Evidence map›Paper›PMID 30991455›Full record

ReviewResearch synthesis methods2020

Dealing with missing outcome data in meta-analysis.

Dimitris Mavridis, Ian R White

Open access · hybridAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Research synthesis methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 10 pooled it
4.8field-weighted citation impact, top 3% of its field
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

32 citing papers in PubMed, 10 syntheses or guidelines pooled it, 60 citations in OpenAlex.

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  6. The utility of coping through emotional approach: A meta-analysis.Health psychology : official journal of the Division of Health Psychology, American Psychological Association · 2024
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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

2 authors at 2 institutions in 3 countries.

Dimitris MavridisDepartment of Primary Education, School of Education, University of Ioannina, Ioannina, Greece.ORCID https://orcid.org/0000-0003-1041-4592
Ian R WhiteInstitute of Clinical Trials and Methodology, MRC Clinical Trials Unit, University College London, London.ORCID https://orcid.org/0000-0002-6718-7661
Délégation Paris 5 · FRMRC Clinical Trials Unit at UCL · GB

Funding

Horizon 2020 Framework Programme 754936Medical Research Council MC_UU_12023/21
6 · The paper itself

Abstract

Missing data result in less precise and possibly biased effect estimates in single studies. Bias arising from studies with incomplete outcome data is naturally propagated in a meta-analysis. Conventional analysis using only individuals with available data is adequate when the meta-analyst can be confident that the data are missing at random (MAR) in every study-that is, that the probability of missing data does not depend on unobserved variables, conditional on observed variables. Usually, such confidence is unjustified as participants may drop out due to lack of improvement or adverse effects. The MAR assumption cannot be tested, and a sensitivity analysis to assess how robust results are to reasonable deviations from the MAR assumption is important. Two methods may be used based on plausible alternative assumptions about the missing data. Firstly, the distribution of reasons for missing data may be used to impute the missing values. Secondly, the analyst may specify the magnitude and uncertainty of possible departures from the missing at random assumption, and these may be used to correct bias and reweight the studies. This is achieved by employing a pattern mixture model and describing how the outcome in the missing participants is related to the outcome in the completers. Ideally, this relationship is informed using expert opinion. The methods are illustrated in two examples with binary and continuous outcomes. We provide recommendations on what trial investigators and systematic reviewers should do to minimize the problem of missing outcome data in meta-analysis.

Indexed as

Data Interpretation, StatisticalMeta-Analysis as TopicBiasData CollectionDepressionHaloperidolHumansMirtazapineModels, StatisticalProbabilityRandomized Controlled Trials as TopicReproducibility of ResultsResearch DesignSchizophreniaTreatment OutcomeUncertaintyHaloperidolMirtazapineinformative missingness odds ratioinformative missingness parametermeta-analysismissing datamissing not at random

Identifiers

PMID30991455
PMCPMC7003862
OpenAlexW2938677524

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.