The Michael J. Fox Foundation for Parkinson's Research’s Post

Jennie Z. Ma, Ph.D., Professor in Public Health Sciences, and Indika Mallawaarachchi, PHS staff biostatistician, collaborated with Julia Scialla, MD, Associate Professor in the Department of Medicine, Nephrology, on a new manuscript on causal Inference for chronic kidney disease (CKD)-associated physiologic factors. The manuscript was recently accepted for publication by the American Journal of Epidemiology, with Dr. Ma serving as the senior author. This project was supported by a Chronic Renal Insufficiency Cohort (CRIC) Opportunity Pool award to Drs. Scialla and Ma. The UVa team (Drs. Scialla and Ma and Indika Mallawaarachchi) led the project, and used a flexible, nonparametric machine learning method to integrate multiple static and dynamic continuous biochemical predictors within a causal inference framework. This approach enabled them to more effectively delineate the associations between CKD-associated physiologic factors and risk of kidney failure. Here is the full citation for the accepted manuscript: Scialla JJ, Mallawaarachchi I, Illenberger N, Brookhart A, Isakova T, Mitra N, Ma JZ. Generalized Propensity Score Methods to Assess CKD-Associated Physiologic Factors and Risk of Kidney Failure in the Chronic Renal Insufficiency Cohort (CRIC) Study. Am J Epidemiol 2024, In Press. University of Virginia #uva #ckd #newpaper #publichealth

Novel Nonlinear Epidemiological Model with Applications in Public Health I'm thrilled to announce the publication of our new preprint, titled "Mathematical Modeling and Hyers-Ulam Stability for a Nonlinear Epidemiological Model with Φᵖ Operator and Mittag-Leffler Kernel," available here: https://lnkd.in/d9cNc_8m This joint work with Achraf ZINIHI and Moulay Rchid Sidi Ammi delves into a novel nonlinear singular fractional SI model, incorporating the Φᵖ operator and the Mittag-Leffler kernel. It holds significant potential for improving our understanding of disease dynamics and informing public health interventions. Key highlights of the paper: - Novel Model Formulation: We propose a new mathematical framework for modeling infectious diseases using the fractional calculus framework. It will be extended in the future by adding more compartments and terms accounting for uncertainty, time-delay, waning effects, etc. - Rigorous Analysis: We establish the existence, uniqueness, boundedness, and non-negativity of the model's solutions. - Hyers-Ulam Stability: We ensure the model's robustness to small perturbations using the powerful Hyers-Ulam stability theorem. - Optimal Control Analysis: We design optimal control strategies to minimize infection spread and maximize the susceptible population, aiding public health decision-making. - Numerical Validation: We showcase the model's practical applicability through comprehensive numerical simulations. I believe this research has significant implications for various fields, including: - Public health officials aiming to develop effective disease control strategies. - Mathematicians interested in applying fractional calculus to real-world problems. - Researchers seeking to advance the understanding of nonlinear epidemiological models. I encourage you to read the full preprint and join the discussion! I'm eager to hear your thoughts and potential applications of this novel modeling approach. #mathematics #analysis #publichealth #epidemiology #optimalcontrol #mathematicalmodeling #fractionalcalculus #research

How should we evaluate #LLMs for downstream clinical use cases? In this npj Digital Medicine article (with Nikita M., Brenda M. Eduardo Rodriguez Almaraz Madhumita Sushil, PhD and Atul Butte), we propose a transition from static QA-style benchmarks to dynamic agent-based simulators, similar to methods used in biology, epidemiology and social sciences. https://lnkd.in/gihJ2BtU

In the ever-evolving landscape of health, including public health, medicine, and social and behavioral science, the credibility of research findings is paramount. Validity, the cornerstone of scientific inquiry, ensures that our conclusions accurately reflect reality and can be relied upon to guide policy and practice. Join the IHSI Biostatistics, Epidemiology, and Research Design (BERD) team for "Validity Matters: Enhancing the Credibility of Health Research" on April 25 at noon. In this webinar, Jesus Sarol Jr., Ph.D., IHSI Senior Research Biostatistician, will delve into the intricacies of validity assessment from the perspective of epidemiologic research. He will discuss validity in different aspects or stages of research study, such as subject selection, measurement, analysis, and interpretation, and describe some common pitfalls and their consequences. Don't miss this opportunity to deepen your understanding of validity, elevate the quality of your research, and embark on a journey toward more robust and reliable scientific inquiry. Register for Zoom details: https://lnkd.in/g9WJ-e6T

🌟 Introducing the Rare Disease Data Center (RDDC) We are excited to unveil the Rare Disease Data Center (RDDC) – an invaluable resource for researchers, medical professionals, and families impacted by rare diseases. Check out here: https://lnkd.in/gF7ZFbe7 📚 𝐄𝐦𝐩𝐨𝐰𝐞𝐫𝐢𝐧𝐠 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 Backed by the Research Institute of Tsinghua, Pearl River Delta (RITPRD) and Cyagen, RDDC integrates global data on rare disease epidemiology, genetics, drugs, and animal models to propel research and understanding forward. 💡 𝐂𝐮𝐭𝐭𝐢𝐧𝐠-𝐄𝐝𝐠𝐞 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 Harnessing AI and bioinformatics, RDDC offers tools like the Pathogenicity Predictor and RNA Splicer for precise analysis of genetic mutations and splice defects, paving the way for novel treatments. 🌍 𝐆𝐥𝐨𝐛𝐚𝐥 𝐈𝐦𝐩𝐚𝐜𝐭 RDDC simplifies the learning curve of rare diseases, making accurate and up-to-date information accessible and visually understandable for all. 🔬 𝐔𝐧𝐢𝐟𝐢𝐞𝐝 𝐄𝐟𝐟𝐨𝐫𝐭𝐬 𝐟𝐨𝐫 𝐚 𝐁𝐞𝐭𝐭𝐞𝐫 𝐓𝐨𝐦𝐨𝐫𝐫𝐨𝐰 Our mission is to enhance the research process, focusing on finding cures and improving lives. Each discovery brings us closer to unveiling the mysteries of rare diseases. 🎗️ 𝐀 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 𝐨𝐟 𝐇𝐨𝐩𝐞 At RDDC, we believe in the strength of a united community. We stand alongside families, offering hope through the tireless efforts of researchers and the power of shared knowledge. Join us on this journey to decode life’s mysteries and fight against rare diseases. Together, we can transform challenges into triumphs and illuminate the path to a brighter, healthier future for all.

👏 Welcome to submit ! 🤩 A wonderful special issue: The Urgent Need for New Hypotheses to Develop Effective Therapeutic Tools Against Alzheimer's Disease 👨🔬 Guest Editor: Prof. Rafael Franco 🔗 Details at https: https://lnkd.in/gG2DGQth The special issue is open both to novel hypotheses, based on scientific criteria, and to studies in patients and controls, regardless of whether they are carried out in a small number of individuals. Welcome to submit your manuscripts to this fascinating special issue! There's no APC throughout the submission and publishing process. Hope it could be a great opportunity for you to share your excellent research outcomes in an open-access platform without any fee. Manuscripts can be submitted via the online system at https://lnkd.in/gY-JfRr. Looking forward to your submission! #AlzheimersDisease #Deuroprotection #PublicHealth #EpidemiologicalStudies #NeuronalDeath #DeuronalSurvival #SynapticPlasticity

👏 Welcome to submit ! 🤩 A wonderful special issue: The Urgent Need for New Hypotheses to Develop Effective Therapeutic Tools Against Alzheimer's Disease 👨🔬 Guest Editor: Prof. Rafael Franco 🔗 Details at https: https://lnkd.in/gG2DGQth The special issue is open both to novel hypotheses, based on scientific criteria, and to studies in patients and controls, regardless of whether they are carried out in a small number of individuals. Welcome to submit your manuscripts to this fascinating special issue! There's no APC throughout the submission and publishing process. Hope it could be a great opportunity for you to share your excellent research outcomes in an open-access platform without any fee. Manuscripts can be submitted via the online system at https://lnkd.in/gY-JfRr. Looking forward to your submission! #AlzheimersDisease #Deuroprotection #PublicHealth #EpidemiologicalStudies #NeuronalDeath #DeuronalSurvival #SynapticPlasticity

Thank you, Paul Arora for sharing insight into this interesting workshop at #ISPOR next week! ⭐ Target Trial Emulation in Practice: An Introduction and Exploration of Application Through Case Studies ⭐ Are we effectively bridging the gap between observational studies and #randomized controlled #trials? How can we enhance the accuracy and applicability of #healthcare interventions across diverse #patientpopulations? https://lnkd.in/gpdpNWb2 Scheduled for Tuesday, May 7, this session promises to be a deep dive into the innovative approach of Target Trial Emulation (#TTE), which is becoming pivotal for healthcare economics and outcomes research (#HEOR). TTE represents a significant improvement in the methodological approach to establishing treatment effectiveness from observational data, addressing the real limitations of traditional #realworldevidence studies. The discussion will be led by Alind Gupta from the University of Toronto, along with discussants Winson Cheung from the University of Calgary, and Manuel Gomes from UCL. The workshop looks to cover essential topics such as benchmarking studies, per-protocol estimation, leveraging real-world external comparators, and addressing data limitations like unmeasured confounders and #missingdata. https://lnkd.in/dZww9KHS ISPOR—The Professional Society for Health Economics and Outcomes Research #rwe #realworldevidence #realworlddata #dataanalytics #HealthcareResearch #Epidemiology #PublicHealth #outcomesresearch #patientoutcomes #healtheconomics #marketaccess #TargetTrialEmulation #DataScience #ISPOR #HealthPolicy #patientcare #patientcentricity #ISPORAnnual #ISPORUS #HTA #causalinference

📽 I just added 10 recorded workshop sessions from the The National Academies of Sciences, Engineering, and Medicine's recent two day workshop on pathogen genomics in public health to the recorded trainings table available at https://lnkd.in/gd5P4E7G 🕵♀️ There are now 140 recorded webinars, trainings, and workshops available covering a wide variety of topics such as bioinformatics, genomic epidemiology, data infrastructure, public health applications of genomics, wastewater, metagenomics, and ethics. 🆘 Do you know of trainings, webinars, etc. that would be relevant to people working in genomic epidemiology or interested in the field? Please submit trainings here so I can get them added to the list: https://lnkd.in/gH5zUdvD 💡 Please share with your networks to both help direct people to available training resources as well as help me identify other resources that should be included in the tables!

📊 Assessing Normality Assumption in Clinical Research: Skewness, Kurtosis, and More 📏 📝 Understanding normality in clinical research data is crucial. This post provides insights into assessing normal distribution using skewness and kurtosis. 💡 Key Points: ▶ Eyeball Test: Useful for larger samples but unreliable for small samples. ▶ Formal Normality Tests: Suitable for small to medium-sized samples, but may be unreliable for large samples. ▶ Skewness and Kurtosis: Skewness measures asymmetry, while kurtosis measures 'peakedness' of a distribution. ▶ Normality Test using Skewness and Kurtosis: Z-tests can be applied, with critical values varying based on sample size: 🔸 For small samples (n < 50), reject the null hypothesis (i.e. Normality) for absolute z-scores > 1.96. 🔸 For medium-sized samples (50 < n < 300), reject for absolute z-value > 3.29. 🔸 For samples > 300, rely on histograms and absolute values of skewness and kurtosis. ▶ Critical values for normality testing vary based on sample size, ensuring appropriate interpretation. ▶ Assumption of Normality: Although some tests are robust to moderate departures from normality, it's essential to assess normality through various means including visual inspection and formal tests. ▶ Transformation of data or nonparametric methods can be considered if substantial departure from normality is observed. ▶ For practical purposes, many statistical packages like SPSS compute the 'excess' kurtosis by subtracting 3 from the proper kurtosis value: 🔸 Ideally, the excess kurtosis of a perfectly normal distribution should be zero. ✳ Learn how to ensure your data meets the normality assumption required for robust analysis. #Normality #Normaldistribution #Skewness #Kurtosis #Assumptions #Dataanalysis #Data #Dataanalytics #Distribution #Samplesize #Clinicalresearch #Medicalresearch #Statisticalanalysis #Test #Statisticaltests #Biostatistics #Epidemiology