Characteristics and interpretation of subgroup analyses based on tumour characteristics in randomised trials testing target-specific anticancer drugs: design of a systematic survey
Bibliographic record
Abstract
BACKGROUND: Target-specific anticancer drugs are under rapid development. Little is known, however, about the risk of administering target-specific drugs to patients who have tumours with molecular alterations or other characteristics that can make the drug ineffective or even harmful. An increasing number of randomised clinical trials (RCTs) investigating target-specific anticancer drugs include subgroup analyses based on tumour characteristics. Such subgroup analyses have the potential to be more credible and influential than subgroup analyses based on traditional factors such as sex or tumour stage. In addition, they may more frequently lead to qualitative subgroup effects, that is, show benefit in one but harm in another subgroup of patients (eg, if the tumour characteristic makes the drug ineffective or even enhance tumour growth). If so, subgroup analyses based on tumour characteristics would be highly relevant for patient safety. The aim of this study is to systematically assess the frequency and characteristics of subgroup analyses based on tumour characteristics, the frequency of qualitative subgroup effects, their credibility, and the interpretations that investigators and guidelines developers report. METHODS AND ANALYSIS: We will perform a systematic survey of 433 RCTs testing the effect of target-specific anticancer drugs. Teams of methodologically trained investigators and oncologists will identify eligible studies, extract relevant data and assess the credibility of putative subgroup effects using a recently developed formal instrument. We will systematically assess how trial investigators interpret apparent subgroup effects based on tumour characteristics and the extent to which they influence subsequent practice guidelines. Our results will provide empirical data characterising an increasingly used type of subgroup analysis in cancer trials and its potential impact on precision medicine to predict benefit or harm. ETHICS AND DISSEMINATION: Formal ethical approval is not required for this study. We will disseminate the findings in a peer-reviewed and open-access journal publication.
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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 | 0.588 | 0.749 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.022 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| 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".