Evidence map›Paper›PMID 38354868›Full record

SynthesisJournal of clinical epidemiology2024

Grilling the data: application of specification curve analysis to red meat and all-cause mortality.

Yumin Wang, Tyler Pitre, Joshua D Wallach, Russell J de Souza, Tanvir Jassal, Dennis Bier, Chirag J Patel, Dena Zeraatkar

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Journal of clinical epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

8 authors.

Yumin WangDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Tyler PitreDepartment of Medicine, McMaster University, Hamilton, Ontario, Canada.
Joshua D WallachDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Russell J de SouzaDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
Tanvir JassalDepartment of Anesthesia, McMaster University, Hamilton, Ontario, Canada.
Dennis BierDepartment of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
Chirag J PatelDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Dena ZeraatkarDepartment of Anesthesia, McMaster University, Hamilton, Ontario, Canada; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada. Electronic address: zeraatd@mcmaster.ca.

Funding

Data science tools to identify robust exposure-phenotype associations for precision medicineR01ES032470 · NIEHS · HARVARD MEDICAL SCHOOL · PI MANRAI, ARJUN KUMAR, PATEL, CHIRAG J. · 2021 to 2025
$3.5M
Identifying Patient Subgroups That Are Most Likely To Benefit From Medications Used To Treat Alcohol Use DisorderK01AA028258 · NIAAA · YALE UNIVERSITY · PI WALLACH, JOSHUA DAVID · 2021 to 2025
$892k
NIAAA NIH HHS K01 AA028258NIEHS NIH HHS R01 ES032470
6 · The paper itself

Abstract

objectivesTo present an application of specification curve analysis-a novel analytic method that involves defining and implementing all plausible and valid analytic approaches for addressing a research question-to nutritional epidemiology. STUDY DESIGN AND

settingWe reviewed all observational studies addressing the effect of red meat on all-cause mortality, sourced from a published systematic review, and documented variations in analytic methods (eg, choice of model, covariates, etc.). We enumerated all defensible combinations of analytic choices to produce a comprehensive list of all the ways in which the data may reasonably be analyzed. We applied specification curve analysis to data from National Health and Nutrition Examination Survey 2007 to 2014 to investigate the effect of unprocessed red meat on all-cause mortality. The specification curve analysis used a random sample of all reasonable analytic specifications we sourced from primary studies.

resultsAmong 15 publications reporting on 24 cohorts included in the systematic review on red meat and all-cause mortality, we identified 70 unique analytic methods, each including different analytic models, covariates, and operationalizations of red meat (eg, continuous vs quantiles). We applied specification curve analysis to National Health and Nutrition Examination Survey, including 10,661 participants. Our specification curve analysis included 1208 unique analytic specifications, of which 435 (36.0%) yielded a hazard ratio equal to or more than 1 for the effect of red meat on all-cause mortality and 773 (64.0%) less than 1. The specification curve analysis yielded a median hazard ratio of 0.94 (interquartile range: 0.83-1.05). Forty-eight specifications (3.97%) were statistically significant, 40 of which indicated unprocessed red meat to reduce all-cause mortality and eight of which indicated red meat to increase mortality.

conclusionWe show that the application of specification curve analysis to nutritional epidemiology is feasible and presents an innovative solution to analytic flexibility.

Indexed as

Nutrition SurveysRed MeatCause of DeathData Interpretation, StatisticalFemaleHumansMaleMortalityObservational Studies as TopicAll-cause mortalityMultiverse analysisNutritionRed meatSpecification curve analysisVibration of effects

Identifiers

PMID38354868
PMCPMC12289182

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.