Methodology for Treatment Evaluation in Patients With Cancer Metastatic to Bone
Bibliographic record
Abstract
BACKGROUND: Patients with cancer metastatic to bone experience several adverse and clinically important skeletal-related events, including pathologic fractures, vertebral compressions with fracture, the need for surgery to treat or prevent fractures, and the need for radiation therapy for the treatment of bone pain. We present appropriate methods for describing and modeling the clinical course of skeletal-related events and comparing treatments for such events. METHODS: On the basis of data from a recently completed randomized, placebo-controlled trial involving 380 breast cancer patients with bone metastases, we tested the validity of the "events-per-person-years" method, one of the most commonly used techniques, for the analysis of skeletal-related events. We then used more robust methods of analysis that are based on fewer assumptions, including a random-effects Poisson model, and contrasted the inferences about skeletal-related event rates and treatment effects for the different analytic methods. All statistical tests were two-sided. RESULTS: The events-per-person-years analysis underestimated substantially the variation in the data and is not appropriate to summarize the incidence rate of skeletal-related events. A random-effects Poisson model did provide a valid basis for analyzing such data. CONCLUSIONS: The underestimation of variability in data associated with the use of the events-per-person-years analysis leads to unduly narrow confidence intervals for complication rates and inflated false-positive error rates in treatment comparisons. A random-effects Poisson model provides a valid, robust basis for describing the clinical course of bone complications and evaluating treatment effects.
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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 | 0.179 | 0.417 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".