Development, validation and results from the impact of treatment evolution in non-small cell lung cancer (iTEN) model.

OBJECTIVES: Treatment of advanced NSCLC (aNSCLC) is rapidly evolving, as new targeted and immuno-oncology (I-O) treatments become available. The iTEN model was developed to predict the cost and survival benefits of changing aNSCLC treatment patterns from a Canadian healthcare system perspective. This report describes iTEN model development and validation. MATERIALS & METHODS: A discrete event patient simulation of aNSCLC was developed. A modified Delphi process using Canadian clinical experts informed the development of treatment sequences that included commonly used, Health Canada approved treatments of aNSCLC. Treatment efficacy and the timing of progression and death were estimated from published Kaplan-Meier progression free and overall survival data. Costs (2018 CDN$) included were: drug acquisition and administration, imaging, monitoring, adverse events, physician visits, best supportive care, and end-of-life. RESULTS AND CONCLUSION: Clinical validity of the iTEN model was assessed by comparing model survival predictions to published real-world evidence (RWE). Four RWE studies that reported the overall survival of patients treated with a broad sampling of common aNSCLC treatment patterns were used for validation. The validation coefficient of determination was R2 = 0.95, with the model generally producing estimates that were neither optimistic nor conservative. The model estimated that current Canadian practice patterns yield a median survival of almost 13 months, a five-year survival rate of 3% and a life-time per-treated-patient cost of $110,806. Cost and survival estimates are presented and were found to vary by aNSCLC subtype. In conclusion, the iTEN model is a reliable tool for forecasting the impact on cost and survival of new treatments for aNSCLC.

Adherence with capecitabine: A population-based analysis based on prescription refill data.

Background Patient adherence is important with the increasing use of oral anticancer drugs. Recent studies reported different capecitabine adherence rates based on self-reporting and microelectronic monitoring of the medication bottle. Patient's awareness of being monitored may confound these results. Prescription records provide a larger and more objective dataset for adherence investigation. We report the use of computer algorithm and manual review of prescription and medical documentation to determine the rate of capecitabine adherence. Methods Two years of capecitabine prescription records from five ambulatory cancer centres were reviewed. Prescription data were extracted using a custom Java-based software tool to compare the predicted vs. actual dispensing date. The difference between the dates was the primary adherence measure (altered treatment date incident) and estimated using a computer algorithm and by manual review of medical charts. Results Of 4412 refill prescriptions, 45.2% was associated with an altered treatment date incident based on the initial computer algorithm. This was reduced to 29.5% after adjusting for clinic scheduling processes and 10.2% after manual chart review to adjust for valid reasons for delay. The reasons for altered treatment date incident were not identified in 52.2% of prescriptions. Conclusions Adherence rate of capecitabine based on refill data seem to be high and consistent with other findings based on patient self-report. Population analysis of prescription data with custom computer algorithm may identify trends in capecitabine adherence with some efficiency. Manual review would likely be required to verify these results. The accuracy of using altered prescription refill dates as an adherence measure requires further studies.