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胃肠道癌症负担与信息不完整问题;如何补充数据?

Burden of gastrointestinal cancers and problem of the incomplete information; how to make up the data?

作者信息

Sharifian Abdolhamid, Pourhoseingholi Mohamad Amin, Baghestani Ahmadreza, Hajizadeh Nastaran, Gholizadeh Sepideh

机构信息

Gandy General Hospital, Tehran, Iran.

Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

出版信息

Gastroenterol Hepatol Bed Bench. 2016 Winter;9(1):12-7.

PMID:26744609
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4702036/
Abstract

Cancer registration is an important source for measuring the burden of cancer in a population. In practice, however, quite frequently incorrect patients are registered or data items can be inaccurately recorded or not recorded at all. Also the process or quality of these registrations varies among countries. In this paper, we briefly discussed some statistical techniques including; Mortality and Incidence Analysis Model (MIAMOD), Prevalence and Incidence Analysis Model (PIAMOD), Bayesian Inference and Capture-recapture methods, which provide tools to re-correct the incomplete or misclassified cancer statistics with regards to gastrointestinal cancers.

摘要

癌症登记是衡量人群癌症负担的重要来源。然而,在实际操作中,经常会出现错误登记患者的情况,或者数据项记录不准确或根本未记录。而且这些登记的过程和质量在不同国家也有所不同。在本文中,我们简要讨论了一些统计技术,包括死亡率和发病率分析模型(MIAMOD)、患病率和发病率分析模型(PIAMOD)、贝叶斯推理和捕获再捕获方法,这些技术为重新校正关于胃肠道癌症的不完整或错误分类的癌症统计数据提供了工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6aa/4702036/7428dfebbbca/GHFBB-9-12-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6aa/4702036/7428dfebbbca/GHFBB-9-12-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6aa/4702036/7428dfebbbca/GHFBB-9-12-g001.jpg

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本文引用的文献

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Burden of gastrointestinal cancer in Asia; an overview.亚洲胃肠道癌症负担概述
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2
Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012.全球癌症发病与死亡:GLOBOCAN 2012 数据源、方法与主要模式。
Int J Cancer. 2015 Mar 1;136(5):E359-86. doi: 10.1002/ijc.29210. Epub 2014 Oct 9.
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Estimating cancer incidence using a Bayesian back-calculation approach.使用贝叶斯反向计算方法估计癌症发病率。
伊朗各省对错误分类数据进行贝叶斯校正后肝细胞癌发病率的趋势。
World J Hepatol. 2017 May 28;9(15):704-710. doi: 10.4254/wjh.v9.i15.704.
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Bayesian adjustment of gastric cancer mortality rate in the presence of misclassification.存在错误分类情况下胃癌死亡率的贝叶斯调整
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Bayesian adjustment for over-estimation and under-estimation of gastric cancer incidence across Iranian provinces.对伊朗各省份胃癌发病率高估和低估情况的贝叶斯调整。
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Estimating the size of hidden populations from register data.根据登记数据估算隐性人口规模。
BMC Med Res Methodol. 2014 Apr 27;14:58. doi: 10.1186/1471-2288-14-58.
5
Estimation of the gastric cancer incidence in Tehran by two- source capture-recapture.采用双源捕获-再捕获法估算德黑兰胃癌发病率。
Asian Pac J Cancer Prev. 2013;14(2):673-7. doi: 10.7314/apjcp.2013.14.2.673.
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A comprehensive assessment of the impact of errors in the cancer registration process on 1- and 5-year relative survival estimates.癌症登记过程中错误对 1 年和 5 年相对生存率估计影响的综合评估。
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