Evidence map›Paper›PMID 27423688›Full record

ArticleTrials2016

No improvement in the reporting of clinical trial subgroup effects in high-impact general medical journals.

Nicole B Gabler, Naihua Duan, Eli Raneses, Leah Suttner, Michael Ciarametaro, Elizabeth Cooney, Robert W Dubois, Scott D Halpern, Richard L Kravitz

Abstract read
In one paragraph

Article in Trials, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 4 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

13 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. The credibility of subgroup analyses reported in stroke trials is low: A systematic review.International journal of stroke : official journal of the International Stroke Society · 2023
    Pooled it
  2. Pooled it
  3. Pooled it
  4. Multimorbidity in randomized controlled trials of behavioral interventions: A systematic review.Health psychology : official journal of the Division of Health Psychology, American Psychological Association · 2019
    Pooled it
  5. Article
  6. Review
  7. A Machine-Learning Approach for Estimating Subgroup- and Individual-Level Treatment Effects: An Illustration Using the 65 Trial.Medical decision making : an international journal of the Society for Medical Decision Making · 2022
    Article
  8. Review
  9. Review
  10. Development of the Instrument to assess the Credibility of Effect Modification Analyses (ICEMAN) in randomized controlled trials and meta-analyses.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2020
    Article
  11. Article
  12. Article
  13. Article
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

9 authors.

Nicole B GablerCenter for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, 708 Blockley Hall, Philadelphia, PA, 19104, USA. gabler@upenn.edu.
Naihua DuanDepartment of Psychiatry and New York Psychiatric Institute, Columbia University, New York, NY, USA.
Eli RanesesCenter for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, 708 Blockley Hall, Philadelphia, PA, 19104, USA.
Leah SuttnerCenter for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, 708 Blockley Hall, Philadelphia, PA, 19104, USA.
Michael CiarametaroNational Pharmaceutical Council, Washington, DC, USA.
Elizabeth CooneyCenter for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, 708 Blockley Hall, Philadelphia, PA, 19104, USA.
Robert W DuboisNational Pharmaceutical Council, Washington, DC, USA.
Scott D HalpernCenter for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, 708 Blockley Hall, Philadelphia, PA, 19104, USA.
Richard L KravitzDepartment of Internal Medicine, Division of General Medicine, University of California Davis School of Medicine, Sacramento, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWhen subgroup analyses are not correctly analyzed and reported, incorrect conclusions may be drawn, and inappropriate treatments provided. Despite the increased recognition of the importance of subgroup analysis, little information exists regarding the prevalence, appropriateness, and study characteristics that influence subgroup analysis. The objective of this study is to determine (1) if the use of subgroup analyses and multivariable risk indices has increased, (2) whether statistical methodology has improved over time, and (3) which study characteristics predict subgroup analysis.

methodsWe randomly selected randomized controlled trials (RCTs) from five high-impact general medical journals during three time periods. Data from these articles were abstracted in duplicate using standard forms and a standard protocol. Subgroup analysis was defined as reporting any subgroup effect. Appropriate methods for subgroup analysis included a formal test for heterogeneity or interaction across treatment-by-covariate groups. We used logistic regression to determine the variables significantly associated with any subgroup analysis or, among RCTs reporting subgroup analyses, using appropriate methodology.

resultsThe final sample of 416 articles reported 437 RCTs, of which 270 (62 %) reported subgroup analysis. Among these, 185 (69 %) used appropriate methods to conduct such analyses. Subgroup analysis was reported in 62, 55, and 67 % of the articles from 2007, 2010, and 2013, respectively. The percentage using appropriate methods decreased over the three time points from 77 % in 2007 to 63 % in 2013 (p < 0.05). Significant predictors of reporting subgroup analysis included industry funding (OR 1.94 (95 % CI 1.17, 3.21)), sample size (OR 1.98 per quintile (1.64, 2.40), and a significant primary outcome (OR 0.55 (0.33, 0.92)). The use of appropriate methods to conduct subgroup analysis decreased by year (OR 0.88 (0.76, 1.00)) and was less common with industry funding (OR 0.35 (0.18, 0.70)). Only 33 (18 %) of the RCTs examined subgroup effects using a multivariable risk index.

conclusionsWhile we found no significant increase in the reporting of subgroup analysis over time, our results show a significant decrease in the reporting of subgroup analyses using appropriate methods during recent years. Industry-sponsored trials may more commonly report subgroup analyses, but without utilizing appropriate methods. Suboptimal reporting of subgroup effects may impact optimal physician-patient decision-making.

Indexed as

Journal Impact FactorModels, StatisticalBibliometricsData Interpretation, StatisticalHumansLogistic ModelsMultivariate AnalysisOdds RatioPeriodicals as TopicRandomized Controlled Trials as TopicResearch DesignHeterogeneity of treatment effectsMethodologyMultivariable risk indexRandomized controlled trialSubgroup analysis

Identifiers

PMID27423688
PMCPMC4947338

What OpenQuestion holds

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LicenceCC BY
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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.