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Objective: To examine the sensitivity of split‐sample reliability estimates to the random split of the data and propose alternative methods for improving the stability of the split‐sample method. Data Sources and Study Setting: Data were simulated to reflect a variety of real‐world quality measure distributions and scenarios. There is no date range to report as the data are simulated. Study Design: Simulation studies of split‐sample reliability estimation were conducted under varying practical scenarios. Data Collection/Extraction Methods: All data were simulated using functions in R. Principal Findings: Single split‐sample reliability estimates can be very dependent on the random split of the data, especially in low sample size and low variability settings. Averaging split‐sample estimates over many splits of the data can yield a more stable reliability estimate. Conclusions: Measure developers and evaluators using the split‐sample reliability method should average a series of reliability estimates calculated from many resamples of the data without replacement to obtain a more stable reliability estimate. [ABSTRACT FROM AUTHOR]

Objective: To illustrate the importance of a multidimensional view of disparities in quality of antidepressant medication management (AMM), as well as discriminating "within‐facility" disparities from disparities that exist between facilities. Data Sources and Study Setting: We used data from the Veterans Health Administration's (VA) Corporate Data Warehouse (CDW) which contains clinical and administrative data from VA facilities nationally. Study Design: CDW data were used to measure five indicators of AMM quality, including the HEDIS Effective Acute‐Phase and Effective Continuation‐Phase measures. Mixed effects regression models were used to examine differences in quality indicators between racial/ethnic groups, controlling for other demographic and clinical factors. An adaptation of the Kitagawa‐Blinder‐Oaxaca (KBO) method was used to decompose mean differences in treatment quality between racial and ethnic groups into within‐ and between‐facility effects. Data Extraction Methods: Demographic, clinical, and health service utilization data were extracted for patients in fiscal year 2017 with a diagnosis of depression and a new start of an antidepressant medication. Principal Findings: The decomposition of the overall differences between White and Black patients on receiving an initial 90‐day prescription (46.7% vs. 32.7%), Effective Acute‐Phase (79.7% vs. 66.8%), and Effective Continuation‐Phase (64.0% vs. 49.6%) HEDIS measures revealed that most of the overall effects were "within‐facility," meaning that Black patients are less likely to meet these measures regardless of where they are treated. Although the overall magnitude of disparities between White and Hispanic patients on these three measures was very similar (46.7% vs. 32.7%; 79.7% vs. 69.2%; 64.0% vs. 53.6%), the differences were more attributable to Hispanic patients being treated in facilities with overall lower performance on these measures. Conclusions: Discriminating within‐ and between‐facility disparities and taking a multidimensional view of quality are essential to informing efforts to address disparities in AMM quality. [ABSTRACT FROM AUTHOR]

Background: Across studies of average treatment effects, some population subgroups consistently have lower representation than others which can lead to discrepancies in how well results generalize. Methods: We develop a framework for quantifying inequity due to systemic disparities in sample representation and a method for mitigation during data analysis. Assuming subgroup treatment effects are exchangeable, an unbiased sample average treatment effect estimator will have lower mean-squared error, on average across studies, for subgroups with less representation when treatment effects vary. We present a method for estimating average treatment effects in representation-adjusted samples which enables subgroups to optimally leverage information from the full sample rather than only their own subgroup's data. Two approaches for specifying representation adjustment are offered—one minimizes average mean-squared error for each subgroup separately and the other balances minimization of mean-squared error and equal representation. We conduct simulation studies to compare the performance of the proposed estimators to several subgroup-specific estimators. Results: We find that the proposed estimators generally provide lower mean squared error, particularly for smaller subgroups, relative to the other estimators. As a case study, we apply this method to a subgroup analysis from a published study. Conclusions: We recommend the use of the proposed estimators to mitigate the impact of disparities in representation, though structural change is ultimately needed. [ABSTRACT FROM AUTHOR]

Background Gene-set analyses measure the association between a disease of interest and a "set" of genes related to a biological pathway. These analyses often incorporate gene network properties to account for differential contributions of each gene. We extend this concept further—defining gene contributions based on biophysical properties—by leveraging mathematical models of biology to predict the effects of genetic perturbations on a particular downstream function. Results We present a method that combines gene weights from model predictions and gene ranks from genome-wide association studies into a weighted gene-set test. We demonstrate in simulation how such a method can improve statistical power. To this effect, we identify a gene set, weighted by model-predicted contributions to intracellular calcium ion concentration, that is significantly related to bipolar disorder in a small dataset (P = 0.04; n = 544). We reproduce this finding using publicly available summary data from the Psychiatric Genomics Consortium (P = 1.7 × 10−4; n = 41,653). By contrast, an approach using a general calcium signaling pathway did not detect a significant association with bipolar disorder (P = 0.08). The weighted gene-set approach based on intracellular calcium ion concentration did not detect a significant relationship with schizophrenia (P = 0.09; n = 65,967) or major depression disorder (P = 0.30; n = 500,199). Conclusions Together, these findings show how incorporating math biology into gene-set analyses might help to identify biological functions that underlie certain polygenic disorders. [ABSTRACT FROM AUTHOR]