Analysis of Dependently Truncated Data in Cox Framework

Liu, Yang; Li, Ji; Zhang, Xu

doi:10.1080/03610918.2017.1322699

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Analysis of Dependently Truncated Data in Cox Framework

2018
By Liu, Yang ; Li, Ji ; Zhang, Xu
http://dx.doi.org/10.1080/03610918.2017.1322699
Source: Commun Stat Simul Comput. 47(6):1677-1695

[PDF-597.81 KB]

English

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Details:

Alternative Title:

Commun Stat Simul Comput
Personal Author:

Liu, Yang ; Li, Ji ; Zhang, Xu

Liu, Yang ; Li, Ji ; Zhang, Xu Less -
Description:

Truncation is a known feature of bone marrow transplant (BMT) registry data, for which the survival time of a leukemia patient is left truncated by the waiting time to transplant. It was recently noted that a longer waiting time was linked to poorer survival. A straightforward solution is a Cox model on the survival time with the waiting time as both truncation variable and covariate. The Cox model should also include other recognized risk factors as covariates. In this paper we focus on estimating the distribution function of waiting time and the probability of selection under the aforementioned Cox model.
Keywords:

[+]

Cox Model Dependent Truncation Inverse Probability Weighting
Source:

Commun Stat Simul Comput. 47(6):1677-1695
Pubmed ID:

28725104
Pubmed Central ID:

PMC5513481
Document Type:

Journal Article
Funding:

CC999999/Intramural CDC HHSUnited States/ ; UL1 TR000371/TR/NCATS NIH HHSUnited States/

CC999999/Intramural CDC HHSUnited States/ ; UL1 TR000371/TR/NCATS NIH HHSUnited States/ Less -
Collection(s):

CDC Public Access
Main Document Checksum:

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urn:sha256:e7f010866dc231f13f2850e5eba70ff6a39bf314fb3cf0caa1528fa9dc6d9a3b
Download URL:

https://stacks.cdc.gov/view/cdc/47522/cdc_47522_DS1.pdf
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