Medication Adherence, Treatment Patterns, and Dose Reduction in Patients with Metastatic Castration-Resistant Prostate Cancer Receiving Abiraterone Acetate or Enzalutamide.
Bibliographic record
Abstract
BACKGROUND: The efficacy of and overall survival associated with metastatic castration-resistant prostate cancer (CRPC) treatments rely on patients' consistent adherence to the recommended dosage regimens. OBJECTIVES: To evaluate treatment patterns and patient adherence to abiraterone acetate or enzalutamide therapy in real-world practice, and to examine the factors that may be associated with medication dose reduction in patients with metastatic CRPC. METHODS: Retrospective analyses were conducted using the Truven Health MarketScan research databases among patients with metastatic CRPC who initiated treatment with abiraterone acetate or enzalutamide between October 1, 2012, and December 31, 2014 (index date). The patients were followed for up to 12 months, and their baseline characteristics were assessed during the 6 months before the index date. Medication adherence was measured at 3, 6, 9, and 12 months postindex using medication possession ratios (MPRs), and dose reduction was measured using refill gaps and relative dose intensity over the entire observation period. Kaplan-Meier survival analyses and Cox proportional hazards models were used to assess the association between the initial treatment and the risk for dose reduction. RESULTS: The study included 2591 and 807 patients who initiated treatment with abiraterone acetate and enzalutamide, respectively. At 6, 9, and 12 months postindex, the patients who initiated abiraterone acetate had higher MPRs than the patients who initiated enzalutamide. In addition, the patients who initiated abiraterone acetate had lower Kaplan-Meier rates of dose reduction across 4 measurements for dose reduction. All hazard ratios for treatment (abiraterone acetate vs enzalutamide) were significantly lower than 1 (range, 0.57-0.80), indicating a lower risk for dose reduction associated with abiraterone acetate. CONCLUSION: Patients who initiated abiraterone acetate therapy had higher medication adherence and lower risk for dose reduction than those who initiated enzalutamide therapy. Improved medication adherence may be associated with longer duration of treatment, which in turn may lead to better survival. Further research is needed to assess the potential effect of medication adherence on the overall survival of patients with metastatic CRPC.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".