In particular cross-validation and bootstrapping are covered to estimate the optimism and shrink the model coefficients accordingly related approaches such as LASSO and elastic net are also discussed. Internal validation strategies are outlined to identify and adjust for overfitting. Model development topics are then covered, including: identifying candidate predictors, handling of missing data, modelling continuous predictors using fractional polynomials or restricted cubic splines for non-linear functions, and variable selection procedures.ĭay 2 focuses on how models are overfitted for the data in which they were derived, and thus often do not generalise to other datasets. It then outlines model specification, focusing on logistic regression for binary outcomes and Cox regression or flexible parametric survival models for time to event outcomes. A background in statistics, epidemiology or data science would also be advantageous.ĭay 1 begins with an overview of the rationale and phases of prediction model research. We recommend participants have a good understanding of key statistical principles and measures (such as effect estimates, confidence intervals and p-values) and the ability to apply and interpret regression models is essential. The course is aimed at individuals that want to learn how to develop and validate risk prediction and prognostic models, specifically for binary or time-to-event clinical outcomes (though continuous outcomes is also covered). All code is already written and so participants can focus more on their understanding of methods and interpretation of results. Our focus is on multivariable models for individualised prediction of future outcomes (prognosis), although many of the concepts described also apply to models for predicting existing disease (diagnosis).Ĭomputer practicals in either R or Stata are included on all three days (two per day), and participants can choose whether to focus on logistic regression examples (for binary outcomes) or Cox / flexible parametric survival examples (for time-to-event outcomes), to tailor the practicals to their own purpose. The course is delivered over 3 days and focuses on model development (day 1), internal validation (day 2), and external validation and novel topics (day 3). ![]() This online course provides a thorough foundation of statistical methods for developing and validating prognostic models in clinical research. 1980 Jun 78(6):1632–1635.Statistical Methods for Risk Prediction and Prognostic Models (Birmingham, online, 21-23 March 2023, FULLY BOOKED ) Prognosis Research in Healthcare Summer School (Keele, online, 12-14 July 2023, BOOKING OPEN) Statistical Methods for Meta-Analysis of Individual Participant Data (Birmingham, online, Autumn 2023 ) Systematic Reviews of Prognosis Studies (Utrecht) Systematic Reviews of Prognosis Studies - free 60 mins interactive module (Cochrane Prognosis Methods Group) Clinical Prediction Models (Maastricht) Regression Modelling Strategies (online) Statistical Methods for Risk Prediction and Prognostic Models University of Birmingham (online course, 13th to 15th June 2023 - BOOK HERE) Cancer risk in ulcerative colitis: scientific requirements for the study of prognosis. Laupacis A, Wells G, Richardson WS, Tugwell P.Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients. Lemeshow S, Teres D, Klar J, Avrunin JS, Gehlbach SH, Rapoport J.A method for predicting survival and mortality of ICU patients using objectively derived weights. Lemeshow S, Teres D, Pastides H, Avrunin JS, Steingrub JS.A comparison of results of meta-analyses of randomized control trials and recommendations of clinical experts. ![]() Antman EM, Lau J, Kupelnick B, Mosteller F, Chalmers TC.
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