Improving Statistical Rigor in Animal Aging Research by Addressing Clustering and Nesting Effects: Illustration with the National Institute on Aging’s Intervention Testing Program Data Article Swipe
Erik Parker
,
Lilian Golzarri‐Arroyo
,
Stephanie Dickinson
,
Beate Henschel
,
Luis-Enrique Becerra-Garcia
,
Thirupathi Reddy Mokalla
,
Olivia C. Robertson
,
Deependra Kaji Thapa
,
Colby J. Vorland
,
David B. Allison
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.03.14.642436
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.03.14.642436
Clustering effects, such as those introduced by housing animals in shared cages, are often overlooked in preclinical lifespan studies, despite their potential to distort variance estimates and inflate Type I error rates, leading to misleading conclusions. This methodological oversight reduces statistical rigor and may undermine the reliability of findings. To address this gap, the current study examines the impact of accounting for clustering and nesting effects on lifespan analyses by comparing the results of statistical models which both account for and ignore these effects. Using 2019 data from the Interventions Testing Program (ITP), a large-scale initiative evaluating the effects of compounds on lifespan in UM-HET3 mice as a case study, we illustrate how different modeling approaches influence statistical estimates and conclusions. Clustering and nesting effects were addressed using linear mixed effects, and Cox frailty models, both of which explicitly account for cage-level dependencies and different levels of data nesting. Comparisons were made between unadjusted lifespan analyses and those incorporating clustering and nesting adjustments. The results of this case study indicate that properly adjusting for clustering and nesting effects can change the conclusions drawn from statistical significance tests as compared to unadjusted model approaches, and so it remains best practice to properly account for clustering and nesting to reduce the potential for inflated Type I error rates. These findings highlight the importance of accounting for clustering and nesting in preclinical research to ensure valid and robust statistical inference. By demonstrating the practical application of clustering adjustments, this work underscores the broader implications for improving reproducibility and rigor in lifespan studies and other experimental designs.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.03.14.642436
- https://www.biorxiv.org/content/biorxiv/early/2025/03/17/2025.03.14.642436.full.pdf
- OA Status
- green
- Cited By
- 2
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408609953
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4408609953Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2025.03.14.642436Digital Object Identifier
- Title
-
Improving Statistical Rigor in Animal Aging Research by Addressing Clustering and Nesting Effects: Illustration with the National Institute on Aging’s Intervention Testing Program DataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-17Full publication date if available
- Authors
-
Erik Parker, Lilian Golzarri‐Arroyo, Stephanie Dickinson, Beate Henschel, Luis-Enrique Becerra-Garcia, Thirupathi Reddy Mokalla, Olivia C. Robertson, Deependra Kaji Thapa, Colby J. Vorland, David B. AllisonList of authors in order
- Landing page
-
https://doi.org/10.1101/2025.03.14.642436Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2025/03/17/2025.03.14.642436.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2025/03/17/2025.03.14.642436.full.pdfDirect OA link when available
- Concepts
-
Nesting (process), Cluster analysis, Statistical inference, Type I and type II errors, Econometrics, Inference, Computer science, Statistical hypothesis testing, Variance (accounting), Statistics, Machine learning, Mathematics, Engineering, Artificial intelligence, Accounting, Economics, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2Per-year citation counts (last 5 years)
- References (count)
-
23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4408609953 |
|---|---|
| doi | https://doi.org/10.1101/2025.03.14.642436 |
| ids.doi | https://doi.org/10.1101/2025.03.14.642436 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/40166275 |
| ids.openalex | https://openalex.org/W4408609953 |
| fwci | 6.67295593 |
| type | preprint |
| title | Improving Statistical Rigor in Animal Aging Research by Addressing Clustering and Nesting Effects: Illustration with the National Institute on Aging’s Intervention Testing Program Data |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10676 |
| topics[0].field.id | https://openalex.org/fields/13 |
| topics[0].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[0].score | 0.9639999866485596 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1302 |
| topics[0].subfield.display_name | Aging |
| topics[0].display_name | Genetics, Aging, and Longevity in Model Organisms |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2776937656 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8910856246948242 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q2229669 |
| concepts[0].display_name | Nesting (process) |
| concepts[1].id | https://openalex.org/C73555534 |
| concepts[1].level | 2 |
| concepts[1].score | 0.815284013748169 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q622825 |
| concepts[1].display_name | Cluster analysis |
| concepts[2].id | https://openalex.org/C134261354 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6993173956871033 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q938438 |
| concepts[2].display_name | Statistical inference |
| concepts[3].id | https://openalex.org/C40696583 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5382393002510071 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q989120 |
| concepts[3].display_name | Type I and type II errors |
| concepts[4].id | https://openalex.org/C149782125 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5304859280586243 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[4].display_name | Econometrics |
| concepts[5].id | https://openalex.org/C2776214188 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5296991467475891 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q408386 |
| concepts[5].display_name | Inference |
| concepts[6].id | https://openalex.org/C41008148 |
| concepts[6].level | 0 |
| concepts[6].score | 0.4856047034263611 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[6].display_name | Computer science |
| concepts[7].id | https://openalex.org/C87007009 |
| concepts[7].level | 2 |
| concepts[7].score | 0.45692870020866394 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q210832 |
| concepts[7].display_name | Statistical hypothesis testing |
| concepts[8].id | https://openalex.org/C196083921 |
| concepts[8].level | 2 |
| concepts[8].score | 0.45348379015922546 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7915758 |
| concepts[8].display_name | Variance (accounting) |
| concepts[9].id | https://openalex.org/C105795698 |
| concepts[9].level | 1 |
| concepts[9].score | 0.41213592886924744 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[9].display_name | Statistics |
| concepts[10].id | https://openalex.org/C119857082 |
| concepts[10].level | 1 |
| concepts[10].score | 0.20470279455184937 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[10].display_name | Machine learning |
| concepts[11].id | https://openalex.org/C33923547 |
| concepts[11].level | 0 |
| concepts[11].score | 0.19727358222007751 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[11].display_name | Mathematics |
| concepts[12].id | https://openalex.org/C127413603 |
| concepts[12].level | 0 |
| concepts[12].score | 0.17857223749160767 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[12].display_name | Engineering |
| concepts[13].id | https://openalex.org/C154945302 |
| concepts[13].level | 1 |
| concepts[13].score | 0.14375829696655273 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[13].display_name | Artificial intelligence |
| concepts[14].id | https://openalex.org/C121955636 |
| concepts[14].level | 1 |
| concepts[14].score | 0.1408262848854065 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q4116214 |
| concepts[14].display_name | Accounting |
| concepts[15].id | https://openalex.org/C162324750 |
| concepts[15].level | 0 |
| concepts[15].score | 0.11831185221672058 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[15].display_name | Economics |
| concepts[16].id | https://openalex.org/C78519656 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q101333 |
| concepts[16].display_name | Mechanical engineering |
| keywords[0].id | https://openalex.org/keywords/nesting |
| keywords[0].score | 0.8910856246948242 |
| keywords[0].display_name | Nesting (process) |
| keywords[1].id | https://openalex.org/keywords/cluster-analysis |
| keywords[1].score | 0.815284013748169 |
| keywords[1].display_name | Cluster analysis |
| keywords[2].id | https://openalex.org/keywords/statistical-inference |
| keywords[2].score | 0.6993173956871033 |
| keywords[2].display_name | Statistical inference |
| keywords[3].id | https://openalex.org/keywords/type-i-and-type-ii-errors |
| keywords[3].score | 0.5382393002510071 |
| keywords[3].display_name | Type I and type II errors |
| keywords[4].id | https://openalex.org/keywords/econometrics |
| keywords[4].score | 0.5304859280586243 |
| keywords[4].display_name | Econometrics |
| keywords[5].id | https://openalex.org/keywords/inference |
| keywords[5].score | 0.5296991467475891 |
| keywords[5].display_name | Inference |
| keywords[6].id | https://openalex.org/keywords/computer-science |
| keywords[6].score | 0.4856047034263611 |
| keywords[6].display_name | Computer science |
| keywords[7].id | https://openalex.org/keywords/statistical-hypothesis-testing |
| keywords[7].score | 0.45692870020866394 |
| keywords[7].display_name | Statistical hypothesis testing |
| keywords[8].id | https://openalex.org/keywords/variance |
| keywords[8].score | 0.45348379015922546 |
| keywords[8].display_name | Variance (accounting) |
| keywords[9].id | https://openalex.org/keywords/statistics |
| keywords[9].score | 0.41213592886924744 |
| keywords[9].display_name | Statistics |
| keywords[10].id | https://openalex.org/keywords/machine-learning |
| keywords[10].score | 0.20470279455184937 |
| keywords[10].display_name | Machine learning |
| keywords[11].id | https://openalex.org/keywords/mathematics |
| keywords[11].score | 0.19727358222007751 |
| keywords[11].display_name | Mathematics |
| keywords[12].id | https://openalex.org/keywords/engineering |
| keywords[12].score | 0.17857223749160767 |
| keywords[12].display_name | Engineering |
| keywords[13].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[13].score | 0.14375829696655273 |
| keywords[13].display_name | Artificial intelligence |
| keywords[14].id | https://openalex.org/keywords/accounting |
| keywords[14].score | 0.1408262848854065 |
| keywords[14].display_name | Accounting |
| keywords[15].id | https://openalex.org/keywords/economics |
| keywords[15].score | 0.11831185221672058 |
| keywords[15].display_name | Economics |
| language | en |
| locations[0].id | doi:10.1101/2025.03.14.642436 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306402567 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| locations[0].source.host_organization | https://openalex.org/I2750212522 |
| locations[0].source.host_organization_name | Cold Spring Harbor Laboratory |
| locations[0].source.host_organization_lineage | https://openalex.org/I2750212522 |
| locations[0].license | |
| locations[0].pdf_url | https://www.biorxiv.org/content/biorxiv/early/2025/03/17/2025.03.14.642436.full.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.1101/2025.03.14.642436 |
| locations[1].id | pmid:40166275 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | bioRxiv : the preprint server for biology |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/40166275 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:11956962 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | bioRxiv |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11956962 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5082540556 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-5537-7900 |
| authorships[0].author.display_name | Erik Parker |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210119109 |
| authorships[0].affiliations[0].raw_affiliation_string | Indiana University Bloomington |
| authorships[0].institutions[0].id | https://openalex.org/I4210119109 |
| authorships[0].institutions[0].ror | https://ror.org/02k40bc56 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210119109, https://openalex.org/I592451 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Indiana University Bloomington |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Erik S Parker |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Indiana University Bloomington |
| authorships[1].author.id | https://openalex.org/A5002938718 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-1221-6701 |
| authorships[1].author.display_name | Lilian Golzarri‐Arroyo |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210119109 |
| authorships[1].affiliations[0].raw_affiliation_string | Indiana University Bloomington |
| authorships[1].institutions[0].id | https://openalex.org/I4210119109 |
| authorships[1].institutions[0].ror | https://ror.org/02k40bc56 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210119109, https://openalex.org/I592451 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | Indiana University Bloomington |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Lilian Golzarri-Arroyo |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Indiana University Bloomington |
| authorships[2].author.id | https://openalex.org/A5080022641 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-2998-4467 |
| authorships[2].author.display_name | Stephanie Dickinson |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210119109 |
| authorships[2].affiliations[0].raw_affiliation_string | Indiana University Bloomington |
| authorships[2].institutions[0].id | https://openalex.org/I4210119109 |
| authorships[2].institutions[0].ror | https://ror.org/02k40bc56 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210119109, https://openalex.org/I592451 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | Indiana University Bloomington |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Stephanie Dickinson |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Indiana University Bloomington |
| authorships[3].author.id | https://openalex.org/A5002446726 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-0708-5660 |
| authorships[3].author.display_name | Beate Henschel |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210119109 |
| authorships[3].affiliations[0].raw_affiliation_string | Indiana University Bloomington |
| authorships[3].institutions[0].id | https://openalex.org/I4210119109 |
| authorships[3].institutions[0].ror | https://ror.org/02k40bc56 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210119109, https://openalex.org/I592451 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | Indiana University Bloomington |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Beate Henschel |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Indiana University Bloomington |
| authorships[4].author.id | https://openalex.org/A5092761509 |
| authorships[4].author.orcid | https://orcid.org/0009-0005-3085-3287 |
| authorships[4].author.display_name | Luis-Enrique Becerra-Garcia |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210119109 |
| authorships[4].affiliations[0].raw_affiliation_string | Indiana University Bloomington |
| authorships[4].institutions[0].id | https://openalex.org/I4210119109 |
| authorships[4].institutions[0].ror | https://ror.org/02k40bc56 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210119109, https://openalex.org/I592451 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | Indiana University Bloomington |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Luis-Enrique Becerra-Garcia |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Indiana University Bloomington |
| authorships[5].author.id | https://openalex.org/A5015727446 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-6329-7206 |
| authorships[5].author.display_name | Thirupathi Reddy Mokalla |
| authorships[5].countries | US |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I4210119109 |
| authorships[5].affiliations[0].raw_affiliation_string | Indiana University Bloomington |
| authorships[5].institutions[0].id | https://openalex.org/I4210119109 |
| authorships[5].institutions[0].ror | https://ror.org/02k40bc56 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I4210119109, https://openalex.org/I592451 |
| authorships[5].institutions[0].country_code | US |
| authorships[5].institutions[0].display_name | Indiana University Bloomington |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Thirupathi R. Mokalla |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Indiana University Bloomington |
| authorships[6].author.id | https://openalex.org/A5116697291 |
| authorships[6].author.orcid | |
| authorships[6].author.display_name | Olivia C. Robertson |
| authorships[6].countries | US |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I4210119109 |
| authorships[6].affiliations[0].raw_affiliation_string | Indiana University Bloomington |
| authorships[6].institutions[0].id | https://openalex.org/I4210119109 |
| authorships[6].institutions[0].ror | https://ror.org/02k40bc56 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I4210119109, https://openalex.org/I592451 |
| authorships[6].institutions[0].country_code | US |
| authorships[6].institutions[0].display_name | Indiana University Bloomington |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Olivia C. Robertson |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Indiana University Bloomington |
| authorships[7].author.id | https://openalex.org/A5083909155 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-5689-0837 |
| authorships[7].author.display_name | Deependra Kaji Thapa |
| authorships[7].countries | US |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I4210119109 |
| authorships[7].affiliations[0].raw_affiliation_string | Indiana University Bloomington |
| authorships[7].institutions[0].id | https://openalex.org/I4210119109 |
| authorships[7].institutions[0].ror | https://ror.org/02k40bc56 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I4210119109, https://openalex.org/I592451 |
| authorships[7].institutions[0].country_code | US |
| authorships[7].institutions[0].display_name | Indiana University Bloomington |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Deependra K. Thapa |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Indiana University Bloomington |
| authorships[8].author.id | https://openalex.org/A5074473858 |
| authorships[8].author.orcid | https://orcid.org/0000-0003-4225-372X |
| authorships[8].author.display_name | Colby J. Vorland |
| authorships[8].countries | US |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I4210119109 |
| authorships[8].affiliations[0].raw_affiliation_string | Indiana University Bloomington |
| authorships[8].institutions[0].id | https://openalex.org/I4210119109 |
| authorships[8].institutions[0].ror | https://ror.org/02k40bc56 |
| authorships[8].institutions[0].type | education |
| authorships[8].institutions[0].lineage | https://openalex.org/I4210119109, https://openalex.org/I592451 |
| authorships[8].institutions[0].country_code | US |
| authorships[8].institutions[0].display_name | Indiana University Bloomington |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Colby J. Vorland |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | Indiana University Bloomington |
| authorships[9].author.id | https://openalex.org/A5062622249 |
| authorships[9].author.orcid | https://orcid.org/0000-0003-3566-9399 |
| authorships[9].author.display_name | David B. Allison |
| authorships[9].countries | US |
| authorships[9].affiliations[0].institution_ids | https://openalex.org/I4210119109 |
| authorships[9].affiliations[0].raw_affiliation_string | Indiana University Bloomington |
| authorships[9].institutions[0].id | https://openalex.org/I4210119109 |
| authorships[9].institutions[0].ror | https://ror.org/02k40bc56 |
| authorships[9].institutions[0].type | education |
| authorships[9].institutions[0].lineage | https://openalex.org/I4210119109, https://openalex.org/I592451 |
| authorships[9].institutions[0].country_code | US |
| authorships[9].institutions[0].display_name | Indiana University Bloomington |
| authorships[9].author_position | last |
| authorships[9].raw_author_name | David B. Allison |
| authorships[9].is_corresponding | False |
| authorships[9].raw_affiliation_strings | Indiana University Bloomington |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.biorxiv.org/content/biorxiv/early/2025/03/17/2025.03.14.642436.full.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Improving Statistical Rigor in Animal Aging Research by Addressing Clustering and Nesting Effects: Illustration with the National Institute on Aging’s Intervention Testing Program Data |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10676 |
| primary_topic.field.id | https://openalex.org/fields/13 |
| primary_topic.field.display_name | Biochemistry, Genetics and Molecular Biology |
| primary_topic.score | 0.9639999866485596 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1302 |
| primary_topic.subfield.display_name | Aging |
| primary_topic.display_name | Genetics, Aging, and Longevity in Model Organisms |
| related_works | https://openalex.org/W2419095690, https://openalex.org/W4290692481, https://openalex.org/W154531455, https://openalex.org/W3003558595, https://openalex.org/W2784206225, https://openalex.org/W1962532029, https://openalex.org/W2461683294, https://openalex.org/W4385586894, https://openalex.org/W2122180890, https://openalex.org/W4255580322 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| locations_count | 3 |
| best_oa_location.id | doi:10.1101/2025.03.14.642436 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306402567 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| best_oa_location.source.host_organization | https://openalex.org/I2750212522 |
| best_oa_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://www.biorxiv.org/content/biorxiv/early/2025/03/17/2025.03.14.642436.full.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.1101/2025.03.14.642436 |
| primary_location.id | doi:10.1101/2025.03.14.642436 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306402567 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| primary_location.source.host_organization | https://openalex.org/I2750212522 |
| primary_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| primary_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| primary_location.license | |
| primary_location.pdf_url | https://www.biorxiv.org/content/biorxiv/early/2025/03/17/2025.03.14.642436.full.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.1101/2025.03.14.642436 |
| publication_date | 2025-03-17 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W2598303606, https://openalex.org/W3029787982, https://openalex.org/W3031219790, https://openalex.org/W2119274873, https://openalex.org/W4312155622, https://openalex.org/W2989869936, https://openalex.org/W2085329742, https://openalex.org/W56226576, https://openalex.org/W4389272744, https://openalex.org/W4213443326, https://openalex.org/W2515120484, https://openalex.org/W4367059284, https://openalex.org/W4255051288, https://openalex.org/W1822978939, https://openalex.org/W1896828031, https://openalex.org/W4406315801, https://openalex.org/W2051361394, https://openalex.org/W2156003677, https://openalex.org/W2121944882, https://openalex.org/W3035066648, https://openalex.org/W2141443987, https://openalex.org/W3135775837, https://openalex.org/W6634321272 |
| referenced_works_count | 23 |
| abstract_inverted_index.I | 30, 214 |
| abstract_inverted_index.a | 94, 108 |
| abstract_inverted_index.By | 238 |
| abstract_inverted_index.To | 50 |
| abstract_inverted_index.as | 4, 107, 188 |
| abstract_inverted_index.by | 7, 70 |
| abstract_inverted_index.in | 10, 16, 104, 228, 257 |
| abstract_inverted_index.it | 196 |
| abstract_inverted_index.of | 48, 60, 74, 100, 137, 147, 166, 222, 243 |
| abstract_inverted_index.on | 67, 102 |
| abstract_inverted_index.so | 195 |
| abstract_inverted_index.to | 23, 34, 190, 200, 207, 231 |
| abstract_inverted_index.we | 111 |
| abstract_inverted_index.Cox | 133 |
| abstract_inverted_index.The | 164 |
| abstract_inverted_index.and | 27, 43, 64, 81, 120, 123, 132, 144, 157, 161, 176, 194, 205, 226, 234, 255, 260 |
| abstract_inverted_index.are | 13 |
| abstract_inverted_index.can | 179 |
| abstract_inverted_index.for | 62, 80, 141, 174, 203, 211, 224, 252 |
| abstract_inverted_index.how | 113 |
| abstract_inverted_index.may | 44 |
| abstract_inverted_index.the | 46, 54, 58, 72, 89, 98, 181, 209, 220, 240, 249 |
| abstract_inverted_index.2019 | 86 |
| abstract_inverted_index.This | 37 |
| abstract_inverted_index.Type | 29, 213 |
| abstract_inverted_index.best | 198 |
| abstract_inverted_index.both | 78, 136 |
| abstract_inverted_index.case | 109, 168 |
| abstract_inverted_index.data | 87, 148 |
| abstract_inverted_index.from | 88, 184 |
| abstract_inverted_index.gap, | 53 |
| abstract_inverted_index.made | 152 |
| abstract_inverted_index.mice | 106 |
| abstract_inverted_index.such | 3 |
| abstract_inverted_index.that | 171 |
| abstract_inverted_index.this | 52, 167, 246 |
| abstract_inverted_index.were | 126, 151 |
| abstract_inverted_index.work | 247 |
| abstract_inverted_index.These | 217 |
| abstract_inverted_index.Using | 85 |
| abstract_inverted_index.drawn | 183 |
| abstract_inverted_index.error | 31, 215 |
| abstract_inverted_index.mixed | 130 |
| abstract_inverted_index.model | 192 |
| abstract_inverted_index.often | 14 |
| abstract_inverted_index.other | 261 |
| abstract_inverted_index.rigor | 42, 256 |
| abstract_inverted_index.study | 56, 169 |
| abstract_inverted_index.tests | 187 |
| abstract_inverted_index.their | 21 |
| abstract_inverted_index.these | 83 |
| abstract_inverted_index.those | 5, 158 |
| abstract_inverted_index.using | 128 |
| abstract_inverted_index.valid | 233 |
| abstract_inverted_index.which | 77, 138 |
| abstract_inverted_index.(ITP), | 93 |
| abstract_inverted_index.cages, | 12 |
| abstract_inverted_index.change | 180 |
| abstract_inverted_index.ensure | 232 |
| abstract_inverted_index.ignore | 82 |
| abstract_inverted_index.impact | 59 |
| abstract_inverted_index.levels | 146 |
| abstract_inverted_index.linear | 129 |
| abstract_inverted_index.models | 76 |
| abstract_inverted_index.rates, | 32 |
| abstract_inverted_index.rates. | 216 |
| abstract_inverted_index.reduce | 208 |
| abstract_inverted_index.robust | 235 |
| abstract_inverted_index.shared | 11 |
| abstract_inverted_index.study, | 110 |
| abstract_inverted_index.Program | 92 |
| abstract_inverted_index.Testing | 91 |
| abstract_inverted_index.UM-HET3 | 105 |
| abstract_inverted_index.account | 79, 140, 202 |
| abstract_inverted_index.address | 51 |
| abstract_inverted_index.animals | 9 |
| abstract_inverted_index.between | 153 |
| abstract_inverted_index.broader | 250 |
| abstract_inverted_index.current | 55 |
| abstract_inverted_index.despite | 20 |
| abstract_inverted_index.distort | 24 |
| abstract_inverted_index.effects | 66, 99, 125, 178 |
| abstract_inverted_index.frailty | 134 |
| abstract_inverted_index.housing | 8 |
| abstract_inverted_index.inflate | 28 |
| abstract_inverted_index.leading | 33 |
| abstract_inverted_index.models, | 135 |
| abstract_inverted_index.nesting | 65, 124, 162, 177, 206, 227 |
| abstract_inverted_index.reduces | 40 |
| abstract_inverted_index.remains | 197 |
| abstract_inverted_index.results | 73, 165 |
| abstract_inverted_index.studies | 259 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.analyses | 69, 156 |
| abstract_inverted_index.compared | 189 |
| abstract_inverted_index.designs. | 263 |
| abstract_inverted_index.effects, | 2, 131 |
| abstract_inverted_index.effects. | 84 |
| abstract_inverted_index.examines | 57 |
| abstract_inverted_index.findings | 218 |
| abstract_inverted_index.indicate | 170 |
| abstract_inverted_index.inflated | 212 |
| abstract_inverted_index.lifespan | 18, 68, 103, 155, 258 |
| abstract_inverted_index.modeling | 115 |
| abstract_inverted_index.nesting. | 149 |
| abstract_inverted_index.practice | 199 |
| abstract_inverted_index.properly | 172, 201 |
| abstract_inverted_index.research | 230 |
| abstract_inverted_index.studies, | 19 |
| abstract_inverted_index.variance | 25 |
| abstract_inverted_index.addressed | 127 |
| abstract_inverted_index.adjusting | 173 |
| abstract_inverted_index.comparing | 71 |
| abstract_inverted_index.compounds | 101 |
| abstract_inverted_index.different | 114, 145 |
| abstract_inverted_index.estimates | 26, 119 |
| abstract_inverted_index.findings. | 49 |
| abstract_inverted_index.highlight | 219 |
| abstract_inverted_index.improving | 253 |
| abstract_inverted_index.influence | 117 |
| abstract_inverted_index.oversight | 39 |
| abstract_inverted_index.potential | 22, 210 |
| abstract_inverted_index.practical | 241 |
| abstract_inverted_index.undermine | 45 |
| abstract_inverted_index.Clustering | 1, 122 |
| abstract_inverted_index.accounting | 61, 223 |
| abstract_inverted_index.approaches | 116 |
| abstract_inverted_index.cage-level | 142 |
| abstract_inverted_index.clustering | 63, 160, 175, 204, 225, 244 |
| abstract_inverted_index.evaluating | 97 |
| abstract_inverted_index.explicitly | 139 |
| abstract_inverted_index.illustrate | 112 |
| abstract_inverted_index.importance | 221 |
| abstract_inverted_index.inference. | 237 |
| abstract_inverted_index.initiative | 96 |
| abstract_inverted_index.introduced | 6 |
| abstract_inverted_index.misleading | 35 |
| abstract_inverted_index.overlooked | 15 |
| abstract_inverted_index.unadjusted | 154, 191 |
| abstract_inverted_index.Comparisons | 150 |
| abstract_inverted_index.application | 242 |
| abstract_inverted_index.approaches, | 193 |
| abstract_inverted_index.conclusions | 182 |
| abstract_inverted_index.large-scale | 95 |
| abstract_inverted_index.preclinical | 17, 229 |
| abstract_inverted_index.reliability | 47 |
| abstract_inverted_index.statistical | 41, 75, 118, 185, 236 |
| abstract_inverted_index.underscores | 248 |
| abstract_inverted_index.adjustments, | 245 |
| abstract_inverted_index.adjustments. | 163 |
| abstract_inverted_index.conclusions. | 36, 121 |
| abstract_inverted_index.dependencies | 143 |
| abstract_inverted_index.experimental | 262 |
| abstract_inverted_index.implications | 251 |
| abstract_inverted_index.significance | 186 |
| abstract_inverted_index.Interventions | 90 |
| abstract_inverted_index.demonstrating | 239 |
| abstract_inverted_index.incorporating | 159 |
| abstract_inverted_index.methodological | 38 |
| abstract_inverted_index.reproducibility | 254 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| corresponding_author_ids | https://openalex.org/A5082540556 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 10 |
| corresponding_institution_ids | https://openalex.org/I4210119109 |
| citation_normalized_percentile.value | 0.89859971 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |