Evidence map›Paper›PMID 33092521›Full record

SynthesisBMC medical research methodology2020

Estimation in meta-analyses of response ratios.

Ilyas Bakbergenuly, David C Hoaglin, Elena Kulinskaya

Abstract readMeta-Analysis
In one paragraph

Synthesis in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
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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

3 authors.

Ilyas BakbergenulySchool of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK.
David C HoaglinDepartment of Population and Quantitative Health Sciences, University of Massachusetts Medical School, 368 Plantation Street, Worcester, 01605, Massachusetts, USA.
Elena KulinskayaSchool of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK. e.kulinskaya@uea.ac.uk.ORCID 0000-0002-9843-1663

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFor outcomes that studies report as the means in the treatment and control groups, some medical applications and nearly half of meta-analyses in ecology express the effect as the ratio of means (RoM), also called the response ratio (RR), analyzed in the logarithmic scale as the log-response-ratio, LRR.

methodsIn random-effects meta-analysis of LRR, with normal and lognormal data, we studied the performance of estimators of the between-study variance, τ

resultsThe results of our extensive simulations showed several challenges in using LRR as an effect measure. Point estimators of τ

conclusionsFor lognormal data, we can recommend only SSW, a weighted average in which a study's weight is proportional to its effective sample size, (when n≥40) and its companion interval (when n≥10). Normal data posed greater challenges. When the means were far enough from 0 (more than one standard deviation, 4 in our simulations), SSW was practically unbiased, and its companion interval was the only option.

Indexed as

Sample SizeHumansBetween-study varianceHeterogeneityLog-response-ratioMeta-analysisRandom-effects modelRatio of means

Identifiers

PMID33092521
PMCPMC7579974

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.