Evidence map›Paper›PMID 38318993›Full record

ArticleStatistics in medicine2024

Sensitivity analysis with iterative outlier detection for systematic reviews and meta-analyses.

Zhuo Meng, Jingshen Wang, Lifeng Lin, Chong Wu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistics in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 20 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 20 pooled it
–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

42 citing papers in PubMed, 20 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Pooled it
  9. Global prevalence of fluoroquinolone resistance inFrontiers in cellular and infection microbiology · 2026
    Pooled it
  10. Pooled it
  11. Pooled it
  12. Pooled it
  13. Pooled it
  14. Pooled it
  15. Pooled it
  16. Pooled it
  17. Pooled it
  18. Pooled it
  19. Pooled it
  20. Pooled it
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

4 authors.

Zhuo MengDepartment of Statistics, College of Arts and Sciences, Florida State University, Tallahassee, Florida, USA.ORCID 0009-0007-4926-2270
Jingshen WangDivision of Biostatistics, School of Public Health, University of California, Berkeley, California, USA.
Lifeng LinDepartment of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, The University of Arizona, Tucson, Arizona, USA.ORCID 0000-0002-3562-9816
Chong WuDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.ORCID 0000-0002-8400-1785

Funding

Uncovering causal protein markers to improve prostate cancer etiology understanding and risk prediction in Africans and EuropeansR01CA263494 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI Chong Wu, Lang Wu · 2022 to 2026
$3.5M
Statistical Methods and Software for Multivariate Meta-analysisR01LM012982 · NLM · UNIVERSITY OF MINNESOTA · PI LIN, LIFENG, SIEGEL, LIANNE · 2019 to 2022
$1.3M
Novel Statistical Methods for Multi-omics Data Integration in Alzheimer's DiseaseR03AG070669 · NIA · FLORIDA STATE UNIVERSITY · PI BRADLEY, JONATHAN RAY, WU, CHONG · 2021 to 2022
$296k
Joint modeling of continuous and binary data in meta-analysisR03MH128727 · NIMH · UNIVERSITY OF ARIZONA · PI LIN, LIFENG · 2022 to 2023
$146k
NCI NIH HHS R01 CA263494NIA NIH HHS R03 AG070669NIMH NIH HHS R03 MH128727NLM NIH HHS R01 LM012982
6 · The paper itself

Abstract

Meta-analysis is a widely used tool for synthesizing results from multiple studies. The collected studies are deemed heterogeneous when they do not share a common underlying effect size; thus, the factors attributable to the heterogeneity need to be carefully considered. A critical problem in meta-analyses and systematic reviews is that outlying studies are frequently included, which can lead to invalid conclusions and affect the robustness of decision-making. Outliers may be caused by several factors such as study selection criteria, low study quality, small-study effects, and so on. Although outlier detection is well-studied in the statistical community, limited attention has been paid to meta-analysis. The conventional outlier detection method in meta-analysis is based on a leave-one-study-out procedure. However, when calculating a potentially outlying study's deviation, other outliers could substantially impact its result. This article proposes an iterative method to detect potential outliers, which reduces such an impact that could confound the detection. Furthermore, we adopt bagging to provide valid inference for sensitivity analyses of excluding outliers. Based on simulation studies, the proposed iterative method yields smaller bias and heterogeneity after performing a sensitivity analysis to remove the identified outliers. It also provides higher accuracy on outlier detection. Two case studies are used to illustrate the proposed method's real-world performance.

Indexed as

Meta-Analysis as TopicSystematic Reviews as TopicBiasComputer SimulationHumansheterogeneityiterative methodmeta-analysisoutliersensitivity analysis

Identifiers

PMID38318993
PMCPMC10947935

What OpenQuestion holds

Textmetadata
LicenceTDM
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.