Measuring persistence to hormonal therapy in patients with breast cancer: accounting for temporary treatment discontinuation
Bibliographic record
Abstract
PURPOSE: Several studies have been conducted to estimate persistence to hormonal therapy among women with breast cancer (BC). Most studies focus on first treatment discontinuation. Patients, however, can have numerous periods of treatment discontinuation or treatment exposure. Our objective is to estimate persistence to tamoxifen in patients with BC while accounting for temporary treatment discontinuations and this by using multi-state (MS) models. METHODS: A cohort of 10,806 women with BC having received at least one prescription of tamoxifen between 1998 and 2008 was constituted from the UK General Practice Research Database. We fitted a semi-Markov model with three states to estimate the probability of being off treatment over a 5-year period while accounting for temporary treatment discontinuations (transition between on treatment and off treatment) and competing risks (recurrence of BC or death). RESULTS: Non-persistence, as estimated from the MS model, ranged from 12.1% (95% confidence interval [95%CI]: 9.2-15.1) at 1 year to 14.9% (95%CI: 11.7-18.1) at 5 years. Estimations of non-persistence based on the Kaplan-Meier model were higher, i.e., 29.3% (95%CI: 28.1-30.6) at 5 years, as well as those obtained from a competing risk model, i.e., 24.0% (95%CI: 22.9-25.1). Most temporary discontinuations (94.7%) lasted less than 6 months. Temporary treatment discontinuations are frequent and should be accounted for when measuring adherence to treatment. MS models can provide a useful framework for this sort of analysis insofar as they help describe patients' complex behavior. This may help tailor interventions that improve persistence to hormonal therapy among women with BC.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".