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Sample size estimation for comparing dynamic treatment regimens in a SMART: a Monte Carlo-based approach and case study with longitudinal overdispersed count outcomes

arXiv

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arXiv 2021年

作者： Yap, Jamie Dziak, John J. Maiti, Raju Lynch, Kevin McKay, James R. Chakraborty, Bibhas Nahum-Shani, Inbal Institute for Social Research Ann ArborMI United States Bennett Pierce Prevention Research Center Pennsylvania State University PA United States Economic Research Unit Indian Statistical Institute Kolkata India Department of Psychiatry University of Pennsylvania PA United States Philadephia Veterans Affairs Medical Center PA United States Centre for Quantitative Medicine Duke-NUS Medical School Singapore Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States

Dynamic treatment regimens (DTRs), also known as treatment algorithms or adaptive interventions, play an increasingly important role in many health domains. DTRs are motivated to address the unique and changing needs of individuals by delivering the type of treatment needed, when needed, while minimizing unnecessary treatment. Practically, a DTR is a sequence of decision rules that specify, for each of several points in time, how available information about the individual’s status and progress should be used in practice to decide which treatment (e.g., type or intensity) to deliver. The sequential multiple assignment randomized trial (SMART) is an experimental design widely used to empirically inform the development of DTRs. Sample size planning resources for SMARTs have been developed for continuous, binary, and survival outcomes. However, an important gap exists in sample size estimation methodology for SMARTs with longitudinal count outcomes. Further, in many health domains, count data are overdispersed – having variance greater than their mean. We propose a Monte Carlo-based approach to sample size estimation applicable to many types of longitudinal outcomes and provide a case study with longitudinal overdispersed count outcomes. A SMART for engaging alcohol and cocaine-dependent patients in treatment is used as motivation. © 2021, CC BY.

关键词： Patient treatment

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The Immune-Silenced Dialogue In Endometrial Cancer Tumor Microenvironment Escape: Could The Volume Be Tuned Up By The Fetal-Maternal Immune Tolerance Cross-Talk?

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International Journal of Gynecological Cancer 2025年第2期35卷

作者： Valentina Bruno Margherita Ferretti Martina Betti Stefano Scalera Simona Di Martino Ana Maria Arteni Brindusa Ludovica Ciuffreda Francesca De Nicola Alessandra Logoteta Emanuela Mancini Ermelinda Baiocco Cristina Vincenzoni Giuseppe Cutillo Livia Ronchetti Brunella Zizolfi Mariantonia Carosi Giulia Piaggio Maurizio Fanciulli Attilio Di Spiezio Sardo Enrico Vizza Dept. of Experimental Clinical Oncology Gynecologic Oncology Unit IRCCS- Regina Elena National Cancer Institute Rome Italy Dept. of Research Diagnosis and Innovative Technologies UOSD SAFU IRCCS- Regina Elena National Cancer Institute Rome Italy Biostatistics Bioinformatics and Clinical Trial Center IRCCS- Regina Elena National Cancer Institute Rome Italy Dept. of Research Diagnosis and Innovative Technologies Anatomy Pathology Unit IRCCS- Regina Elena National Cancer Institute Rome Italy Department of Biomedicine and Prevention Biotechnologies and Translational Medicine Tor Vergata University Rome Italy Department of Public Health University of Naples Federico II Naples Italy

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QUANTITATIVE METABOLIC PARAMETERS EVALUATION IN PATIENTS WITH AGGRESSIVE B-CELL LYMPHOMAS TREATED WITH ANTI-CD19 CAR T-CELLS

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Hematological Oncology 2021年第S2期39卷

作者： A. Guidetti A. Dodero G. Argiroffi P. Verderio A. Lorenzoni A. Chiappella C. Carniti E. Seregni s. Pizzamiglio A. Alessi P. Corradini Fondazione IRCCS Istituto Nazionale dei Tumori Hematology Milano Italy Fondazione IRCCS Istituto dei Tumori Nuclear Medicine Milano Italy Fondazione IRCCS Istituto Nazionale dei Tumori Unit of Bioinformatics and Biostatistics Milano Italy

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Comparing conventional statistical models and machine learning in a small cohort of South African cardiac patients

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Informatics in Medicine Unlocked 2022年 34卷

作者： Premsagar, Preesha Aldous, Colleen Esterhuizen, Tonya M. Gomes, Byron J. Gaskell, Jason William Tabb, David L. Department of Internal Medicine Nelson R Mandela School of Medicine University of Kwa-Zulu Natal South Africa Nelson R Mandela School of Medicine University of Kwa-Zulu Natal South Africa Division of Epidemiology/Biostatistics Department of Global Health Faculty of Medical and Health Sciences Stellenbosch University South Africa Financial Mathematics and BSc Actuarial Science and Statistics University of Witwatersrand South Africa B.Bus.Sci Actuarial Science Specialising in Actuarial Science University of Cape Town South Africa Institut Pasteur Université Paris Cité CNRS UAR 2024 Mass Spectrometry for Biology Unit 28 rue du Dr Roux Cedex 15 PARIS 75724 France Centre for Bioinformatics and Computational Biology Stellenbosch University Stellenbosch South Africa

Background: Machine learning is used to process big data volumes with complex non-linear relationships between predictive variables and predictions. Research into the usefulness of machine learning in small data volumes remains limited. Aim: To compare conventional statistical methods and machine learning to predict angiogram outcomes in a small cohort of South African cardiac patients. Methods: This is a retrospective study on patients with cardiac risk factors at Inkosi Albert Luthuli Central Hospital, Durban, South Africa, from 2002 to 2008. Models were designed using predictive risk factors to forecast a binary angiogram outcome (normal or abnormal) by applying conventional statistical models (binary logistic and log binomial) and stacking ensemble machine learning. Results: The outcome prevalence of abnormal angiograms was 99/173 (57%). Predictive data was used to model this outcome. The binary logistic regression model, which estimates odds ratio, was unsuitable. The log binomial model, which estimates relative risk, did not converge after various stepwise modelling attempts. Thereafter, machine learning models were used. These included logistic regression, k-nearest neighbour, decision tree, support vector machine, and naïve Bayes. The ensemble model amalgamated all algorithms and showed accuracy >70% and excellent performance at different thresholds with an area under the curve (AUC) > 80%. Discussion: The logistic regression model was unsuitable because an odds ratio would have been unreliable and overestimated the true effect since the outcome prevalence was >10%. A log binomial model with relative risk estimates did not converge, possibly owing to the multiple predictive variables. Overall, conventional statistical models were unsuccessful in this instance. Machine learning models had limitations from a small dataset. However, the combined modelling with the stacking ensemble method produced good results in the small, homogenous database by exploiting the

关键词： Angiography Ensemble Machine learning Prediction Statistics

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Machine Learning for Monoclonal Gammopathies of Renal Significance Risk Stratification Using Clinical and Pathology Data

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Kidney International Reports 2025年

作者： Cazzaniga, Giorgio Capitoli, Giulia Barretta, Raffaella Smith, Andrew Vischini, Gisella Papalia, Giuliana Alberici, Federico Mescia, Federica Eccher, Albino Becker, Jan Ulrich Naesens, Maarten Furian, Lucrezia Schröppel, Bernd Galimberti, Stefania Pagni, Fabio Department of Medicine and Surgery Pathology University of Milano-Bicocca IRCCS Fondazione San Gerardo dei Tintori Monza Italy Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center School of Medicine and Surgery University of Milan - Bicocca Monza Italy Biostatistics and Clinical Epidemiology Fondazione IRCCS San Gerardo dei Tintori Monza Italy Department of Medicine and Surgery Proteomics and Metabolomics Unit University of Milano-Bicocca Monza Italy Nephrology Dialysis and Renal Transplant Unit IRCCS Azienda Ospedaliero-Universitaria di Bologna Alma Mater Studiorum University of Bologna Italy Department of Medical and Surgical Science Alma Mater Studiorum - University of Bologna Bologna Italy Department of Pharmacy Health and Nutritional Sciences University of Calabria Rende Italy Nephrology Unit Uniersity of Brescia ASST Spedali Civili di Brescia Brescia Italy Department of Medical and Surgical Sciences for Children and Adults University of Modena and Reggio Emilia University Hospital of Modena Modena Italy Institute of Pathology University Hospital of Cologne Cologne Germany Department of Microbiology Immunology and Transplantation Nephrology and Kidney Transplantation Research Group KU Leuven Leuven Belgium Department of Nephrology and Kidney Transplantation University Hospitals Leuven Leuven Belgium Kidney and Pancreas Transplantation Unit Department of Surgical Oncological and Gastroenterological Sciences School of Medicine and Surgery University of Padua Padua Italy Section of Nephrology University Hospital Ulm Germany

Introduction: The prompt detection of monoclonal gammopathies of renal significance (MGRS) is clinically relevant for the initiation of chemotherapy. Although clinical and laboratory data can suggest the presence of MGRS, renal biopsy still represents the gold standard, despite not always being performed or eventually postponed because it is not deemed useful or informative. In this retrospective study, machine learning (ML) is used to build a tool to assist the prebiopsy risk stratification of MGRS and reinforce the rationale for the histological examination. Methods: The study included a total of 258 patients with monoclonal gammopathy of undetermined significance, of which 168 MGRS cases (65%) and 90 non-MGRS cases (35%) based on the final renal biopsy result. Results: Patients with MGRS were more frequently female (48.2% vs. 28.9%, P = 0.004) and had more frequent Bence Jones proteinuria (73.2% vs. 41.1%, P < 0.001) than non-MGRS cases, with amyloidosis being the most common diagnosis (62%). The ML model achieved an accuracy of 0.79 (95% confidence interval [CI]: 0.67–0.88) and an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.65–0.93) in the validation set. The model is available as a free desktop and Android mobile app (MGRS Interactive Resource for AI-Guided Evaluation, MIRAGE). Conclusion: The ML-based MGRS risk stratification tool can help in the selection of patients with higher probability to have MGRS on renal biopsy, aiming to direct those patients to a complete histological characterization of the disease. © 2025 International Society of Nephrology

关键词： computational pathology digital health digital pathology MGRS onco-nephrology renal biopsy

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Prevalence and Predictors of Inflammation in Pregnant Women: Multi-Country Analysis From BRINDA Project

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Current Developments in Nutrition 2022年 6卷 924-924页

作者： Luo, Hanqi Cole, Chelsea Jahan, Afrin Peerson, Janet Ko, Yi-An Addo, O. Yaw Suchdev, Parminder Young, Melissa Emory University Rollins School of Public Health Emory University Hubert Department of Global Health Hubert Department of Global Health Rollins School of Public Health Emory University University of California Davis Department of Biostatistics and Bioinformatics Rollins School of Public Health Emory University Centers for Disease Control and Prevention Nutrition Branch International Micronutrient Malnutrition Prevention and Control Program Unit Nutrition Branch International Micronutrient Malnutrition Prevention and Control Program Unit Centers for Disease Control and Prevention

Objectives:Limited data exist on the prevalence and predictors of inflammation during pregnancy. We aimed to characterize the inflammatory pattern and predictors of subclinical inflammation across pregnancy using multi-country analysis. Methods:The Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project compiled 17 datasets of pregnant women (n = 14,077) from 15 countries in both high- and low-income settings. Datasets were included if at least one inflammation biomarker (C-reactive protein, CRP, or α-1-acid glycoprotein, AGP) were collected. We estimated the prevalence of any subclinical inflammation (defined as CRP >5 mg/L or AGP >1 g/L), examined AGP and CRP patterns throughout pregnancy, and assessed the relationship between inflammation and covariates such as maternal age, gestational age, socioeconomic, and water and sanitation factors for each dataset. Results:The prevalence of inflammation varied from 16.6% in Afghanistan to < 1% in Vietnam using elevated AGP and from 52.9% in the US to 7.6% in Vietnam using elevated CRP. Inflammation was common but varied across datasets: >40% in 5 datasets, 20–40% in 6 datasets, 10–< 20% in 5 datasets and < 10% in one dataset. AGP decreased with increasing gestational age (P < 0.01 in all seven datasets with gestational age information); however, the magnitude of decrease in AGP varied by country. In contrast, CRP showed an inconsistent pattern by gestational age. In multivariable models, the predictors of inflammation included age, trimester, urban or rural residence, socioeconomic status, improved sanitation, improved drinking water, lactating and smoking status, although strengths of association differed by dataset. Conclusions:Although there was considerable heterogeneity in the prevalence of inflammation, inflammation was common across pregnancy in diverse settings. AGP, a measure of long-term inflammation, decreased across pregnancy in all countries, whereas the pattern for CRP was

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CO5.2 - Méthodes d'analyses transcriptomiques: fiabilité des rangs des gènes sélectionnés et performance diagnostique. Etude de simulation et application QUID-NASH

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Revue d'Épidémiologie et de Santé Publique 2023年 71卷

作者： P. Arnault de Guenyveau J. Lascano Maillard C. Laouénan F. Mehl F. Letourneur D. Valla J. Mullaert E. Gloaguen AP-HP - Hôpital Bichat DEBRC - Unité de recherche clinique Paris Nord Val de Seine Paris France Vital-IT Group SIB Swiss Institute for Bioinformatics Bioinformatics Lausanne Switzerland Université de Paris UMR1137 (IAME) UMR1137 (IAME) Inserm Paris France GenomIC Université de Paris Institut Cochin Inserm CNRS GenomIC Paris France Université Paris Cité Inserm Centre de recherche sur l'inflammation Paris France AP-HP Hôpital Beaujon Service d'hépatologie Clichy-la-Garenne France Institut Curie Inserm U900 Biostatistics for Personalized Medicine Saint-Cloud France

Introduction Les deux principaux objectifs des études transcriptomiques sont: l'identification des gènes différentiellement exprimés (DEG) entre deux groupes et le développement de mod...

Introduction Les deux principaux objectifs des études transcriptomiques sont: l'identification des gènes différentiellement exprimés (DEG) entre deux groupes et le développement de modèles prédictifs. Identifier les DEG dans le bon ordre peut être pertinent pour orienter les analyses fonctionnelles et pourrait permettre, en outre, de développer des modèles prédictifs plus performants. Plusieurs méthodes statistiques pour l'analyse de données transcriptomiques existent, conduisant à des résultats souvent différents. Nous proposons ici d’évaluer la fiabilité des classements des gènes sélectionnés et les performances prédictives obtenues par cinq méthodes largement utilisées: « Linear model for microarray data » (limma) suivi de la méthode de correction de Benjamini-Hochberg, « Significance Analysis of Microarrays » (SAM), « Least Absolute Schrinkage and Selection Operator » (LASSO, validation croisée classique et validation croisée imbriquée), « Sparse Partial Least Square Discriminant Analysis » (SPLSDA) et « Random Forest » (RF). Méthodes Pour les comparer, nous avons simulé des données pour lesquelles les rangs réels des gènes étaient connus. Neuf scénarii ont été envisagés avec différentes forces d'association, niveaux de bruit, tailles d'échantillon et pourcentages de DEG. Nous avons utilisé le Tau de Kendall pondéré pour mesurer la fiabilité des rangs estimés des DEG, et l'aire sous la courbe ROC (AUC) pour leur performance diagnostique. S'en est suivie une application aux données réelles de l'étude QUID-NASH (« Quantitative Imaging in Diabetes - Non-Alcoholic SteatoHepatitis »). Résultats En situation de faible niveau de bruit, les méthodes limma et SAM sont significativement plus performantes que les méthodes multivariées. Dans les cas de bruit élevé, SAM et LASSO (version validation croisée imbriquée) ont fourni des classements plus fiables. Comme attendu, la fiabilité des classements se détériore avec l'augmentation du niveau de bruit ou la diminution de la

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Braix Risk Score: An Automated Mammogram-Based Biomarker for Breast Cancer Created by Applying Artificial Intelligence

SSRN

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SSRN 2024年

作者： Hopper, John L. Nguyen, Tuong Linh Elliott, Michael S. Al-Qershi, Osamah Schmidt, Daniel F. Makalic, Enes Li, Shuai Fox, Samantha K. Dowty, James G. Peña-Solorzano, Carlos Andres Kwok, Chun Fung Chen, Yuanhong Wang, Chong Lippey, Jocelyn F. Brotchie, Peter Carneiro, Gustavo McCarthy, Davis J. Frazer, Helen M.L. Centre for Epidemiology & Biostatistics Melbourne School of Population and Global Health University of Melbourne Victoria Australia Bioinformatics and Cellular Genomics Unit St Vincent’s Institute of Medical Research Victoria Australia Melbourne Integrative Genomics School of Mathematics and Statistics School of BioSciences Faculty of Science University of Melbourne Victoria Australia Faculty of Information Technology Monash University ClaytonVIC Australia Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care University of Cambridge Cambridge United Kingdom Precision Medicine School of Clinical Sciences at Monash Health Monash University ClaytonVIC Australia Murdoch Children’s Research Institute Royal Children’s Hospital ParkvilleVIC Australia School of Computer Science Australian Institute for Machine Learning University of Adelaide SA Australia Department of Surgery St Vincent’s Hospital Melbourne VIC Australia Faculty of Medicine Dentistry and Health Sciences University of Melbourne Victoria Australia St Vincent’s BreastScreen St Vincent’s Hospital Melbourne VIC Australia Department of Radiology St Vincent’s Hospital Melbourne VIC Australia Centre for Vision Speech and Signal Processing University of Surrey United Kingdom BreastScreen Victoria VIC Australia

Background: Artificial intelligence (AI) algorithms to detect breast cancers from mammographic images are being developed. We evaluated the performance of an algorithm across all stages of the screening process and beyond. Methods: We used an image-specific AI-based breast cancer detection measure learnt from images of women attending BreastScreen Victoria (BRAIx cancer detection measure) to create a woman-specific standard normal mammography-based risk score (BRAIx risk score). Applying logistic regression to a random independent prospective testing cohort of 96,348 women, we estimated risk of invasive breast cancers: (i) detected at cohort entry (525);and (ii) for women given an all-clear, diagnosed during the next 4 years either at future screens (790) or during intervals between screens (308), as a function of the BRAIx risk score and other measures. We estimated the odds ratio per standard deviation (OR) and a greater risk in an upper ***: The BRAIx risk score predicted reader outcomes, recall, cancer detection and future risk. For 4-years risk, OR=2.70 (95% confidence interval (CI) 2.52 to 2.88) overall with evidence for a greater OR in the upper tail (P=10-18). The predicted absolute risk was >10% for 1% of all-clear women. BRAIx risk score was greater for women with a family history and explained 23% (95% CI 21-25%) of familial aspects of risk. Mammographic density was weakly associated with risk before (OR=1.27;95% CI 1.20 to 1.35), but not after, fitting ***: The BRAIx risk score is a strong biomarker for breast cancer that could be used to inform risk-based recall for further assessment, detection and future screening. Its use would automatically identify all-clear women at similar risk to BRCA1 and BRCA2 mutation carriers. BRAIx could also reveal insights into both familial and non-familial causes. Funding: Medical Research Future Fund, the Ramaciotti Foundation, National Breast Cancer Foundation, Cancer *** o

关键词： Mammography

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Correction: High arterial oxygen levels and supplemental oxygen administration in traumatic brain injury: insights from CENTER-TBI and OzENTER-TBI

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Intensive care medicine 2023年第2期49卷 269-272页

作者： Emanuele Rezoagli Matteo Petrosino Paola Rebora David K Menon Stefania Mondello D James Cooper Andrew I R Maas Eveline J A Wiegers Stefania Galimberti Giuseppe Citerio School of Medicine and Surgery University of Milano - Bicocca Monza Italy. Department of Emergency and Intensive Care San Gerardo University Hospital Extracorporeal Membrane Oxygenation (ECMO) Center Fondazione IRCCS San Gerardo dei Tintori Monza Italy. Department of Medicine and Surgery Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center University of Milano - Bicocca Monza Italy. Division of Anaesthesia Addenbrooke's Hospital University of Cambridge Hills Road Box 93 Cambridge CB2 0QQ UK. Department of Biomedical and Dental Sciences and Morphofunctional Imaging University of Messina Messina Italy. Intensive Care Department Alfred Hospital Melbourne Australia. School of Public Health and Preventive Medicine Monash University Melbourne Australia. Antwerp University Hospital and University of Antwerp Edegem Belgium. Department of Public Health Erasmus MC University Medical Center Rotterdam The Netherlands. School of Medicine and Surgery University of Milano - Bicocca Monza Italy. giuseppe.citerio@unimib.it. NeuroIntensive Care Unit Neuroscience Department Fondazione IRCCS San Gerardo dei Tintori Monza Italy. giuseppe.citerio@unimib.it.

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Genome instability drives epistatic adaptation in the human pathogen Leishmania

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Proceedings of the National Academy of Sciences of the united States of America 2021年第51期118卷 e2113744118-e2113744118页

作者： Bussotti, Giovanni Piel, Laura Pescher, Pascale Domagalska, Malgorzata A. Shanmugha Rajan, K. Cohen-Chalamish, Smadar Doniger, Tirza Hiregange, Disha-Gajanan Myler, Peter J. Unger, Ron Michaeli, Shulamit Spath, Gerald F. Bioinformatics and Biostatistics Hub C3BI Institut Pasteur Universite de Paris Unite de Service et de Recherche (USR) 3756 Institut Pasteur CNRS Paris 75015 France Unite de Parasitologie moleculaire et Signalisation Institut Pasteur Universite de Paris INSERM U1201 Paris 75015 France Molecular Parasitology Unit Institute of Tropical Medicine B-2000 Antwerp Belgium The Mina and Everard Goodman Faculty of Life Sciences Advanced Materials and Nanotechnology Institute Bar-Ilan University Ramat-Gan 52900 Israel Department of Chemical and Structural Biology Weizmann Institute of Science Rehovot 7610001 Israel Seattle Structural Genomics Center for Infectious Disease Seattle 98195 WA United States Department of Biomedical Informatics and Medical Education University of Washington Seattle 98195 WA United States Department of Pediatrics University of Washington Seattle 98195 WA United States Department of Global Health University of Washington Seattle 98195 WA United States Center for Global Infectious Disease Research Seattle Children's Research Institute Seattle 98195 WA United States

How genome instability is harnessed for fitness gain despite its potential deleterious effects is largely elusive. An ideal system to address this important open question is provided by the protozoan pathogen Leishmania, which exploits frequent variations in chromosome and gene copy number to regulate expression levels. Using ecological genomics and experimental evolution approaches, we provide evidence that Leishmania adaptation relies on epistatic interactions between functionally associated gene copy number variations in pathways driving fitness gain in a given environment. We further uncover posttranscriptional regulation as a key mechanism that compensates for deleterious gene dosage effects and provides phenotypic robustness to genetically heterogenous parasite populations. Finally, we correlate dynamic variations in small nucleolar RNA (snoRNA) gene dosage with changes in ribosomal RNA 2⁰-O-methylation and pseudouridylation, suggesting translational control as an additional layer of parasite adaptation. Leishmania genome instability is thus harnessed for fitness gain by genome-dependent variations in gene expression and genome-independent compensatory mechanisms. This allows for polyclonal adaptation and maintenance of genetic heterogeneity despite strong selective pressure. The epistatic adaptation described here needs to be considered in Leishmania epidemiology and biomarker discovery and may be relevant to other fast-evolving eukaryotic cells that exploit genome instability for adaptation, such as fungal pathogens or cancer. © 2021 National Academy of Sciences. All rights reserved.

关键词： Epistatic interactions Evolutionary adaptation Fitness gain Genome instability Posttranscriptional regulation

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