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Record W4388993586 · doi:10.1093/jncics/pkad100

Characteristics of post hoc subgroup analyses of oncology clinical trials: a systematic review

2023· review· en· W4388993586 on OpenAlexaff
Jawad Alrawabdeh, Marah Alzubi, Muntaser Al Zyoud, Nada Odeh, Yazan Hamadneh, Hira Mian, Ghulam Rehman Mohyuddin, Amar H. Kelkar, Aaron M. Goodman, Rajshekhar Chakraborty, David A. Russler‐Germain, Nikita Mehra, Diva Baggio, Edward R. Scheffer Cliff, Samer Al Hadidi

Bibliographic record

VenueJNCI Cancer Spectrum · 2023
Typereview
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsMcMaster University
FundersAmerican Society of Hematology
KeywordsMedicineSubgroup analysisClinical trialPost hocPost-hoc analysisInternal medicineOncologyGeneralizability theoryMEDLINEFamily medicineMeta-analysisPsychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.252
metaresearch head score (Gemma)0.619
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad)
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.922

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2520.619
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0120.035
Bibliometrics0.0180.017
Science and technology studies0.0010.002
Scholarly communication0.0050.007
Open science0.0030.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.885
GPT teacher head0.757
Teacher spread0.128 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designSystematic review
DomainMethods
GenreReview

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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