Evidence map›Paper›PMID 34283909›Full record

SynthesisKorean journal of anesthesiology2021

Explanation of trial sequential analysis: using a post-hoc analysis of meta-analyses published in Korean Journal of Anesthesiology.

Alessandro De Cassai, Martina Tassone, Federico Geraldini, Massimo Sergi, Nicolò Sella, Annalisa Boscolo, Marina Munari

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Korean journal of anesthesiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 10 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

17 citing papers in PubMed, 10 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. Pooled it
  10. Pooled it
  11. Review
  12. Review
  13. Review
  14. Review
  15. Review
  16. Article
  17. Improving Meta-analyses.Arquivos brasileiros de cardiologia · 2023
    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

7 authors.

Alessandro De CassaiUOC Anesthesia and Intensive Care Unit, University Hospital of Padua, Padua, Italy.
Martina TassoneUOC Anesthesia and Intensive Care Unit, Department of Medicine-DIMED, University of Padua, Padua, Italy.
Federico GeraldiniUOC Anesthesia and Intensive Care Unit, University Hospital of Padua, Padua, Italy.
Massimo SergiUOC Anesthesia and Intensive Care Unit, University Hospital of Padua, Padua, Italy.
Nicolò SellaUOC Anesthesia and Intensive Care Unit, University Hospital of Padua, Padua, Italy.
Annalisa BoscoloUOC Anesthesia and Intensive Care Unit, University Hospital of Padua, Padua, Italy.
Marina MunariUOC Anesthesia and Intensive Care Unit, University Hospital of Padua, Padua, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTrial sequential analysis (TSA) is a recent cumulative meta-analysis method used to weigh type I and II errors and to estimate when the effect is large enough to be unaffected by further studies. The aim of this study was to illustrate possible TSA scenarios and their significance using meta-analyses published in the Korean Journal of Anesthesiology as working material.

methodsWe performed a systematic medical literature search for meta-analyses published in the Korean Journal of Anesthesiology. TSA was performed on each main outcome, estimating the required sample size on the calculated effect size for the intervention, considering a type I error of 5% and a power of 90% or 99%.

resultsSix meta-analyses with a total of ten main outcomes were included in the analysis. Seven TSAs confirmed the results of the meta-analyses. However, only three of them reached the required sample size. In the two TSAs, the cumulative z-lines were not statistically significant. One TSA boundary for effect was reached with the 90% analysis, but not with the 99% analysis.

conclusionsIn TSA, a meta-analysis pooled effect may be established to assess if the cumulative sample size is large enough. TSA can be used to add strength to the conclusions of meta-analyses; however, pre-registration of the TSA protocol is of paramount importance. This study could be useful to better understand the use of TSA as an additional statistical tool to improve meta-analysis quality.

Indexed as

AnesthesiologyHumansRepublic of KoreaResearch DesignSample SizeAnesthesiaMedical educationMeta-analysisReviewSample sizeStatistics

Identifiers

PMID34283909
PMCPMC8497914

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.