Evidence mapPaperPMID 39102176Full record

ArticleDrug safety2024

Examining the Effect of Missing Data and Unmeasured Confounding on External Comparator Studies: Case Studies and Simulations.

Gerd Rippin, Héctor Sanz, Wilhelmina E Hoogendoorn, Nicolás M Ballarini, Joan A Largent, Eleni Demas, Douwe Postmus, Theodor Framke, Lukas M Aguirre Dávila, Chantal Quinten and 1 more

Abstract read
In one paragraph

Article in Drug safety, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 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

11 authors.

Gerd RippinIQVIA, Unterschweinstiege 2-14, 60549, Frankfurt, Germany. gerd.rippin@iqvia.com.ORCID http://orcid.org/0000-0001-6293-8465
Héctor SanzIQVIA, Unterschweinstiege 2-14, 60549, Frankfurt, Germany.
Wilhelmina E HoogendoornIQVIA, Unterschweinstiege 2-14, 60549, Frankfurt, Germany.
Nicolás M BallariniIQVIA, Unterschweinstiege 2-14, 60549, Frankfurt, Germany.
Joan A LargentIQVIA, Unterschweinstiege 2-14, 60549, Frankfurt, Germany.
Eleni DemasIQVIA, Unterschweinstiege 2-14, 60549, Frankfurt, Germany.
Douwe PostmusEuropean Medicines Agency, Domenico Scarlattilaan 6, Amsterdam, 1083 HS, The Netherlands.
Theodor FramkeEuropean Medicines Agency, Domenico Scarlattilaan 6, Amsterdam, 1083 HS, The Netherlands.
Lukas M Aguirre DávilaPaul-Ehrlich-Institut, Paul-Ehrlich-Strasse 51-59, 63225, Langen, Germany.
Chantal QuintenEuropean Medicines Agency, Domenico Scarlattilaan 6, Amsterdam, 1083 HS, The Netherlands.
Francesco PignattiEuropean Medicines Agency, Domenico Scarlattilaan 6, Amsterdam, 1083 HS, The Netherlands.

Funding

European Medicines Agency EMA/2017/09/PEEuropean Medicines Agency Lot 4
6 · The paper itself

Abstract

BACKGROUND AND

objectiveMissing data and unmeasured confounding are key challenges for external comparator studies. This work evaluates bias and other performance characteristics depending on missingness and unmeasured confounding by means of two case studies and simulations.

methodsTwo case studies were constructed by taking the treatment arms from two randomised controlled trials and an external real-world data source that exhibited substantial missingness. The indications of the randomised controlled trials were multiple myeloma and metastatic hormone-sensitive prostate cancer. Overall survival was taken as the main endpoint. The effects of missing data and unmeasured confounding were assessed for the case studies by reporting estimated external comparator versus randomised controlled trial treatment effects. Based on the two case studies, simulations were performed broadening the settings by varying the underlying hazard ratio, the sample size, the sample size ratio between the experimental arm and the external comparator, the number of missing covariates and the percentage of missingness. Thereby, bias and other performance metrics could be quantified dependent on these factors.

resultsFor the multiple myeloma external comparator study, results were in line with the randomised controlled trial, despite missingness and potential unmeasured confounding, while for the metastatic hormone-sensitive prostate cancer case study missing data led to a low sample size, leading overall to inconclusive results. Furthermore, for the metastatic hormone-sensitive prostate cancer study, missing data in important eligibility criteria led to further limitations. Simulations were successfully applied to gain a quantitative understanding of the effects of missing data and unmeasured confounding.

conclusionsThis exploratory study confirmed external comparator strengths and limitations by quantifying the impact of missing data and unmeasured confounding using case studies and simulations. In particular, missing data in key eligibility criteria were seen to limit the ability to derive the external comparator target analysis population accurately, while simulations demonstrated the magnitude of bias to expect for various settings.

Indexed as

BiasComputer SimulationMultiple MyelomaProstatic NeoplasmsRandomized Controlled Trials as TopicConfounding Factors, EpidemiologicData Interpretation, StatisticalHumansMaleResearch DesignSample Size

Identifiers

PMID39102176
PMCPMC11554740

What Socratic holds

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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 Socratic graph.