Evidence map›Paper›PMID 33435879›Full record

ArticleBMC medical research methodology2021

When does the use of individual patient data in network meta-analysis make a difference? A simulation study.

Steve Kanters, Mohammad Ehsanul Karim, Kristian Thorlund, Aslam Anis, Nick Bansback

Abstract read
In one paragraph

Article in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

5 authors.

Steve KantersSchool of Population and Public Health, University of British Columbia, 2206 E Mall, Vancouver, British Columbia, Canada. skanters@raincity-analytics.com.ORCID 0000-0003-0322-5398
Mohammad Ehsanul KarimSchool of Population and Public Health, University of British Columbia, 2206 E Mall, Vancouver, British Columbia, Canada.
Kristian ThorlundDepartments of Health Research Methods, Evidence & Impact, McMaster University, Hamilton, Canada.
Aslam AnisSchool of Population and Public Health, University of British Columbia, 2206 E Mall, Vancouver, British Columbia, Canada.
Nick BansbackSchool of Population and Public Health, University of British Columbia, 2206 E Mall, Vancouver, British Columbia, Canada.

Funding

Canadian Institutes for Health Research
6 · The paper itself

Abstract

backgroundThe use of individual patient data (IPD) in network meta-analyses (NMA) is rapidly growing. This study aimed to determine, through simulations, the impact of select factors on the validity and precision of NMA estimates when combining IPD and aggregate data (AgD) relative to using AgD only.

methodsThree analysis strategies were compared via simulations: 1) AgD NMA without adjustments (AgD-NMA); 2) AgD NMA with meta-regression (AgD-NMA-MR); and 3) IPD-AgD NMA with meta-regression (IPD-NMA). We compared 108 parameter permutations: number of network nodes (3, 5 or 10); proportion of treatment comparisons informed by IPD (low, medium or high); equal size trials (2-armed with 200 patients per arm) or larger IPD trials (500 patients per arm); sparse or well-populated networks; and type of effect-modification (none, constant across treatment comparisons, or exchangeable). Data were generated over 200 simulations for each combination of parameters, each using linear regression with Normal distributions. To assess model performance and estimate validity, the mean squared error (MSE) and bias of treatment-effect and covariate estimates were collected. Standard errors (SE) and percentiles were used to compare estimate precision.

resultsOverall, IPD-NMA performed best in terms of validity and precision. The median MSE was lower in the IPD-NMA in 88 of 108 scenarios (similar results otherwise). On average, the IPD-NMA median MSE was 0.54 times the median using AgD-NMA-MR. Similarly, the SEs of the IPD-NMA treatment-effect estimates were 1/5 the size of AgD-NMA-MR SEs. The magnitude of superior validity and precision of using IPD-NMA varied across scenarios and was associated with the amount of IPD. Using IPD in small or sparse networks consistently led to improved validity and precision; however, in large/dense networks IPD tended to have negligible impact if too few IPD were included. Similar results also apply to the meta-regression coefficient estimates.

conclusionsOur simulation study suggests that the use of IPD in NMA will considerably improve the validity and precision of estimates of treatment effect and regression coefficients in the most NMA IPD data-scenarios. However, IPD may not add meaningful validity and precision to NMAs of large and dense treatment networks when negligible IPD are used.

Indexed as

Network Meta-Analysis as TopicResearch ReportBiasComputer SimulationHumansIndividual patient dataIPDMethodsNetwork meta-analysesNMASimulation study

Identifiers

PMID33435879
PMCPMC7805229

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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.