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发病率、进展、消退和消失概率的综合分析。

Integrated analysis of incidence, progression, regression and disappearance probabilities.

作者信息

Huang Guan-Hua

机构信息

Institute of Statistics, National Chiao Tung University, Hsinchu, Taiwan.

出版信息

BMC Med Res Methodol. 2008 Jun 25;8:40. doi: 10.1186/1471-2288-8-40.

Abstract

BACKGROUND

Age-related maculopathy (ARM) is a leading cause of vision loss in people aged 65 or older. ARM is distinctive in that it is a disease which can transition through incidence, progression, regression and disappearance. The purpose of this study is to develop methodologies for studying the relationship of risk factors with different transition probabilities.

METHODS

Our framework for studying this relationship includes two different analytical approaches. In the first approach, one can define, model and estimate the relationship between each transition probability and risk factors separately. This approach is similar to constraining a population to a certain disease status at the baseline, and then analyzing the probability of the constrained population to develop a different status. While this approach is intuitive, one risks losing available information while at the same time running into the problem of insufficient sample size. The second approach specifies a transition model for analyzing such a disease. This model provides the conditional probability of a current disease status based upon a previous status, and can therefore jointly analyze all transition probabilities. Throughout the paper, an analysis to determine the birth cohort effect on ARM is used as an illustration.

RESULTS AND CONCLUSION

This study has found parallel separate and joint analyses to be more enlightening than any analysis in isolation. By implementing both approaches, one can obtain more reliable and more efficient results.

摘要

背景

年龄相关性黄斑病变(ARM)是65岁及以上人群视力丧失的主要原因。ARM的独特之处在于它是一种可以经历发病、进展、消退和消失过程的疾病。本研究的目的是开发用于研究风险因素与不同转变概率之间关系的方法。

方法

我们用于研究这种关系的框架包括两种不同的分析方法。在第一种方法中,可以分别定义、建模和估计每个转变概率与风险因素之间的关系。这种方法类似于在基线时将人群限制在某种疾病状态,然后分析受限人群发展为不同状态的概率。虽然这种方法直观,但存在丢失可用信息的风险,同时还会遇到样本量不足的问题。第二种方法指定一个转变模型来分析此类疾病。该模型基于先前状态提供当前疾病状态的条件概率,因此可以联合分析所有转变概率。在整篇论文中,以确定出生队列对ARM的影响的分析为例进行说明。

结果与结论

本研究发现并行的单独分析和联合分析比任何单独的分析更具启发性。通过实施这两种方法,可以获得更可靠、更有效的结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6696/2459200/7307fbd699d8/1471-2288-8-40-1.jpg

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