A General Propensity Score for Signal Identification Using Tree-Based Scan Statistics.

The tree-based scan statistic (TreeScan; Martin Kulldorff, Harvard Medical School, Boston, Massachusetts) is a data-mining method that adjusts for multiple testing of correlated hypotheses when screening thousands of potential adverse events for signal identification. Simulation has demonstrated the promise of TreeScan with a propensity score (PS)-matched cohort design. However, it is unclear which variables to include in a PS for applied signal identification studies to simultaneously adjust for confounding across potential outcomes.

Quantifying how small variations in design elements affect risk in an incident cohort study in claims.

Background: Epidemiological study reporting is improving but is not transparent enough for easy evaluation or replication. One barrier is insufficient details about design elements in published studies.

Methods: Using a previously conducted drug safety evaluation in claims as a test case, we investigated the impact of small changes in five key design elements on risk estimation.

Representation of obese participants in obesity-related cancer randomized trials.

Background: Obesity is a risk factor for numerous cancer types, and may influence cancer treatment outcomes. Underrepresentation of obese patients in obesity-related cancer randomized controlled trials (RCTs) may affect generalizability of results. We aimed to assess the reporting of information about eligibility and enrollment of obese participants in obesity-related cancer RCTs.