Evidence map›Paper›PMID 34950945›Full record

ReviewThe journals of gerontology. Series A, Biological sciences and medical sciences2022

From Model Organisms to Humans, the Opportunity for More Rigor in Methodologic and Statistical Analysis, Design, and Interpretation of Aging and Senescence Research.

Daniella E Chusyd, Steven N Austad, Andrew W Brown, Xiwei Chen, Stephanie L Dickinson, Keisuke Ejima, David Fluharty, Lilian Golzarri-Arroyo, Richard Holden, Yasaman Jamshidi-Naeini and 5 more

Abstract readReview
In one paragraph

Review in The journals of gerontology. Series A, Biological sciences and medical sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Symposia Report of The Annual Biological Sciences Section Meeting of the Gerontological Society of America 2023, Tampa, Florida.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025
    Article
  2. Gerontologic Biostatistics and Data Science: Aging Research in the Era of Big Data.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024
    Review
  3. Article
  4. Disciplinary Roots of 300 Top-Ranked Scientific Contributors to Gerontology: From Legacy to Enriching Our Discovery.The journals of gerontology. Series A, Biological sciences and medical sciences · 2022
    Article
  5. Article
  6. Article
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

15 authors.

Daniella E ChusydDepartment of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, Indiana, USA.
Steven N AustadDepartment of Biology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Andrew W BrownDepartment of Applied Health Science, Indiana University Bloomington, Bloomington, Indiana, USA.ORCID 0000-0002-1758-8205
Xiwei ChenDepartment of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, Indiana, USA.
Stephanie L DickinsonDepartment of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, Indiana, USA.
Keisuke EjimaDepartment of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, Indiana, USA.ORCID 0000-0002-1185-3987
David FluhartyDepartment of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, Indiana, USA.
Lilian Golzarri-ArroyoDepartment of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, Indiana, USA.
Richard HoldenDepartment of Health and Wellness Design, Indiana University Bloomington, Bloomington, Indiana, USA.
Yasaman Jamshidi-NaeiniDepartment of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, Indiana, USA.
Doug LandsittelDepartment of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, Indiana, USA.
Stella LarteyDepartment of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, Indiana, USA.
Edward MannixDepartment of Anatomy, Cell Biology, and Physiology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Colby J VorlandDepartment of Applied Health Science, Indiana University Bloomington, Bloomington, Indiana, USA.ORCID 0000-0003-4225-372X
David B AllisonDepartment of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, Indiana, USA.

Funding

Sex hormones and arthritis in a long lived animal modelP30AG050886 · NIA · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Jianhua Zhang · 2015 to 2026
$11.3M
Nathan Shock Centers Coordinating CenterU24AG056053 · NIA · AMERICAN FEDERATION FOR AGING RESEARCH · PI STEVEN N. AUSTAD, Stephanie Lederman · 2017 to 2026
$6.1M
Strengthening Causal Inference in Behavioral Obesity ResearchR25HL124208 · NHLBI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI ALLISON, DAVID B, BROWN, ANDREW W · 2014 to 2023
$1.8M
The Mathematical Sciences in Obesity ResearchR25DK099080 · NIDDK · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI DAVID B ALLISON, Andrew W Brown · 2013 to 2026
$1.2M
Gordon and Betty Moore FoundationNHLBI NIH HHS R25 HL124208NIA NIH HHS P30 AG050886NIA NIH HHS U24 AG056053NIDDK NIH HHS R25 DK099080
6 · The paper itself

Abstract

This review identifies frequent design and analysis errors in aging and senescence research and discusses best practices in study design, statistical methods, analyses, and interpretation. Recommendations are offered for how to avoid these problems. The following issues are addressed: (a) errors in randomization, (b) errors related to testing within-group instead of between-group differences, (c) failing to account for clustering, (d) failing to consider interference effects, (e) standardizing metrics of effect size, (f) maximum life-span testing, (g) testing for effects beyond the mean, (h) tests for power and sample size, (i) compression of morbidity versus survival curve squaring, and (j) other hot topics, including modeling high-dimensional data and complex relationships and assessing model assumptions and biases. We hope that bringing increased awareness of these topics to the scientific community will emphasize the importance of employing sound statistical practices in all aspects of aging and senescence research.

Indexed as

AgingResearch DesignBiasData Interpretation, StatisticalHumansSample SizeGeroscienceMethodologiesReproducibility

Identifiers

PMID34950945
PMCPMC9678201

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.