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Using weighted regression model for estimating cohort effect in age-period contingency table data

BACKGROUND: Recently, the multiphase method was proposed to estimate cohort effects after removing the effects of age and period in age-period contingency table data. Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver and is strongly associated with cirrhosis, due to b...

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Autores principales: Tzeng, I-Shiang, Ng, Chau Yee, Chen, Jau-Yuan, Chen, Li-Shya, Wu, Chin-Chieh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5929429/
https://www.ncbi.nlm.nih.gov/pubmed/29731986
http://dx.doi.org/10.18632/oncotarget.24868
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author Tzeng, I-Shiang
Ng, Chau Yee
Chen, Jau-Yuan
Chen, Li-Shya
Wu, Chin-Chieh
author_facet Tzeng, I-Shiang
Ng, Chau Yee
Chen, Jau-Yuan
Chen, Li-Shya
Wu, Chin-Chieh
author_sort Tzeng, I-Shiang
collection PubMed
description BACKGROUND: Recently, the multiphase method was proposed to estimate cohort effects after removing the effects of age and period in age-period contingency table data. Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver and is strongly associated with cirrhosis, due to both alcohol and viral etiologies. In epidemiology, age-period-cohort (APC) model can be used to describe (or predict) the secular trend in HCC mortality. RESULTS: The confidence interval (CI) of the weighted estimates was found to be relatively narrow (compared to unweighted estimates). Moreover, for males, the mortality trend reverses itself during 2006–2010 was found from an increasing trend into a slightly deceasing trend. For females, the increasing trend reverses (earlier than males) itself during 2001–2005. CONCLUSIONS: The weighted estimation of the regression model is recommended for the multiphase method in estimating the cohort effects in age-period contingency table data. IMPACT: The regression model can be modified through the weighted average estimate of the effects with narrower CI of each cohort. METHODS: After isolating the residuals during the median polish phase, the final phase is to estimate the magnitude of the cohort effects using the regression model of these residuals on the cohort category with the weight equal to the occupied proportion according to the number of death of HCC in each cohort.
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spelling pubmed-59294292018-05-04 Using weighted regression model for estimating cohort effect in age-period contingency table data Tzeng, I-Shiang Ng, Chau Yee Chen, Jau-Yuan Chen, Li-Shya Wu, Chin-Chieh Oncotarget Research Paper BACKGROUND: Recently, the multiphase method was proposed to estimate cohort effects after removing the effects of age and period in age-period contingency table data. Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver and is strongly associated with cirrhosis, due to both alcohol and viral etiologies. In epidemiology, age-period-cohort (APC) model can be used to describe (or predict) the secular trend in HCC mortality. RESULTS: The confidence interval (CI) of the weighted estimates was found to be relatively narrow (compared to unweighted estimates). Moreover, for males, the mortality trend reverses itself during 2006–2010 was found from an increasing trend into a slightly deceasing trend. For females, the increasing trend reverses (earlier than males) itself during 2001–2005. CONCLUSIONS: The weighted estimation of the regression model is recommended for the multiphase method in estimating the cohort effects in age-period contingency table data. IMPACT: The regression model can be modified through the weighted average estimate of the effects with narrower CI of each cohort. METHODS: After isolating the residuals during the median polish phase, the final phase is to estimate the magnitude of the cohort effects using the regression model of these residuals on the cohort category with the weight equal to the occupied proportion according to the number of death of HCC in each cohort. Impact Journals LLC 2018-04-13 /pmc/articles/PMC5929429/ /pubmed/29731986 http://dx.doi.org/10.18632/oncotarget.24868 Text en Copyright: © 2018 Tzeng et al. http://creativecommons.org/licenses/by/3.0/ This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) (CC-BY), which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Research Paper
Tzeng, I-Shiang
Ng, Chau Yee
Chen, Jau-Yuan
Chen, Li-Shya
Wu, Chin-Chieh
Using weighted regression model for estimating cohort effect in age-period contingency table data
title Using weighted regression model for estimating cohort effect in age-period contingency table data
title_full Using weighted regression model for estimating cohort effect in age-period contingency table data
title_fullStr Using weighted regression model for estimating cohort effect in age-period contingency table data
title_full_unstemmed Using weighted regression model for estimating cohort effect in age-period contingency table data
title_short Using weighted regression model for estimating cohort effect in age-period contingency table data
title_sort using weighted regression model for estimating cohort effect in age-period contingency table data
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5929429/
https://www.ncbi.nlm.nih.gov/pubmed/29731986
http://dx.doi.org/10.18632/oncotarget.24868
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