250 research outputs found
Postmus/MDT-SML: Initial release
No full textR code for reproducing the results of the computational experiments presented in the paper "A Simulated Maximum Likelihood Procedure for Analysing Imprecise Trade-off Thresholds between the Benefits and Harms of Medicines"
Supplementary_Fig-S1.rjf_online_supp – Supplemental material for From Individual to Population Preferences: Comparison of Discrete Choice and Dirichlet Models for Treatment Benefit-Risk Tradeoffs
No full textSupplemental material, Supplementary_Fig-S1.rjf_online_supp for From Individual to Population Preferences: Comparison of Discrete Choice and Dirichlet Models for Treatment Benefit-Risk Tradeoffs by Tommi Tervonen, Francesco Pignatti and Douwe Postmus in Medical Decision Making</p
Supplementary_Fig-S2.rjf_online_supp – Supplemental material for From Individual to Population Preferences: Comparison of Discrete Choice and Dirichlet Models for Treatment Benefit-Risk Tradeoffs
No full textSupplemental material, Supplementary_Fig-S2.rjf_online_supp for From Individual to Population Preferences: Comparison of Discrete Choice and Dirichlet Models for Treatment Benefit-Risk Tradeoffs by Tommi Tervonen, Francesco Pignatti and Douwe Postmus in Medical Decision Making</p
Supplementary_Fig-S3.rjf_online_supp – Supplemental material for From Individual to Population Preferences: Comparison of Discrete Choice and Dirichlet Models for Treatment Benefit-Risk Tradeoffs
No full textSupplemental material, Supplementary_Fig-S3.rjf_online_supp for From Individual to Population Preferences: Comparison of Discrete Choice and Dirichlet Models for Treatment Benefit-Risk Tradeoffs by Tommi Tervonen, Francesco Pignatti and Douwe Postmus in Medical Decision Making</p
tommite/pub-dirichlet-mnl: Code for "From individual to population preferences: Comparison of discrete choice and Dirichlet models for treatment benefit-risk trade-offs"
No full text<p>Code for T. Tervonen, F. Pignatti, D. Postmus: "From individual to population preferences: Comparison of discrete choice and Dirichlet models for treatment benefit-risk trade-offs", published in Medical Decision Making, 2019.</p>
Contemporary issues in static and dynamic prediction:some applications and evaluation in the clinical context
Full text linkPrediction models that estimate the probabilities of developing a specific disease (diagnostic model) or a specific endpoint of disease (prognostic model) given a set of subject’s characteristics are closely connected to personalized medicine of which the key idea is to base medical decisions on individual patient characteristics rather than on population averages. Depending on decision point, prediction models can be divided into two categories: static prediction models (making one-off decision) and dynamic prediction models (making dynamically updated decisions). While multivariable logistic and Cox regression are commonly used to develop prediction models, they are not the master key to every situation. Various issues such as clustered data, competing risks and time-dependent variable may occur when a simple logistic or Cox model cannot estimate the risk correctly in static and dynamic prediction. Although adapted or more advanced approaches have been developed to address those issues in medical statistics field, they are not appropriately applied in clinical research. To fill this gap, this thesis illustrated how sophisticated statistical models can be appropriately applied to obtain better predictions using a series of clinical case studies
Why life speeds up as you get older how memory shapes our past
No full text"In this book, Douwe Draaisma, author of the internationally acclaimed Metaphors of Memory, explores the nature of autobiographical memory. Applying a unique blend of scholarship, poetic sensibility and keen observation he tackles such extraordinary phenomena as deja vu, near-death experiences, the memory feats of idiots savants and the effects of extreme trauma on memory recall. Raising almost as many questions as it answers, this book will not fail to touch you at the same time as it educates and entertains."--BOOK JACKET
Optimizing clinical risk stratification in acute heart failure
Full text linkHeart failure occurs when the heart is unable to pump sufficiently to maintain blood flow to meet the body's needs. Acute heart failure is defined as a rapid onset of signs and symptoms of heart failure resulting in the need for urgent medical treatment. Acute heart failure is associated with survival poorer than many forms of cancer and is an enormous burden to health care systems mainly related to the high rate of readmissions. Identification of patients who do well after hospitalization and those who have high risk for death or hospital readmission is paramount to personalize treatment and intensity of post-discharge monitoring. However, contemporary instruments used to stratify patients into different risk categories are inadequate and there remains an unmet need for improved risk stratification tools. In this thesis, Dr. Demissei has provided important tools to improve the prediction of patients that have high risk of poor clinical outcome. His thesis showed that multiple markers, instead of using single markers, improved the accuracy of prediction of major future problems. His research also showed that more accurate risk stratification instruments could help to improve the identification of subgroups of patients who do/do not benefit from a specific drug treatment. This is a first step towards personalized medicine, where only patients that benefit will receive the drug. Future research is needed to study whether risk-guided treatment and monitoring strategies improve survival and reduce the huge burden of hospital readmissions in acute heart failure patients
Towards optimal decision making in personalized medicine: Potential value assessment of biomarkers in heart failure exemplars
Full text linkTreatment selection based on average effects observed in entire target population masks variation among patients (heterogeneity) and may result in less than optimal decision making. Personalized medicine is a new and complex concept, which aims to improve health by offering more tailored and individualized care to patients. Until recently, it was still rather difficult to foresee a broad clinical application of the medical technologies developed to enable personalized medicine. This is mainly because a well-developed methodological framework was still lacking to support the translation of such technologies from bench via bedside and ultimately to society. To this end, this thesis introduced a coherent set of methodologies to investigate the added value for personalized medicine, towards optimal therapeutic and policy decision making. The thesis focused on assessing the clinical impact and commercial potential of biomarker-based strategies using several case studies in heart failure
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