ReviewPharmaceutical statistics2020
A critical review of graphics for subgroup analyses in clinical trials.
Review in Pharmaceutical statistics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled 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.
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.
Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Meta-Analysis of Whole Blood Cholinesterase Activity in Healthy Ruminants Using a Modified Electrometric Measurement Method.Acta medica academica · 2026Pooled it
- Investigating treatment-effect modification by a continuous covariate in IPD meta-analysis: an approach using fractional polynomials.BMC medical research methodology · 2022Pooled it
- Alitretinoin versus phototherapy as the first-line treatment in adults with severe chronic hand eczema: the ALPHA RCT.Health technology assessment (Winchester, England) · 2024Trial
- Cell Therapy Improves Quality-of-Life in Heart Failure: Outcomes From a Phase III Clinical Trial.Stem cells translational medicine · 2024Trial
- High Parathyroid Hormone Rather than Low Vitamin D Is Associated with Reduced Event-Free Survival in Childhood Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2024Article
- Outcomes following poor-grade aneurysmal subarachnoid haemorrhage: a prospective observational study.Acta neurochirurgica · 2023Observational
- Effect of the Diabetic Nephropathy Aggravation Prevention Program on medical visit behavior in individuals under the municipal national health insurance.Journal of diabetes investigation · 2023Article
- Heterogeneous treatment effects in social policy studies: An assessment of contemporary articles in the health and social sciences.Annals of epidemiology · 2022Article
- Machine learning for tumor growth inhibition: Interpretable predictive models for transparency and reproducibility.CPT: pharmacometrics & systems pharmacology · 2022Article
- A novel concept of screening for subgrouping factors for the association between socioeconomic status and respiratory allergies.Journal of exposure science & environmental epidemiology · 2022Review
- Gut Microbiota Alteration Is Associated With Cognitive Deficits in Genetically Diabetic (Db/db) Mice During Aging.Frontiers in aging neuroscience · 2021Article
- Engagement With a Behavior Change App for Alcohol Reduction: Data Visualization for Longitudinal Observational Study.Journal of medical Internet research · 2020Observational
- A critical review of graphics for subgroup analyses in clinical trials.Pharmaceutical statistics · 2020Review
- Bayesian Solutions for Assessing Differential Effects in Biomarker Positive and Negative Subgroups.Pharmaceutical statisticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Subgroup analyses are a routine part of clinical trials to investigate whether treatment effects are homogeneous across the study population. Graphical approaches play a key role in subgroup analyses to visualise effect sizes of subgroups, to aid the identification of groups that respond differentially, and to communicate the results to a wider audience. Many existing approaches do not capture the core information and are prone to lead to a misinterpretation of the subgroup effects. In this work, we critically appraise existing visualisation techniques, propose useful extensions to increase their utility and attempt to develop an effective visualisation approach. We focus on forest plots, UpSet plots, Galbraith plots, subpopulation treatment effect pattern plot, and contour plots, and comment on other approaches whose utility is more limited. We illustrate the methods using data from a prostate cancer study.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.