Volume 19, Number 4Review ArticlesActive Surveillance Use Among a Low-risk Prostate Cancer Population in a Large US Payer System: 17-Gene Genomic Prostate Score Versus Other Risk Stratification MethodsOriginal ResearchMichael J KemeterPhillip G FebboSteven CanfieldJohn HornbergerMany men with low-risk prostate cancer (PCa) receive definitive treatment despite recommendations that have been informed by two large, randomized trials encouraging active surveillance (AS). We conducted a retrospective cohort study using the Optum™ Research Database (Eden Prairie, MN) of electronic health records and administrative claims data to assess AS use for patients tested with a 17-gene Genomic Prostate Score™ (GPS; Genomic Health, Redwood City, CA) assay and/or prostate magnetic resonance imaging (MRI). De-identified records were extracted on health plan members enrolled from June 2013 to June 2016 who had ≥1 record of PCa (n = 291,876). Inclusion criteria included age ≥18 years, new diagnosis, American Urological Association low-risk PCa (stage T1-T2a, prostate-specific antigen ≥10 ng/mL, Gleason score = 6), and clinical activity for at least 12 months before and after diagnosis. Data included baseline characteristics, use of GPS testing and/or MRI, and definitive procedures. GPS or MRI testing was performed in 17% of men (GPS, n = 375, 4%; MRI, n = 1174, 13%). AS use varied from a low of 43% for men who only underwent MRI to 89% for GPS-tested men who did not undergo MRI (P<001). At 6-month follow-up, AS use was 31.0% higher (95% CI, 27.6%-34.5%; P<001) for men receiving the GPS test only versus men who did not undergo GPS testing or MRI; the difference was 30.5% at 12-month follow-up. In a large US payer system, the GPS assay was associated with significantly higher AS use at 6 and 12 months compared with men who had MRI only, or no GPS or MRI testing. [Rev Urol. 2017;19(4):203–212 doi: 10.3909/riu0786] © 2018 MedReviews®, LLCProstate cancerActive surveillanceEvidence-based practiceComparative effectiveness researchGenomic biomarkerMagnetic resonance imaging
Volume 20, Number 2Original ResearchBalancing Confounding and Generalizability Using Observational, Real-world Data: 17-gene Genomic Prostate Score Assay Effect on Active SurveillanceMichael J KemeterPhillip G FebboSteven CanfieldJohn HornbergerRandomized, controlled trials can provide high-quality, unbiased evidence for therapeutic interventions but are not always a practical or viable study design for certain healthcare decisions, such as those involving prognostic or predictive testing. Studies using large, real-world databases may be more appropriate and more generalizable to the intended target population of physicians and patients to answer these questions but carry potential for hidden bias. We illustrate several emerging methods of analyzing observational studies using propensity score matching (PSM) and coarsened exact matching (CEM). These advanced statistical methods are intended to reveal a “hidden experiment” within an observational database, and so refute or confirm a potential causal effect of assignment to an intervention and study outcome. We applied these methods to the Optum™ Research Database (ORD; Eden Prairie, MN) of electronic health records and administrative claims data to assess the effect of the 17-gene Genomic Prostate Score® (GPS™; Genomic Health, Redwood City, CA) assay on use of active surveillance (AS). In a traditional multivariable logistic regression, the GPS assay increased the use of AS by 29% (95% CI, 24%-33%). Upon applying the matching methods, the effect of the GPS assay on AS use varied between 27% and 80% and the matched data were significant among all algorithms. All matching algorithms performed well in identifying matched data that improved the imbalance in baseline covariates. By using different matching methods to assess causal inference in an observational database, we provide further confidence that the effect of the GPS assay on AS use is statistically significant and unlikely to be a result of confounding due to differences in baseline characteristics of the patients or the settings in which they were seen. [Rev Urol. 2018;20(2):69–76 doi: 10.3909/riu0799] © 2018 MedReviews, LLC®Prostate cancerActive surveillanceEvidence-based practiceComparative effectiveness researchGenomic biomarkerPropensity scoreMatching