Statistical fundamentals on cancer research for clinicians: Working with your statisticians
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: To facilitate understanding statistical principles and methods for clinicians involved in cancer research. METHODS: An overview of study design is provided on cancer research for both observational and clinical trials addressing study objectives and endpoints, superiority tests, non-inferiority and equivalence design, and sample size calculation. The principles of statistical models and tests including contemporary standard methods of analysis and evaluation are discussed. Finally, some statistical pitfalls frequently evident in clinical and translational studies in cancer are discussed. RESULTS: We emphasize the practical aspects of study design (superiority vs non-inferiority vs equivalence study) and assumptions underpinning power calculations and sample size estimation. The differences between relative risk, odds ratio, and hazard ratio, understanding outcome endpoints, purposes of interim analysis, and statistical modeling to minimize confounding effects and bias are also discussed. CONCLUSION: Proper design and correctly constructed statistical models are critical for the success of cancer research studies. Most statistical inaccuracies can be minimized by following essential statistical principles and guidelines to improve quality in research 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it