Literature DB >> 23839860

Decomposition of number of life years lost according to causes of death.

P K Andersen1.   

Abstract

We study the competing risks model and show that the cause j cumulative incidence function integrated from 0 to τ has a natural interpretation as the expected number of life years lost due to cause j before time τ. This is analogous to the τ-restricted mean lifetime, which is the survival function integrated from 0 to τ. It is discussed how the number of years lost may be related to subject-specific explanatory variables in a regression model based on pseudo-observations, and the method is exemplified using data from a bone marrow transplantation study. Finally, inclusion of standard mortality rates is discussed.
Copyright © 2013 John Wiley & Sons, Ltd.

Entities:  

Keywords:  competing risks; life expectancy; pseudo-observations; restricted mean; standard mortality rates; survival analysis

Mesh:

Year:  2013        PMID: 23839860     DOI: 10.1002/sim.5903

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


  29 in total

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Journal:  Eur J Epidemiol       Date:  2018-05-05       Impact factor: 8.082

2.  Modeling marginal features in studies of recurrent events in the presence of a terminal event.

Authors:  Per Kragh Andersen; Jules Angst; Henrik Ravn
Journal:  Lifetime Data Anal       Date:  2019-01-29       Impact factor: 1.588

3.  Adjusted restricted mean survival times in observational studies.

Authors:  Sarah C Conner; Lisa M Sullivan; Emelia J Benjamin; Michael P LaValley; Sandro Galea; Ludovic Trinquart
Journal:  Stat Med       Date:  2019-05-22       Impact factor: 2.373

Review 4.  Goodness of fit tests for estimating equations based on pseudo-observations.

Authors:  Klemen Pavlič; Torben Martinussen; Per Kragh Andersen
Journal:  Lifetime Data Anal       Date:  2018-02-27       Impact factor: 1.588

5.  Multiple event times in the presence of informative censoring: modeling and analysis by copulas.

Authors:  Dongdong Li; X Joan Hu; Mary L McBride; John J Spinelli
Journal:  Lifetime Data Anal       Date:  2019-11-15       Impact factor: 1.588

6.  Quantile regression on inactivity time.

Authors:  Lauren C Balmert; Ruosha Li; Limin Peng; Jong-Hyeon Jeong
Journal:  Stat Methods Med Res       Date:  2021-03-20       Impact factor: 3.021

7.  Modeling restricted mean survival time under general censoring mechanisms.

Authors:  Xin Wang; Douglas E Schaubel
Journal:  Lifetime Data Anal       Date:  2017-02-21       Impact factor: 1.588

8.  Long-term Somatic Disease Risk in Adult Danish Cancer Survivors.

Authors:  Trille Kristina Kjaer; Elisabeth Anne Wreford Andersen; Jeanette Falck Winther; Pernille Envold Bidstrup; Michael Borre; Henrik Møller; Signe Benzon Larsen; Christoffer Johansen; Susanne Oksbjerg Dalton
Journal:  JAMA Oncol       Date:  2019-04-01       Impact factor: 31.777

9.  Missingness in the Setting of Competing Risks: from missing values to missing potential outcomes.

Authors:  Bryan Lau; Catherine Lesko
Journal:  Curr Epidemiol Rep       Date:  2018-03-19

10.  Direct modeling of the crude probability of cancer death and the number of life years lost due to cancer without the need of cause of death: a pseudo-observation approach in the relative survival setting.

Authors:  Dimitra-Kleio Kipourou; Maja Pohar Perme; Bernard Rachet; Aurelien Belot
Journal:  Biostatistics       Date:  2022-01-13       Impact factor: 5.899

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