Evidence map›Paper›PMID 39487397›Full record

ArticleBMC medical research methodology2024

Variability of relative treatment effect among populations with low, moderate and high control group event rates: a meta-epidemiological study.

M Hassan Murad, Zhen Wang, Mengli Xiao, Haitao Chu, Lifeng Lin

Abstract read
In one paragraph

Article in BMC medical research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

M Hassan MuradEvidence-Based Practice Center, Kern Center for the Science of Healthcare Delivery, Mayo Clinic, 200 First ST SW, Rochester, MN, 55905, USA. Murad.mohammad@mayo.edu.
Zhen WangEvidence-Based Practice Center, Kern Center for the Science of Healthcare Delivery, Mayo Clinic, 200 First ST SW, Rochester, MN, 55905, USA.
Mengli XiaoDepartment of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Haitao ChuStatistical Research and Data Science Center, Pfizer Inc, New York, NY, USA.
Lifeng LinDepartment of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe current practice in guideline development is to use the control group event rate (CR) as a surrogate of baseline risk and to assume portability of the relative treatment effect across populations with low, moderate and high baseline risk. We sought to emulate this practice in a very large sample of meta-analyses.

methodsWe retrieved data from all meta-analyses published in the Cochrane Database of Systematic Reviews (2003-2020) that evaluated a binary outcome, reported 2 × 2 data for each individual study and included at least 4 studies. We excluded studies with no events. We conducted meta-analyses with odds ratios and relative risks and performed subgroup analyses based on tertiles of CR. In sensitivity analyses, we evaluated the use of total event rate (TR) instead of CR and using quartiles instead of tertiles.

resultsThe analysis included 2,531 systematic reviews (27,692 meta-analyses, 226,975 studies, 25,669,783 patients).The percentages of meta-analyses with statistically significant interaction (P < 0.05) based on CR tertile or quartile ranged 12-18% across various sensitivity analyses. This percentage increased as the number of studies or range of CR per meta-analysis increased, reflecting increased power of the subgroup test. The percentages of meta-analyses with statistically significant interaction (P < 0.05) with TR quantiles were lower than those with CR but remained higher than expected by chance.

conclusionThis analysis suggests that when CR or TR are used as surrogates for baseline risk, relative treatment effects may not be portable across populations with varying baseline risks in many meta-analyses. Categroization of the continuous CR variable and not addressing measurement error limit inferences from such analyses and imply that CR is an undesirable source for baseline risk. Guideline developers and decision-makers should be provided with relative and absolute treatment effects that are conditioned on the baseline risk or derived from studies with similar baseline risk to their target populations.

Indexed as

Epidemiologic StudiesControl GroupsHumansMeta-Analysis as TopicOdds RatioSystematic Reviews as TopicTreatment OutcomeBaseline riskHeterogeneityMathematical couplingMeasurement errorMeta-analysisPortabilitySubgroup analysis

Identifiers

PMID39487397
PMCPMC11529075

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.