Literature DB >> 22081496

Interpretability and importance of functionals in competing risks and multistate models.

Per Kragh Andersen1, Niels Keiding.   

Abstract

The basic parameters in both survival analysis and more general multistate models, including the competing risks model and the illness-death model, are the transition hazards. It is often necessary to supplement the analysis of such models with other model parameters, which are all functionals of the transition hazards. Unfortunately, not all such functionals are equally meaningful in practical contexts, even though they may be mathematically well defined. We have found it useful to check whether the functionals satisfy three simple principles, which may be used as criteria for practical interpretability.
Copyright © 2011 John Wiley & Sons, Ltd.

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Year:  2011        PMID: 22081496     DOI: 10.1002/sim.4385

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  63 in total

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Review 2.  Applying competing risks regression models: an overview.

Authors:  Bernhard Haller; Georg Schmidt; Kurt Ulm
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Review 4.  Incorporation of clinical and biological factors improves prognostication and reflects contemporary clinical practice.

Authors:  Rashmi K Murthy; Juhee Song; Akshara S Raghavendra; Yisheng Li; Limin Hsu; Kenneth R Hess; Carlos H Barcenas; Vicente Valero; Robert W Carlson; Debu Tripathy; Gabriel N Hortobagyi
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5.  Survival analysis in the presence of competing risks.

Authors:  Zhongheng Zhang
Journal:  Ann Transl Med       Date:  2017-02

6.  Nested exposure case-control sampling: a sampling scheme to analyze rare time-dependent exposures.

Authors:  Jan Feifel; Madlen Gebauer; Martin Schumacher; Jan Beyersmann
Journal:  Lifetime Data Anal       Date:  2018-11-13       Impact factor: 1.588

7.  Contribution to the discussion of 'Survival models and health sequences' by W. Dempsey and P. McCullagh.

Authors:  Per Kragh Andersen
Journal:  Lifetime Data Anal       Date:  2018-07-18       Impact factor: 1.588

8.  Bayesian Semi-parametric Analysis of Semi-competing Risks Data: Investigating Hospital Readmission after a Pancreatic Cancer Diagnosis.

Authors:  Kyu Ha Lee; Sebastien Haneuse; Deborah Schrag; Francesca Dominici
Journal:  J R Stat Soc Ser C Appl Stat       Date:  2015-02-01       Impact factor: 1.864

Review 9.  Comparison of stopped Cox regression with direct methods such as pseudo-values and binomial regression.

Authors:  Hans C van Houwelingen; Hein Putter
Journal:  Lifetime Data Anal       Date:  2014-08-02       Impact factor: 1.588

10.  Application of multi-state models in cancer clinical trials.

Authors:  Jennifer G Le-Rademacher; Ryan A Peterson; Terry M Therneau; Ben L Sanford; Richard M Stone; Sumithra J Mandrekar
Journal:  Clin Trials       Date:  2018-07-23       Impact factor: 2.486

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