Related Experiment Video
Updated: Jun 29, 2025
Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
Published on: July 25, 2020
Differential Treatment Effects of Subgroup Analyses in Phase 3 Oncology Trials From 2004 to 2020
Alexander D Sherry1, Andrew W Hahn2, Zachary R McCaw3,4
1Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston.
Importance:
Subgroup analyses are often performed in oncology to investigate differential treatment effects and may even constitute the basis for regulatory approvals. Current understanding of the features, results, and quality of subgroup analyses is limited.
Objective:
To evaluate forest plot interpretability and credibility of differential treatment effect claims among oncology trials.
Design, Setting, And Participants:
This cross-sectional study included randomized phase 3 clinical oncology trials published prior to 2021. Trials were screened from ClinicalTrials.gov.
Main Outcomes And Measures:
Missing visual elements in forest plots were defined as a missing point estimate or use of a linear x-axis scale for hazard and odds ratios. Multiplicity of testing control was recorded. Differential treatment effect claims were rated using the Instrument for Assessing the Credibility of Effect Modification Analyses. Linear and logistic regressions evaluated associations with outcomes.
Results:
Among 785 trials, 379 studies (48%) enrolling 331 653 patients reported a subgroup analysis. The forest plots of 43% of trials (156 of 363) were missing visual elements impeding interpretability. While 4148 subgroup effects were evaluated, only 1 trial (0.3%) controlled for multiple testing. On average, trials that did not meet the primary end point conducted 2 more subgroup effect tests compared with trials meeting the primary end point (95% CI, 0.59-3.43 tests; P = .006). A total of 101 differential treatment effects were claimed across 15% of trials (55 of 379). Interaction testing was missing in 53% of trials (29 of 55) claiming differential treatment effects. Trials not meeting the primary end point were associated with greater odds of no interaction testing (odds ratio, 4.47; 95% CI, 1.42-15.55, P = .01). The credibility of differential treatment effect claims was rated as low or very low in 93% of cases (94 of 101).
Conclusions And Relevance:
In this cross-sectional study of phase 3 oncology trials, nearly half of trials presented a subgroup analysis in their primary publication. However, forest plots of these subgroup analyses largely lacked essential features for interpretation, and most differential treatment effect claims were not supported. Oncology subgroup analyses should be interpreted with caution, and improvements to the quality of subgroup analyses are needed.
Insights
Subgroup analyses in oncology trials often lack interpretable forest plots and credible claims of differential treatment effects. Most claims were low quality, necessitating caution and improved methods for subgroup analysis reporting.
Area of Science:
- Oncology
- Clinical Trials
- Biostatistics
Background:
- Subgroup analyses are crucial in oncology for assessing differential treatment effects and informing regulatory decisions.
- However, the quality, interpretability, and credibility of these analyses in published oncology trials are not well understood.
- Limited understanding necessitates an evaluation of current practices in reporting subgroup analyses.
Purpose of the Study:
- To evaluate the interpretability and credibility of forest plots used in oncology clinical trials.
- To assess the validity of claims regarding differential treatment effects derived from subgroup analyses in oncology.
Main Methods:
- A cross-sectional study analyzed randomized phase 3 oncology trials published before 2021, screened via ClinicalTrials.gov.
- Forest plot interpretability was assessed by the presence of point estimates and appropriate x-axis scales.
- Credibility of differential treatment effect claims was rated using a standardized instrument, with multiplicity testing and interaction testing also recorded.
Main Results:
- Of 785 trials, 379 (48%) reported subgroup analyses; 43% of their forest plots lacked essential visual elements.
- Only 1 trial (0.3%) controlled for multiple testing among 4148 subgroup effects evaluated.
- 93% of differential treatment effect claims (101 total) were rated as low or very low credibility, with 53% missing interaction testing.
Conclusions:
- Nearly half of phase 3 oncology trials include subgroup analyses, but reporting quality is often poor, hindering interpretation.
- Most claims of differential treatment effects lack sufficient evidence and credibility, particularly in trials not meeting primary endpoints.
- There is a critical need for improved methodology and reporting standards for subgroup analyses in oncology research.
More Related Videos
Related Concept Videos
01:20Comparing the Survival Analysis of Two or More Groups
01:21Cancer Survival Analysis
01:12Hazard Ratio
For example, in a clinical trial...
02:49Cancer Therapies
However, cancer treatments can pose several challenges, as therapies used to kill cancer cells are generally also toxic to normal cells. Moreover, cancer cells mutate rapidly and can develop resistance to chemical agents or radiation therapy. Besides, all types of cancer cells may not respond to the same therapy. Some cancer cells respond to one...
02:57Targeted Cancer Therapies
There are several types of targeted therapies against...
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...