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.
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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.
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Variation of renal function over time is associated with major bleeding in patients treated with direct oral anticoagulants for atrial fibrillation.
Cecilia Becattini,Michela Giustozzi,Maria Giovanna Ranalli,G. Bogliari,F Cianella,Melina Verso,Giancarlo Agnelli,Maria Cristina Vedovati +7 more
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.
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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.
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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.
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Bedside sonography assessment of extravascular lung water increase after major pulmonary resection in non-small cell lung cancer patients.
Lucio Cagini,Marco Andolfi,Cecilia Becattini,Maria Giovanna Ranalli,Francesco Bartolucci,A. Mancuso,Jacopo Vannucci,Giancarlo Agnelli,Francesco Puma +8 more
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.