Emulating Control Arms for Cancer Clinical Trials Using External Cohorts Created From Electronic Health Record-Derived Real-World Data.

Electronic health record (EHR)-derived real-world data (RWD) can be sourced to create external comparator cohorts to oncology clinical trials. This exploratory study assessed whether EHR-derived patient cohorts could emulate select clinical trial control arms across multiple tumor types. The impact of analytic decisions on emulation results was also evaluated. By digitizing Kaplan-Meier curves, we reconstructed published control arm results from 15 trials that supported drug approvals from January 1, 2016, to April 30, 2018. RWD cohorts were constructed using a nationwide EHR-derived de-identified database by aligning eligibility criteria and weighting to trial baseline characteristics. Trial data and RWD cohorts were compared using Kaplan-Meier and Cox proportional hazards regression models for progression-free survival (PFS) and overall survival (OS; individual cohorts) and multitumor random effects models of hazard ratios (HRs) for median endpoint correlations (across cohorts). Post hoc, the impact of specific analytic decisions on endpoints was assessed using a case study. Comparing trial data and weighted RWD cohorts, PFS results were more similar (HR range = 0.63-1.18, pooled HR = 0.84, correlation of median = 0.91) compared to OS (HR range = 0.36-1.09, pooled HR = 0.76, correlation of median = 0.85). OS HRs were more variable and trended toward worse for RWD cohorts. The post hoc case study had OS HR ranging from 0.67 (95% confidence interval (CI): 0.56-0.79) to 0.92 (95% CI: 0.78-1.09) depending on specific analytic decisions. EHR-derived RWD can emulate oncology clinical trial control arm results, although with variability. Visibility into clinical trial cohort characteristics may shape and refine analytic approaches.

Using Collaborative Simulation Modeling to Develop a Web-Based Tool to Support Policy-Level Decision Making About Breast Cancer Screening Initiation Age.

BACKGROUND: There are no publicly available tools designed specifically to assist policy makers to make informed decisions about the optimal ages of breast cancer screening initiation for different populations of US women. OBJECTIVE: To use three established simulation models to develop a web-based tool called Mammo OUTPuT. METHODS: The simulation models use the 1970 US birth cohort and common parameters for incidence, digital screening performance, and treatment effects. Outcomes include breast cancers diagnosed, breast cancer deaths averted, breast cancer mortality reduction, false-positive mammograms, benign biopsies, and overdiagnosis. The Mammo OUTPuT tool displays these outcomes for combinations of age at screening initiation (every year from 40 to 49), annual versus biennial interval, lifetime versus 10-year horizon, and breast density, compared to waiting to start biennial screening at age 50 and continuing to 74. The tool was piloted by decision makers (n = 16) who completed surveys. RESULTS: The tool demonstrates that benefits in the 40s increase linearly with earlier initiation age, without a specific threshold age. Likewise, the harms of screening increase monotonically with earlier ages of initiation in the 40s. The tool also shows users how the balance of benefits and harms varies with breast density. Surveys revealed that 100% of users (16/16) liked the appearance of the site; 94% (15/16) found the tool helpful; and 94% (15/16) would recommend the tool to a colleague. CONCLUSIONS: This tool synthesizes a representative subset of the most current CISNET (Cancer Intervention and Surveillance Modeling Network) simulation model outcomes to provide policy makers with quantitative data on the benefits and harms of screening women in the 40s. Ultimate decisions will depend on program goals, the population served, and informed judgments about the weight of benefits and harms.

Lymphovascular invasion as a prognostic indicator in stage I non-small cell lung cancer: a systematic review and meta-analysis.

BACKGROUND: Lymphovascular invasion (LVI) is considered a high-risk pathologic feature in resected non-small cell carcinoma (NSCLC). The ability to stratify stage I patients into risk groups may permit refinement of adjuvant treatment recommendations. We performed a systematic review and meta-analysis to evaluate whether the presence of LVI is associated with disease outcome in stage I NSCLC patients. METHODS: A systematic search of the literature was performed (1990 to December 2012 in MEDLINE/EMBASE). Two reviewers independently assessed the quality of the articles and extracted data. Pooled hazard ratios (HRs) and 95% confidence intervals (CI) were estimated with a random effects model. Two end points were independently analyzed: recurrence-free survival (RFS) and overall survival (OS). We analyzed unadjusted and adjusted effect estimates, resulting in four separate meta-analyses. RESULTS: We identified 20 published studies that reported the comparative survival of stage I patients with and without LVI. The unadjusted pooled effect of LVI was significantly associated with worse RFS (HR, 3.63; 95% CI, 1.62 to 8.14) and OS (HR, 2.38; 95% CI, 1.72 to 3.30). Adjusting for potential confounders yielded similar results, with RFS (HR, 2.52; 95% CI, 1.73 to 3.65) and OS (HR, 1.81; 95% CI, 1.53 to 2.14) both significantly worse for patients exhibiting LVI. CONCLUSIONS: The present study indicates that LVI is a strong prognostic indicator for poor outcome for patients with surgically managed stage I lung cancer. Future prospective lung cancer trials with well-defined methods for evaluating LVI are necessary to validate these results.