دورية أكاديمية
أعرض في EDS

Calibration plots for multistate risk predictions models.

التفاصيل البيبلوغرافية
العنوان: Calibration plots for multistate risk predictions models.
المؤلفون: Pate A; Centre for Health Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK., Sperrin M; Centre for Health Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.; NIHR Manchester Biomedical Research Centre, University of Manchester, Manchester, UK., Riley RD; Institute of Applied Health Research, University of Birmingham, Birmingham, UK., Peek N; Centre for Health Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.; NIHR Manchester Biomedical Research Centre, University of Manchester, Manchester, UK., Van Staa T; Centre for Health Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK., Sergeant JC; Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.; Centre for Biostatistics, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK., Mamas MA; Keele Cardiovascular Research Group, Keele University, Stoke-on-Trent, UK., Lip GYH; Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark., O'Flaherty M; NIHR Applied Research Collaboration NW Coast, University of Liverpool, Liverpool, UK.; Independent Researcher, Manchester, UK., Barrowman M; NIHR Applied Research Collaboration NW Coast, University of Liverpool, Liverpool, UK., Buchan I; Independent Researcher, Manchester, UK.; Institute of Population Health, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK., Martin GP; Centre for Health Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.
المصدر: Statistics in medicine [Stat Med] 2024 Jun 30; Vol. 43 (14), pp. 2830-2852. Date of Electronic Publication: 2024 May 08.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Chichester ; New York : Wiley, c1982-
مواضيع طبية MeSH: Diabetes Mellitus, Type 2*/epidemiology , Models, Statistical* , Computer Simulation*, Humans ; Risk Assessment/methods ; Risk Assessment/statistics & numerical data ; Logistic Models ; Calibration ; Cardiovascular Diseases/epidemiology ; Renal Insufficiency, Chronic/epidemiology ; Probability
مستخلص: Introduction: There is currently no guidance on how to assess the calibration of multistate models used for risk prediction. We introduce several techniques that can be used to produce calibration plots for the transition probabilities of a multistate model, before assessing their performance in the presence of random and independent censoring through a simulation.
Methods: We studied pseudo-values based on the Aalen-Johansen estimator, binary logistic regression with inverse probability of censoring weights (BLR-IPCW), and multinomial logistic regression with inverse probability of censoring weights (MLR-IPCW). The MLR-IPCW approach results in a calibration scatter plot, providing extra insight about the calibration. We simulated data with varying levels of censoring and evaluated the ability of each method to estimate the calibration curve for a set of predicted transition probabilities. We also developed evaluated the calibration of a model predicting the incidence of cardiovascular disease, type 2 diabetes and chronic kidney disease among a cohort of patients derived from linked primary and secondary healthcare records.
Results: The pseudo-value, BLR-IPCW, and MLR-IPCW approaches give unbiased estimates of the calibration curves under random censoring. These methods remained predominately unbiased in the presence of independent censoring, even if the censoring mechanism was strongly associated with the outcome, with bias concentrated in low-density regions of predicted transition probability.
Conclusions: We recommend implementing either the pseudo-value or BLR-IPCW approaches to produce a calibration curve, combined with the MLR-IPCW approach to produce a calibration scatter plot. The methods have been incorporated into the "calibmsm" R package available on CRAN.
(© 2024 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)
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معلومات مُعتمدة: MR/T025085/1 United Kingdom MRC_ Medical Research Council
فهرسة مساهمة: Keywords: calibration; clinical prediction; model validation; multistate model; risk prediction
تواريخ الأحداث: Date Created: 20240509 Date Completed: 20240614 Latest Revision: 20240614
رمز التحديث: 20240614
DOI: 10.1002/sim.10094
PMID: 38720592
قاعدة البيانات: MEDLINE
الوصف
تدمد:1097-0258
DOI:10.1002/sim.10094