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  1. Book ; Online: Bayesian Design in Clinical Trials

    Berchialla, Paola / Baldi, Ileana

    2022  

    Keywords Humanities ; Social interaction ; dose-escalation ; combination study ; modelling assumption ; interaction ; adaptive designs ; adaptive randomization ; Bayesian designs ; clinical trials ; predictive power ; target allocation ; Bayesian inference ; highest posterior density intervals ; normal approximation ; predictive analysis ; sample size determination ; bayesian meta-analysis ; clustering ; binary data ; priors ; frequentist validation ; Bayesian ; rare disease ; prior distribution ; meta-analysis ; sample size ; bridging studies ; distribution distance ; oncology ; phase I ; dose-finding ; dose-response ; bayesian inference ; prior elicitation ; latent dirichlet allocation ; clinical trial ; power-prior ; poor accrual ; Bayesian trial ; cisplatin ; doxorubicin ; oxaliplatin ; dose escalation ; PIPAC ; peritoneal carcinomatosis ; randomized controlled trial ; causal inference ; doubly robust estimation ; propensity score ; Bayesian monitoring ; futility rules ; interim analysis ; posterior and predictive probabilities ; stopping boundaries ; Bayesian trial design ; early phase dose finding ; treatment combinations ; optimal dose combination
    Language 0|e
    Size 1 electronic resource (190 pages)
    Publisher MDPI - Multidisciplinary Digital Publishing Institute
    Publishing place Basel
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT021611498
    ISBN 9783036533339 ; 3036533338
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Article ; Online: Bioactive glass for periodontal regeneration: a systematic review.

    Motta, Chiara / Cavagnetto, Davide / Amoroso, Federico / Baldi, Ileana / Mussano, Federico

    BMC oral health

    2023  Volume 23, Issue 1, Page(s) 264

    Abstract: Background: One of the major clinical challenges of this age could be represented by the possibility to obtain a complete regeneration of infrabony defects. Over the past few years, numerous materials and different approaches have been developed to ... ...

    Abstract Background: One of the major clinical challenges of this age could be represented by the possibility to obtain a complete regeneration of infrabony defects. Over the past few years, numerous materials and different approaches have been developed to obtain bone and periodontal healing. Among all biomaterials, bioglasses (BG) are one of the most interesting due to their ability to form a highly reactive carbonate hydroxyapatite layer. Our aim was to systematically review the literature on the use and capability of BG for the treatment of periodontal defects and to perform a meta-analysis of their efficacy.
    Methods: A search of MEDLINE/PubMed, Cochrane Library, Embase and DOSS was conducted in March 2021 to identify randomized controlled trials (RCTs) using BG in the treatment of intrabony and furcation defects. Two reviewers selected the articles included in the study considering the inclusion criteria. The outcomes of interest were periodontal and bone regeneration in terms of decrease of probing depth (PD) and gain of clinical attachment level (CAL). A network meta-analysis (NMA) was fitted, according to the graph theory methodology, using a random effect model.
    Results: Through the digital search, 46 citations were identified. After duplicate removal and screening process, 20 articles were included. All RCTs were retrieved and rated following the Risk of bias 2 scale, revealing several potential sources of bias. The meta-analysis focused on the evaluation at 6 months, with 12 eligible articles for PD and 10 for CAL. As regards the PD at 6 months, AUTOGENOUS CORTICAL BONE, BIOGLASS and PLATELET RICH FIBRIN were more efficacious than open flap debridement alone, with a statistically significant standardized mean difference (SMD) equal to -1.57, -1.06 and - 2.89, respectively. As to CAL at 6 months, the effect of BIOGLASS is reduced and no longer significant (SMD = -0.19, p-value = 0.4) and curiously PLATELET RICH FIBRIN was more efficacious than OFD (SMD =-4.13, p-value < 0.001) in CAL gain, but in indirect evidence.
    Conclusions: The present review partially supports the clinical efficacy of BG in periodontal regeneration treatments for periodontal purposes. Indeed, the SMD of 0.5 to 1 in PD and CAL obtained with BG compared to OFD alone seem clinically insignificant even if it is statistically significant. Heterogeneity sources related to periodontal surgery are multiple, difficult to assess and likely hamper a quantitative assessment of BG efficacy.
    MeSH term(s) Humans ; Biocompatible Materials/therapeutic use ; Bone Regeneration ; Dental Care ; Durapatite ; Furcation Defects
    Chemical Substances Biocompatible Materials ; Durapatite (91D9GV0Z28)
    Language English
    Publishing date 2023-05-08
    Publishing country England
    Document type Systematic Review ; Meta-Analysis ; Journal Article
    ZDB-ID 2091511-1
    ISSN 1472-6831 ; 1472-6831
    ISSN (online) 1472-6831
    ISSN 1472-6831
    DOI 10.1186/s12903-023-02898-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Prediction of treatment outcome in clinical trials under a personalized medicine perspective.

    Berchialla, Paola / Lanera, Corrado / Sciannameo, Veronica / Gregori, Dario / Baldi, Ileana

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 4115

    Abstract: A central problem in most data-driven personalized medicine scenarios is the estimation of heterogeneous treatment effects to stratify individuals into subpopulations that differ in their susceptibility to a particular disease or response to a specific ... ...

    Abstract A central problem in most data-driven personalized medicine scenarios is the estimation of heterogeneous treatment effects to stratify individuals into subpopulations that differ in their susceptibility to a particular disease or response to a specific treatment. In this work, with an illustrative example on type 2 diabetes we showed how the increasing ability to access and analyzed open data from randomized clinical trials (RCTs) allows to build Machine Learning applications in a framework of personalized medicine. An ensemble machine learning predictive model is first developed and then applied to estimate the expected treatment response according to the medication that would be prescribed. Machine learning is quickly becoming indispensable to bridge science and clinical practice, but it is not sufficient on its own. A collaborative effort is requested to clinicians, statisticians, and computer scientists to strengthen tools built on machine learning to take advantage of this evidence flow.
    MeSH term(s) Diabetes Mellitus, Type 2/drug therapy ; Humans ; Machine Learning ; Precision Medicine ; Randomized Controlled Trials as Topic ; Treatment Outcome
    Language English
    Publishing date 2022-03-08
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-07801-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review.

    Azzolina, Danila / Berchialla, Paola / Gregori, Dario / Baldi, Ileana

    International journal of environmental research and public health

    2021  Volume 18, Issue 4

    Abstract: Bayesian inference is increasingly popular in clinical trial design and analysis. The subjective knowledge derived from an expert elicitation procedure may be useful to define a prior probability distribution when no or limited data is available. This ... ...

    Abstract Bayesian inference is increasingly popular in clinical trial design and analysis. The subjective knowledge derived from an expert elicitation procedure may be useful to define a prior probability distribution when no or limited data is available. This work aims to investigate the state-of-the-art Bayesian prior elicitation methods with a focus on clinical trial research. A literature search on the Current Index to Statistics (CIS), PubMed, and Web of Science (WOS) databases, considering "prior elicitation" as a search string, was run on 1 November 2020. Summary statistics and trend of publications over time were reported. Finally, a Latent Dirichlet Allocation (LDA) model was developed to recognise latent topics in the pertinent papers retrieved. A total of 460 documents pertinent to the Bayesian prior elicitation were identified. Of these, 213 (45.4%) were published in the "Probability and Statistics" area. A total of 42 articles pertain to clinical trial and the majority of them (81%) reports parametric techniques as elicitation method. The last decade has seen an increased interest in prior elicitation and the gap between theory and application getting narrower and narrower. Given the promising flexibility of non-parametric approaches to the experts' elicitation, more efforts are needed to ensure their diffusion also in applied settings.
    MeSH term(s) Bayes Theorem ; Probability ; Research Design
    Language English
    Publishing date 2021-02-13
    Publishing country Switzerland
    Document type Journal Article ; Review
    ISSN 1660-4601
    ISSN (online) 1660-4601
    DOI 10.3390/ijerph18041833
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Prediction of treatment outcome in clinical trials under a personalized medicine perspective

    Paola Berchialla / Corrado Lanera / Veronica Sciannameo / Dario Gregori / Ileana Baldi

    Scientific Reports, Vol 12, Iss 1, Pp 1-

    2022  Volume 8

    Abstract: Abstract A central problem in most data-driven personalized medicine scenarios is the estimation of heterogeneous treatment effects to stratify individuals into subpopulations that differ in their susceptibility to a particular disease or response to a ... ...

    Abstract Abstract A central problem in most data-driven personalized medicine scenarios is the estimation of heterogeneous treatment effects to stratify individuals into subpopulations that differ in their susceptibility to a particular disease or response to a specific treatment. In this work, with an illustrative example on type 2 diabetes we showed how the increasing ability to access and analyzed open data from randomized clinical trials (RCTs) allows to build Machine Learning applications in a framework of personalized medicine. An ensemble machine learning predictive model is first developed and then applied to estimate the expected treatment response according to the medication that would be prescribed. Machine learning is quickly becoming indispensable to bridge science and clinical practice, but it is not sufficient on its own. A collaborative effort is requested to clinicians, statisticians, and computer scientists to strengthen tools built on machine learning to take advantage of this evidence flow.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article: Editorial: Methodological Approaches for Quantitative Nursing Research.

    Baldi, Ileana / Gregori, Dario

    The open nursing journal

    2017  Volume 11, Page(s) 142–143

    Language English
    Publishing date 2017-10-31
    Publishing country United Arab Emirates
    Document type Editorial
    ZDB-ID 2395986-1
    ISSN 1874-4346
    ISSN 1874-4346
    DOI 10.2174/1874434601711010142
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Prior Elicitation for Use in Clinical Trial Design and Analysis

    Danila Azzolina / Paola Berchialla / Dario Gregori / Ileana Baldi

    International Journal of Environmental Research and Public Health, Vol 18, Iss 1833, p

    A Literature Review

    2021  Volume 1833

    Abstract: Bayesian inference is increasingly popular in clinical trial design and analysis. The subjective knowledge derived from an expert elicitation procedure may be useful to define a prior probability distribution when no or limited data is available. This ... ...

    Abstract Bayesian inference is increasingly popular in clinical trial design and analysis. The subjective knowledge derived from an expert elicitation procedure may be useful to define a prior probability distribution when no or limited data is available. This work aims to investigate the state-of-the-art Bayesian prior elicitation methods with a focus on clinical trial research. A literature search on the Current Index to Statistics (CIS), PubMed, and Web of Science (WOS) databases, considering “prior elicitation” as a search string, was run on 1 November 2020. Summary statistics and trend of publications over time were reported. Finally, a Latent Dirichlet Allocation (LDA) model was developed to recognise latent topics in the pertinent papers retrieved. A total of 460 documents pertinent to the Bayesian prior elicitation were identified. Of these, 213 (45.4%) were published in the “Probability and Statistics” area. A total of 42 articles pertain to clinical trial and the majority of them (81%) reports parametric techniques as elicitation method. The last decade has seen an increased interest in prior elicitation and the gap between theory and application getting narrower and narrower. Given the promising flexibility of non-parametric approaches to the experts’ elicitation, more efforts are needed to ensure their diffusion also in applied settings.
    Keywords prior elicitation ; latent dirichlet allocation ; clinical trial ; Medicine ; R
    Subject code 310
    Language English
    Publishing date 2021-02-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Editorial: Children's Health from Global Determinants to Local Consequences: The Indian Perspective.

    Gregori, Dario / Gulati, Achal / Baldi, Ileana

    Indian journal of pediatrics

    2019  Volume 86, Issue Suppl 1, Page(s) 1–2

    MeSH term(s) Child Health ; Hospitalization ; Humans ; India ; Life Style ; Menstrual Cycle ; Pediatric Obesity ; Public Health ; Risk Factors
    Language English
    Publishing date 2019-01-09
    Publishing country India
    Document type Editorial ; Introductory Journal Article
    ZDB-ID 218231-2
    ISSN 0973-7693 ; 0019-5456
    ISSN (online) 0973-7693
    ISSN 0019-5456
    DOI 10.1007/s12098-018-2823-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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  9. Article ; Online: A Bayesian Sample Size Estimation Procedure Based on a B-Splines Semiparametric Elicitation Method.

    Azzolina, Danila / Berchialla, Paola / Bressan, Silvia / Da Dalt, Liviana / Gregori, Dario / Baldi, Ileana

    International journal of environmental research and public health

    2022  Volume 19, Issue 21

    Abstract: Sample size estimation is a fundamental element of a clinical trial, and a binomial experiment is the most common situation faced in clinical trial design. A Bayesian method to determine sample size is an alternative solution to a frequentist design, ... ...

    Abstract Sample size estimation is a fundamental element of a clinical trial, and a binomial experiment is the most common situation faced in clinical trial design. A Bayesian method to determine sample size is an alternative solution to a frequentist design, especially for studies conducted on small sample sizes. The Bayesian approach uses the available knowledge, which is translated into a prior distribution, instead of a point estimate, to perform the final inference. This procedure takes the uncertainty in data prediction entirely into account. When objective data, historical information, and literature data are not available, it may be indispensable to use expert opinion to derive the prior distribution by performing an elicitation process. Expert elicitation is the process of translating expert opinion into a prior probability distribution. We investigated the estimation of a binomial sample size providing a generalized version of the average length, coverage criteria, and worst outcome criterion. The original method was proposed by Joseph and is defined in a parametric framework based on a Beta-Binomial model. We propose a more flexible approach for binary data sample size estimation in this theoretical setting by considering parametric approaches (Beta priors) and semiparametric priors based on B-splines.
    MeSH term(s) Sample Size ; Bayes Theorem ; Research Design ; Models, Statistical ; Probability
    Language English
    Publishing date 2022-10-31
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2175195-X
    ISSN 1660-4601 ; 1661-7827
    ISSN (online) 1660-4601
    ISSN 1661-7827
    DOI 10.3390/ijerph192114245
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Use of Sequential Multiple Assignment Randomized Trials (SMARTs) in oncology: systematic review of published studies.

    Lorenzoni, Giulia / Petracci, Elisabetta / Scarpi, Emanuela / Baldi, Ileana / Gregori, Dario / Nanni, Oriana

    British journal of cancer

    2022  Volume 128, Issue 7, Page(s) 1177–1188

    Abstract: Sequential multiple assignments randomized trials (SMARTs) are a type of experimental design where patients may be randomised multiple times according to pre-specified decision rules. The present work investigates the state-of-the-art of SMART designs in ...

    Abstract Sequential multiple assignments randomized trials (SMARTs) are a type of experimental design where patients may be randomised multiple times according to pre-specified decision rules. The present work investigates the state-of-the-art of SMART designs in oncology, focusing on the discrepancy between the available methodological approaches in the statistical literature and the procedures applied within cancer clinical trials. A systematic review was conducted, searching PubMed, Embase and CENTRAL for protocols or reports of results of SMART designs and registrations of SMART designs in clinical trial registries applied to solid tumour research. After title/abstract and full-text screening, 33 records were included. Fifteen were reports of trials' results, four were trials' protocols and fourteen were trials' registrations. The study design was defined as SMART by only one out of fifteen trial reports. Conversely, 13 of 18 study protocols and trial registrations defined the study design SMART. Furthermore, most of the records considered each stage separately in the analysis, without considering treatment regimens embedded in the trial. SMART designs in oncology are still limited. Study powering and analysis is mainly based on statistical approaches traditionally used in single-stage parallel trial designs. Formal reporting guidelines for SMART designs are needed.
    MeSH term(s) Humans ; Randomized Controlled Trials as Topic ; Research Design ; Medical Oncology
    Language English
    Publishing date 2022-12-26
    Publishing country England
    Document type Systematic Review ; Journal Article ; Review
    ZDB-ID 80075-2
    ISSN 1532-1827 ; 0007-0920
    ISSN (online) 1532-1827
    ISSN 0007-0920
    DOI 10.1038/s41416-022-02110-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Uh III Zs.142: Show issues Location:
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