Abstract:
:A significant source of missing data in longitudinal epidemiological studies on elderly individuals is death. Subjects in large scale community-based longitudinal dementia studies are usually evaluated for disease status in study waves, not under continuous surveillance as in traditional cohort studies. Therefore, for the deceased subjects, disease status prior to death cannot be ascertained. Statistical methods assuming deceased subjects to be missing at random may not be realistic in dementia studies and may lead to biased results. We propose a stochastic model approach to simultaneously estimate disease incidence and mortality rates. We set up a Markov chain model consisting of three states, non-diseased, diseased and dead, and estimate the transition hazard parameters using the maximum likelihood approach. Simulation results are presented indicating adequate performance of the proposed approach.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Harezlak J,Gao S,Hui SLdoi
10.1002/sim.1506subject
Has Abstractpub_date
2003-05-15 00:00:00pages
1465-75issue
9eissn
0277-6715issn
1097-0258journal_volume
22pub_type
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journal_title:Statistics in medicine
pub_type: 杂志文章
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journal_title:Statistics in medicine
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pub_type: 杂志文章
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pub_type: 杂志文章
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更新日期:2020-07-30 00:00:00
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pub_type: 评论,信件
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pub_type: 杂志文章
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journal_title:Statistics in medicine
pub_type: 杂志文章
doi:10.1002/sim.6624
更新日期:2016-01-30 00:00:00
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pub_type: 杂志文章,多中心研究
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journal_title:Statistics in medicine
pub_type: 临床试验,杂志文章,随机对照试验
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更新日期:1993-07-30 00:00:00
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pub_type: 杂志文章
doi:10.1002/sim.2322
更新日期:2006-05-30 00:00:00
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doi:10.1002/sim.7952
更新日期:2019-01-15 00:00:00
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pub_type: 杂志文章
doi:10.1002/1097-0258(20001230)19:24<3497::aid-sim830>
更新日期:2000-12-30 00:00:00
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更新日期:2009-10-15 00:00:00
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更新日期:2016-03-30 00:00:00
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doi:10.1002/sim.4780131007
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