Dynamic predictions with time‐dependent covariates in survival analysis using joint modeling and landmarking

A key question in clinical practice is accurate prediction of patient prognosis. To this end, nowadays, physicians have at their disposal a variety of tests and biomarkers to aid them in optimizing medical care. These tests are often performed on a regular basis in order to closely follow the progre...

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Published in:Biometrical journal Vol. 59; no. 6; pp. 1261 - 1276
Main Authors: Rizopoulos, Dimitris, Molenberghs, Geert, Lesaffre, Emmanuel M.E.H.
Format: Journal Article
Language:English
Published: Germany Wiley - VCH Verlag GmbH & Co. KGaA 01.11.2017
Subjects:
Aortic Valve - surgery
Biomarkers
Biometry - methods
Calibration
Decision making
Discrimination
Economic models
Female
Heuristics
Humans
Information processing
Male
Mathematical models
Medical prognosis
Models, Statistical
Optimization
Physicians
Probability
Prognostic modeling
Random effects
Reoperation
Risk prediction
Statistical analysis
Survival
Survival Analysis
Time Factors
Random effects
Risk prediction
Discrimination
Calibration
Prognostic modeling
ISSN:0323-3847, 1521-4036, 1521-4036
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Abstract A key question in clinical practice is accurate prediction of patient prognosis. To this end, nowadays, physicians have at their disposal a variety of tests and biomarkers to aid them in optimizing medical care. These tests are often performed on a regular basis in order to closely follow the progression of the disease. In this setting, it is of interest to optimally utilize the recorded information and provide medically relevant summary measures, such as survival probabilities, which will aid in decision making. In this work, we present and compare two statistical techniques that provide dynamically updated estimates of survival probabilities, namely landmark analysis and joint models for longitudinal and time‐to‐event data. Special attention is given to the functional form linking the longitudinal and event time processes, and to measures of discrimination and calibration in the context of dynamic prediction.
AbstractList A key question in clinical practice is accurate prediction of patient prognosis. To this end, nowadays, physicians have at their disposal a variety of tests and biomarkers to aid them in optimizing medical care. These tests are often performed on a regular basis in order to closely follow the progression of the disease. In this setting, it is of interest to optimally utilize the recorded information and provide medically relevant summary measures, such as survival probabilities, which will aid in decision making. In this work, we present and compare two statistical techniques that provide dynamically updated estimates of survival probabilities, namely landmark analysis and joint models for longitudinal and time‐to‐event data. Special attention is given to the functional form linking the longitudinal and event time processes, and to measures of discrimination and calibration in the context of dynamic prediction.
A key question in clinical practice is accurate prediction of patient prognosis. To this end, nowadays, physicians have at their disposal a variety of tests and biomarkers to aid them in optimizing medical care. These tests are often performed on a regular basis in order to closely follow the progression of the disease. In this setting, it is of interest to optimally utilize the recorded information and provide medically relevant summary measures, such as survival probabilities, which will aid in decision making. In this work, we present and compare two statistical techniques that provide dynamically updated estimates of survival probabilities, namely landmark analysis and joint models for longitudinal and time-to-event data. Special attention is given to the functional form linking the longitudinal and event time processes, and to measures of discrimination and calibration in the context of dynamic prediction.A key question in clinical practice is accurate prediction of patient prognosis. To this end, nowadays, physicians have at their disposal a variety of tests and biomarkers to aid them in optimizing medical care. These tests are often performed on a regular basis in order to closely follow the progression of the disease. In this setting, it is of interest to optimally utilize the recorded information and provide medically relevant summary measures, such as survival probabilities, which will aid in decision making. In this work, we present and compare two statistical techniques that provide dynamically updated estimates of survival probabilities, namely landmark analysis and joint models for longitudinal and time-to-event data. Special attention is given to the functional form linking the longitudinal and event time processes, and to measures of discrimination and calibration in the context of dynamic prediction.
Author Molenberghs, Geert
Rizopoulos, Dimitris
Lesaffre, Emmanuel M.E.H.
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  surname: Rizopoulos
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  organization: Erasmus Medical Center
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  fullname: Molenberghs, Geert
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  givenname: Emmanuel M.E.H.
  surname: Lesaffre
  fullname: Lesaffre, Emmanuel M.E.H.
  organization: Interuniversity Institute for Biostatistics and Statistical Bioinformatics, KU Leuven & Universiteit Hasselt
BackLink https://www.ncbi.nlm.nih.gov/pubmed/28792080$$D View this record in MEDLINE/PubMed
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Keywords Random effects
Risk prediction
Discrimination
Calibration
Prognostic modeling
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Snippet A key question in clinical practice is accurate prediction of patient prognosis. To this end, nowadays, physicians have at their disposal a variety of tests...
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SubjectTerms Aortic Valve - surgery
Biomarkers
Biometry - methods
Calibration
Decision making
Discrimination
Economic models
Female
Heuristics
Humans
Information processing
Male
Mathematical models
Medical prognosis
Models, Statistical
Optimization
Physicians
Probability
Prognostic modeling
Random effects
Reoperation
Risk prediction
Statistical analysis
Survival
Survival Analysis
Time Factors
Title Dynamic predictions with time‐dependent covariates in survival analysis using joint modeling and landmarking
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fbimj.201600238
https://www.ncbi.nlm.nih.gov/pubmed/28792080
https://www.proquest.com/docview/1963865799
https://www.proquest.com/docview/1927593827
Volume 59
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