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Clinical Prediction Models
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TLDR
This paper presents a case study on survival analysis: Prediction of secondary cardiovascular events and lessons from case studies on overfitting and optimism in prediction models.Abstract:
Introduction.- Applications of prediction models.- Study design for prediction models.- Statistical models for prediction.- Overfitting and optimism in prediction models.- Choosing between alternative statistical models.- Dealing with missing values.- Case study on dealing with missing values.- Coding of categorical and continuous predictors.- Restrictions on candidate predictors.- Selection of main effects.- Assumptions in regression models: Additivity and linearity.- Modern estimation methods.- Estimation with external methods.- Evaluation of performance.- Clinical usefulness.- Validation of prediction models.- Presentation formats.- Patterns of external validity.- Updating for a new setting.- Updating for a multiple settings.- Prediction of a binary outcome: 30-day mortality after acute myocardial infarction.- Case study on survival analysis: Prediction of secondary cardiovascular events.- Lessons from case studies.read more
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Journal ArticleDOI
Clinical Prediction Models for Cardiovascular Disease Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database
Benjamin S. Wessler,Lana Lai Yh,Whitney Kramer,Michael Cangelosi,Gowri Raman,Jennifer S. Lutz,David M. Kent +6 more
TL;DR: Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care as mentioned in this paper for patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described.
Journal ArticleDOI
Automated differentiation of pre-diagnosis Huntington's disease from healthy control individuals based on quadratic discriminant analysis of the basal ganglia: the IMAGE-HD study
Nellie Georgiou-Karistianis,Marcus A. Gray,Alicia Rhian Dymowski,India Bohanna,Leigh A. Johnston,Leigh A. Johnston,Andrew Churchyard,Phyllis Chua,Julie C. Stout,Gary F. Egan +9 more
TL;DR: Evidence is provided that combined multi-modal imaging measures can accurately classify individuals up to 15 years prior to onset when therapeutic intervention is likely to have maximal effects in slowing the trajectory of disease development.
Journal ArticleDOI
Individual participant data meta-analysis of prognostic factor studies: state of the art?
TL;DR: Meta-analyses of prognostic factors studies using individual participant data are achievable and offer many advantages, as displayed most expertly by the IMPACT initiative, however such projects face numerous logistical and methodological obstacles, and their conduct and reporting can often be substantially improved.
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Regression trees for predicting mortality in patients with cardiovascular disease: What improvement is achieved by using ensemble-based methods?
Peter C. Austin,Peter C. Austin,Douglas S. Lee,Douglas S. Lee,Douglas S. Lee,Ewout W. Steyerberg,Jack V. Tu,Jack V. Tu +7 more
TL;DR: It is concluded that ensemble methods from the data mining and machine learning literature increase the predictive performance of regression trees, but may not lead to clear advantages over conventional logistic regression models for predicting short‐term mortality in population‐based samples of subjects with cardiovascular disease.
Journal ArticleDOI
Predictors for outcome of failure of balloon dilatation in patients with achalasia
TL;DR: Young age at presentation, classic achalasia, high LOS pressure 3 months after PD and incomplete obliteration of the balloon's waist during PD are the most important predicting factors for the need for repeated treatment during follow-up.