BACKGROUND: Routine data collections are used increasingly to examine outcomes of real-world cancer drug use. These datasets lack clinical details about important endpoints such as disease progression. AIM: To validate a proxy for disease progression in metastatic cancer patients using prescribing and dispensing claims. METHODS: We used data from a cohort study of patients undergoing chemotherapy who provided informed consent to the collection of cancer-treatment data from medical records and linkage to pharmaceutical claims. We derived proxy decision rules based on changes to drug treatment in prescription histories (n = 36 patients) and validated the proxy in prescribing data (n = 62 patients). We adapted the decision rules and validated the proxy in dispensing data (n = 109). Our gold standard was disease progression ascertained in patient medical records. Individual progression episodes were the unit of analysis for sensitivity and Positive Predictive Value (PPV) calculations and specificity and Negative Predictive Value (NPV) were calculated at the patient level. RESULTS: The sensitivity of our proxy in prescribing data was 74.3% (95% Confidence Interval (CI), 55.6-86.6%) and PPV 61.2% (95% CI, 45.0-75.3%); specificity and NPV were 87.8% (95% CI, 73.8-95.9%) and 100% (95% CI, 90.3-100%), respectively. In dispensing data, the sensitivity of our proxy was 64% (95% CI, 55.0-77.0%) and PPV 56.0% (95% CI, 43.0-69.0%); specificity and NPV were 81% (95% CI, 70.05-89.0%) and 91.0% (95% CI, 82.0-97.0%), respectively. CONCLUSION: Our proxy overestimated episodes of disease progression. The proxy's performance is likely to improve if the date of prescribing is used instead of date of dispensing in claims data and by incorporating medical service claims (such as imaging prior to drug changes) in the algorithm. Our proxy is not sufficiently robust for use in real world comparative effectiveness research for cancer medicines.
BACKGROUND: Routine data collections are used increasingly to examine outcomes of real-world cancer drug use. These datasets lack clinical details about important endpoints such as disease progression. AIM: To validate a proxy for disease progression in metastatic cancerpatients using prescribing and dispensing claims. METHODS: We used data from a cohort study of patients undergoing chemotherapy who provided informed consent to the collection of cancer-treatment data from medical records and linkage to pharmaceutical claims. We derived proxy decision rules based on changes to drug treatment in prescription histories (n = 36 patients) and validated the proxy in prescribing data (n = 62 patients). We adapted the decision rules and validated the proxy in dispensing data (n = 109). Our gold standard was disease progression ascertained in patient medical records. Individual progression episodes were the unit of analysis for sensitivity and Positive Predictive Value (PPV) calculations and specificity and Negative Predictive Value (NPV) were calculated at the patient level. RESULTS: The sensitivity of our proxy in prescribing data was 74.3% (95% Confidence Interval (CI), 55.6-86.6%) and PPV 61.2% (95% CI, 45.0-75.3%); specificity and NPV were 87.8% (95% CI, 73.8-95.9%) and 100% (95% CI, 90.3-100%), respectively. In dispensing data, the sensitivity of our proxy was 64% (95% CI, 55.0-77.0%) and PPV 56.0% (95% CI, 43.0-69.0%); specificity and NPV were 81% (95% CI, 70.05-89.0%) and 91.0% (95% CI, 82.0-97.0%), respectively. CONCLUSION: Our proxy overestimated episodes of disease progression. The proxy's performance is likely to improve if the date of prescribing is used instead of date of dispensing in claims data and by incorporating medical service claims (such as imaging prior to drug changes) in the algorithm. Our proxy is not sufficiently robust for use in real world comparative effectiveness research for cancer medicines.
Authors: Hava Izci; Tim Tambuyzer; Krizia Tuand; Victoria Depoorter; Annouschka Laenen; Hans Wildiers; Ignace Vergote; Liesbet Van Eycken; Harlinde De Schutter; Freija Verdoodt; Patrick Neven Journal: J Natl Cancer Inst Date: 2020-10-01 Impact factor: 13.506
Authors: Benjamin Daniels; Federico Girosi; Hanna Tervonen; Belinda E Kiely; Sarah J Lord; Nehmat Houssami; Sallie-Anne Pearson Journal: PLoS One Date: 2018-07-26 Impact factor: 3.240