Real-World Tumor Response of Palbociclib in Combination With an Aromatase Inhibitor as First-Line Therapy in Pre/Perimenopausal Women With Metastatic Breast Cancer.

Background: Limited real-world data exist regarding the efficacy of palbociclib in combination with endocrine therapy in pre/perimenopausal women with metastatic breast cancer.

Objective: We aimed to compare real-world tumor responses among pre/perimenopausal women who initiated palbociclib plus an aromatase inhibitor (AI) or AI monotherapy as first-line treatment for hormone receptor-positive/human epidermal growth factor receptor 2-negative metastatic breast cancer.

Methods: This retrospective observational cohort study (NCT05012644) used electronic health record data from The US Oncology Network.

Scalable Feature Engineering from Electronic Free Text Notes to Supplement Confounding Adjustment of Claims-Based Pharmacoepidemiologic Studies.

Natural language processing (NLP) tools turn free-text notes (FTNs) from electronic health records (EHRs) into data features that can supplement confounding adjustment in pharmacoepidemiologic studies. However, current applications are difficult to scale. We used unsupervised NLP to generate high-dimensional feature spaces from FTNs to improve prediction of drug exposure and outcomes compared with claims-based analyses.

High-dimensional propensity scores for empirical covariate selection in secondary database studies: Planning, implementation, and reporting.

Real-world evidence used for regulatory, payer, and clinical decision-making requires principled epidemiology in design and analysis, applying methods to minimize confounding given the lack of randomization. One technique to deal with potential confounding is propensity score (PS) analysis, which allows for the adjustment for measured preexposure covariates. Since its first publication in 2009, the high-dimensional propensity score (hdPS) method has emerged as an approach that extends traditional PS covariate selection to include large numbers of covariates that may reduce confounding bias in the analysis of healthcare databases.

Visualizations throughout pharmacoepidemiology study planning, implementation, and reporting.

Transparency is increasingly promoted to instill trust in nonrandomized studies using real-world data. Graphics and data visualizations support transparency by aiding communication and understanding, and can inform study design and analysis decisions. However, other than graphical representation of a study design and flow diagrams (e.

Categorization of COVID-19 severity to determine mortality risk.

Purpose: Algorithms for classification of inpatient COVID-19 severity are necessary for confounding control in studies using real-world data.

Methods: Using Healthverity chargemaster and claims data, we selected patients hospitalized with COVID-19 between April 2020 and February 2021, and classified them by severity at admission using an algorithm we developed based on respiratory support requirements (supplemental oxygen or non-invasive ventilation, O2/NIV, invasive mechanical ventilation, IMV, or NEITHER). To evaluate the utility of the algorithm, patients were followed from admission until death, discharge, or a 28-day maximum to report mortality risks and rates overall and by stratified by severity.