Evidence map›Paper›PMID 25590047›Full record

ArticleAdvances in geriatrics

Biodemographic Analyses of Longitudinal Data on Aging, Health, and Longevity: Recent Advances and Future Perspectives.

Konstantin G Arbeev, Igor Akushevich, Alexander M Kulminski, Svetlana V Ukraintseva, Anatoliy I Yashin

Abstract read
In one paragraph

Article in Advances in geriatrics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Dynamics of biomarkers in relation to aging and mortality.Mechanisms of ageing and development · 2016
    Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Konstantin G ArbeevCenter for Population Health and Aging, Duke University, Erwin Mill Building, 2024 W. Main Street, P.O. Box 90420, Durham, NC 27705, USA.
Igor AkushevichCenter for Population Health and Aging, Duke University, Erwin Mill Building, 2024 W. Main Street, P.O. Box 90420, Durham, NC 27705, USA.
Alexander M KulminskiCenter for Population Health and Aging, Duke University, Erwin Mill Building, 2024 W. Main Street, P.O. Box 90420, Durham, NC 27705, USA.
Svetlana V UkraintsevaCenter for Population Health and Aging, Duke University, Erwin Mill Building, 2024 W. Main Street, P.O. Box 90420, Durham, NC 27705, USA.
Anatoliy I YashinCenter for Population Health and Aging, Duke University, Erwin Mill Building, 2024 W. Main Street, P.O. Box 90420, Durham, NC 27705, USA.

Funding

Genetics of changes in population pyramids: Implications for health forecasting with a focus on Alzheimer's Disease and related dementiasRF1AG046860 · NIA · DUKE UNIVERSITY · PI YASHIN, ANATOLIY I · 2019 to 2019
$3.1M
YASHIN Admin Supp: Time Trends in Alzheimer's Disease: Addressing the Changing Role of Risk Factors and Instrument ValidityR01AG046860 · NIA · DUKE UNIVERSITY · PI YASHIN, ANATOLIY I · 2014 to 2018
$3.1M
Causal Effects of Time-Dependent Treatment: Optimizing Care of Cancer PatientsR21AG045245 · NIA · DUKE UNIVERSITY · PI AKUSHEVICH, IGOR · 2013 to 2013
$150k
NIA NIH HHS R01 AG046860NIA NIH HHS R21 AG045245NIA NIH HHS RF1 AG046860
6 · The paper itself

Abstract

Biodemography became one of the most innovative and fastest growing areas in demography. This progress is fueled by the growing variability and amount of relevant data available for analyses as well as by methodological developments allowing for addressing new research questions using new approaches that can better utilize the potential of these data. In this review paper, we summarize recent methodological advances in biodemography and their diverse practical applications. Three major topics are covered: (1) computational approaches to reconstruction of age patterns of incidence of geriatric diseases and other characteristics such as recovery rates at the population level using Medicare claims data; (2) methodological advances in genetic and genomic biodemography and applications to research on genetic determinants of longevity and health; and (3) biodemographic models for joint analyses of time-to-event data and longitudinal measurements of biomarkers collected in longitudinal studies on aging. We discuss how such data and methodology can be used in a comprehensive prediction model for joint analyses of incomplete datasets that take into account the wide spectrum of factors affecting health and mortality transitions including genetic factors and hidden mechanisms of aging-related changes in physiological variables in their dynamic connection with health and survival.

Identifiers

PMID25590047
PMCPMC4290867

What Socratic holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.