Validating a proxy for disease progression in metastatic cancer patients using prescribing and dispensing data.
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.


OBJECTIVE
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.


CONCLUSIONS
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.
Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Vikram
V
Joshi
Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Barbara-Ann
BA
Adelstein
Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia.|Faculty of Pharmacy, University of Sydney, Sydney, Australia.
Andrea
A
Schaffer
Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia.|Faculty of Pharmacy, University of Sydney, Sydney, Australia.
Preeyaporn
P
Srasuebkul
School of Public Health, Sydney Medical School, University of Sydney, Sydney, Australia.
Timothy
T
Dobbins
Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia.|Faculty of Pharmacy, University of Sydney, Sydney, Australia.|School of Public Health, Sydney Medical School, University of Sydney, Sydney, Australia.
Sallie-Anne
SA
Pearson
Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia.|Centre for Health Economics Research and Evaluation (CHERE), University of Technology, Sydney, Australia.
D000293:Adolescent; D000328:Adult; D000368:Aged; D015331:Cohort Studies; D003625:Data Collection / Q000379:methods; D018450:Disease Progression; D011307:Drug Prescriptions / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009362:Neoplasm Metastasis; D009369:Neoplasms / Q000188:drug therapy* / Q000473:pathology*; D016609:Neoplasms, Second Primary / Q000188:drug therapy / Q000473:pathology; D019727:Proxy; D015203:Reproducibility of Results; D055815:Young Adult
cancer; claims data; disease progression; observational studies; physician practice patterns