of Perugia. The author has contributed to research in topics: Small area estimation & Generalized linear mixed model. The author has an hindex of 6, co-authored 9 publications receiving 253 citations.

Papers
More filters
Journal ArticleDOI

Non‐parametric small area estimation using penalized spline regression

TL;DR: In this paper, a small area estimation approach that combines small area random effects with a smooth, non-parametrically specified trend is proposed, where penalized splines are used as the representation for the nonparametric trend and the resulting model is readily fitted by using existing model fitting approaches such as restricted maximum likelihood.
Journal ArticleDOI

Variation of renal function over time is associated with major bleeding in patients treated with direct oral anticoagulants for atrial fibrillation.

TL;DR: In patients on treatment with direct anticoagulants (DOACs) variation of renal function is common and identifying conditions associated with variation of kidney function may increase safety of DOACs.
Journal ArticleDOI

Effects of β-Blockers With and Without Vasodilating Properties on Central Blood Pressure Systematic Review and Meta-Analysis of Randomized Trials in Hypertension

TL;DR: In this article, the authors conducted a systematic review and meta-analysis of randomized trials exploring the effects of β-blockers on both pSBP and cSBP in hypertension.
Journal ArticleDOI

Small area estimation of the mean using non-parametric M-quantile regression: a comparison when a linear mixed model does not hold

TL;DR: In this article, the authors investigate alternatives when a linear MM does not hold because, on one side, linearity may not be assumed and/or, on the other, normality of the random effects may not been assumed.
Journal ArticleDOI

Bedside sonography assessment of extravascular lung water increase after major pulmonary resection in non-small cell lung cancer patients.

TL;DR: The results suggest that LUS, due to its non-invasiveness, affordability and capacity to detect increases in EVLW, might be useful in better managing postoperative patients.