Evidence map›Paper›PMID 37490207›Full record

SynthesisPharmacoEconomics2023

Handling Missing Data in Health Economics and Outcomes Research (HEOR): A Systematic Review and Practical Recommendations.

Kumar Mukherjee, Necdet B Gunsoy, Rita M Kristy, Joseph C Cappelleri, Jessica Roydhouse, Judith J Stephenson, David J Vanness, Sujith Ramachandran, Nneka C Onwudiwe, Sri Ram Pentakota and 2 more

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in PharmacoEconomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 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

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

12 authors.

Kumar MukherjeePhiladelphia College of Osteopathic Medicine, Suwanee, GA, USA.
Necdet B GunsoyAbbvie Ltd., Vanwall Business Park, Maidenhead, UK.
Rita M KristyAstellas Pharma, Northbrook, IL, USA.
Joseph C CappelleriPfizer Inc., Groton, CT, USA.
Jessica RoydhouseMenzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia.
Judith J StephensonCarelon Research, Wilmington, DE, USA.
David J VannessPennsylvania State University, University Park, PA, USA.
Sujith RamachandranUniversity of Mississippi, University, MS, USA.
Nneka C OnwudiwePharmaceutical Economics Consultants of America, Silver Spring, MD, USA.
Sri Ram PentakotaRutgers New Jersey Medical School, Jersey City, NJ, USA.
Helene KarcherNovartis Pharma AG, Basel, Switzerland.
Gian Luca Di TannaDepartment of Business Economics, Health and Social Care, University of Applied Sciences and Arts of Southern Switzerland, Stabile Piazzetta, Via Violino 11, 6928, Manno, Switzerland. gianluca.ditanna@supsi.ch.ORCID 0000-0002-5470-3567

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMissing data in costs and/or health outcomes and in confounding variables can create bias in the inference of health economics and outcomes research studies, which in turn can lead to inappropriate policies. Most of the literature focuses on handling missing data in randomized controlled trials, which are not necessarily always the data used in health economics and outcomes research.

objectivesWe aimed to provide an overview on missing data issues and how to address incomplete data and report the findings of a systematic literature review of methods used to deal with missing data in health economics and outcomes research studies that focused on cost, utility, and patient-reported outcomes.

methodsA systematic search of papers published in English language until the end of the year 2020 was carried out in PubMed. Studies using statistical methods to handle missing data for analyses of cost, utility, or patient-reported outcome data were included, as were reviews and guidance papers on handling missing data for those outcomes. The data extraction was conducted with a focus on the context of the study, the type of missing data, and the methods used to tackle missing data.

resultsFrom 1433 identified records, 40 papers were included. Thirteen studies were economic evaluations. Thirty studies used multiple imputation with 17 studies using multiple imputation by chained equation, while 15 studies used a complete-case analysis. Seventeen studies addressed missing cost data and 23 studies dealt with missing outcome data. Eleven studies reported a single method while 20 studies used multiple methods to address missing data.

conclusionsSeveral health economics and outcomes research studies did not offer a justification of their approach of handling missing data and some used only a single method without a sensitivity analysis. This systematic literature review highlights the importance of considering the missingness mechanism and including sensitivity analyses when planning, analyzing, and reporting health economics and outcomes research studies.

Indexed as

Outcome Assessment, Health CareResearch DesignBiasCost-Benefit AnalysisData Interpretation, StatisticalHumans

Identifiers

PMID37490207
PMCPMC10635950

What OpenQuestion holds

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LicenceCC BY-NC
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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.