Comparison of Approaches for Measuring Adherence and Persistence to Oral Oncologic Therapies in Patients Diagnosed with Metastatic Renal Cell Carcinoma
Bibliographic record
Abstract
BACKGROUND: Adherence and persistence studies face several methodologic difficulties, including short-term mortality. We compared approaches to quantify adherence and persistence to first line (1L) oral targeted therapy (TT) in patients diagnosed with metastatic renal cell carcinoma (mRCC). METHODS: Patients with mRCC ages 66 years or more who initiated TTs within 4 months of diagnosis were identified in the Surveillance, Epidemiology, and End Results Medicare-linked database (2007-2015). Adherence [proportion of days covered (PDC) >80%] was calculated using (i) PDC with a fixed 6-month denominator including then excluding patients who died within the 6 months and (ii) PDC with a denominator measuring time on treatment. Risk of nonpersistence was obtained by censoring death or treating death as a competing risk using cumulative incidence functions. RESULTS: Among 485 patients with mRCC initiating a 1L oral TT (sunitinib, 64%; pazopanib, 25%; other, 11%), 40% died within 6 months. Adherence was higher after restricting to patients who survived (60%) compared with including those patients and assigning zero days covered after death (47%). Risk of nonpersistence was higher when censoring patients at death, 0.91 [95% confidence interval (CI), 0.88-0.94], compared with treating death as a competing risk, 0.75 (95% CI, 0.71-0.79). CONCLUSIONS: Different approaches to handling death resulted in different adherence and persistence estimates in the metastatic setting. Future studies should explicitly report the proportion of patient deaths over time and explore appropriate methods to account for death as competing risk. IMPACT: Use of several approaches can provide a more comprehensive picture of medication-taking behavior in the metastatic setting where death is a major competing risk.
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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 | 0.027 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".