Literature DB >> 8910963

Missing cause of death information in the analysis of survival data.

J Andersen1, E Goetghebeur, L Ryan.   

Abstract

Goetghebeur and Ryan proposed a method for proportional hazards analyses of competing risks failure-time data when the failure type is missing for some cases. This paper evaluates the properties of the method using data from a clinical trial in Hodgkin's disease. We generated several patterns of missingness in the cause of death in 'pseudo-studies' derived from the study database. We found that the proposed method provided regression coefficients and inferences that were less biased than those from other methods over an increasing percentage of missingness in the failure type when missingness is random, when it depends on an important covariate, when it depends on failure type, and when it depends on follow-up time. We present suggestions for study design with planned missingness in the failure type.

Entities:  

Mesh:

Year:  1996        PMID: 8910963     DOI: 10.1002/(SICI)1097-0258(19961030)15:20<2191::AID-SIM358>3.0.CO;2-D

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


  7 in total

1.  Comparison between two partial likelihood approaches for the competing risks model with missing cause of failure.

Authors:  Kaifeng Lu; Anastasios A Tsiatis
Journal:  Lifetime Data Anal       Date:  2005-03       Impact factor: 1.588

2.  A method for analyzing disease-specific mortality with missing cause of death information.

Authors:  Ping K Ruan; Robert J Gray
Journal:  Lifetime Data Anal       Date:  2006-03       Impact factor: 1.588

3.  Proportional hazards model for competing risks data with missing cause of failure.

Authors:  Seunggeun Hyun; Jimin Lee; Yanqing Sun
Journal:  J Stat Plan Inference       Date:  2012-02-21       Impact factor: 1.111

4.  Analysis of interval-censored competing risks data under missing causes.

Authors:  Debanjan Mitra; Ujjwal Das; Kalyan Das
Journal:  J Appl Stat       Date:  2019-07-16       Impact factor: 1.416

5.  Comparing conditional survival functions with missing population marks in a competing risks model.

Authors:  Dipankar Bandyopadhyay; M Amalia Jácome
Journal:  Comput Stat Data Anal       Date:  2016-03-01       Impact factor: 1.681

6.  Benefits and limitations of Kaplan-Meier calculations of survival chance in cancer surgery.

Authors:  Elfriede Bollschweiler
Journal:  Langenbecks Arch Surg       Date:  2003-08-14       Impact factor: 3.445

7.  Prostate cancer: net survival and cause-specific survival rates after multiple imputation.

Authors:  Adeline Morisot; Faïza Bessaoud; Paul Landais; Xavier Rébillard; Brigitte Trétarre; Jean-Pierre Daurès
Journal:  BMC Med Res Methodol       Date:  2015-07-28       Impact factor: 4.615

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.