Comparison of treatment patterns and economic outcomes in metastatic breast cancer patients initiated on trastuzumab versus lapatinib: a retrospective analysis
Bibliographic record
Abstract
Few studies have compared treatment patterns, healthcare resource utilization (HRU), and costs in patients with metastatic breast cancer (mBC) receiving HER2 directed therapy. This study evaluated these outcomes in patients receiving trastuzumab or lapatinib. Adult women with mBC, who were initiated on trastuzumab or lapatinib, on or after March 13, 2007, were selected from the US-based PharMetrics® Integrated Database (2000-2011). Patients were required to be continuously enrolled in their healthcare plan for ≥6 months prior to and ≥30 days following trastuzumab or lapatinib initiation. Trastuzumab or lapatinib discontinuation rates (defined as a gap ≥45 consecutive days) were compared using multivariate Cox proportional-hazards models. HRU and monthly healthcare cost differences were estimated using multivariate negative binomial regression models and generalized linear models, respectively. Among the 643 patients who met the inclusion criteria, 381 and 262 patients were included in the trastuzumab and lapatinib groups, respectively. The majority of the 262 patients receiving lapatinib previously received trastuzumab (N = 171 [65.3%]). After adjustment for potential confounders, when compared to trastuzumab patients, lapatinib patients had a higher rate of treatment discontinuation (hazard ratio [HR] = 1.57; P < 0.001), a higher rate of outpatient visits (not treatment administration related) (IRR = 1.19; P < 0.004), and a lower rate of medical visits associated with treatment administration (IRR = 0.34; P < 0.001). There were no significant differences between the two groups in total monthly healthcare costs ($11,920 vs. $11,898 for trastuzumab and lapatinib patients, respectively; P = 0.451). Findings from our study show that, irrespective of the treatment initiated at index date, disease management in patients with mBC is associated with similar and substantial healthcare costs. Any differences in specific components of healthcare costs were associated with differences in the mode of treatment administration. Approximately 50% of all costs were non-drug related, and future studies should focus on how these costs may be controlled, regardless of mode of treatment administration.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".