Characteristics of post hoc subgroup analyses of oncology clinical trials: a systematic review
Bibliographic record
Abstract
BACKGROUND: Subgroup analyses in clinical trials assess intervention effects on specific patient subgroups, ensuring generalizability. However, they are usually only able to generate hypotheses rather than definitive conclusions. This study examined the prevalence and characteristics of post hoc subgroup analysis in oncology. METHODS: We systematically reviewed published subgroup analyses from 2000 to 2022. We included articles presenting secondary, post hoc, or subgroup analyses of interventional clinical trials in oncology, cancer survivorship, or cancer screening, published separately from the original clinical trial publication. We collected cancer type, year of publication, where and how subgroup analyses were reported, and funding. RESULTS: Out of 16 487 screened publications, 1612 studies were included, primarily subgroup analyses of treatment trials for solid tumors (82%). Medical writers contributed to 31% of articles, and 58% of articles reported conflicts of interest. Subgroup analyses increased significantly over time, with 695 published between 2019 and 2022, compared to 384 from 2000 to 2014. Gastrointestinal tumors (25%) and lymphoid lineage tumors (39%) were the most frequently studied solid and hematological malignancies, respectively. Industry funding and reporting of conflicts of interest increased over time. Subgroup analyses often neglected to indicate their secondary nature in the title. Most authors were from high-income countries, most commonly North America (45%). CONCLUSIONS: This study demonstrates the rapidly growing use of post hoc subgroup analysis of oncology clinical trials, revealing that the majority are supported by pharmaceutical companies, and they frequently fail to indicate their secondary nature in the title. Given the known methodological limitations of subgroup analyses, caution is recommended among authors, readers, and reviewers when conducting and interpreting these studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.252 | 0.619 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.035 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".