Comparing treatments via the propensity score: stratification or modeling? [0.03%]
通过倾向值评分比较治疗措施：分层或建模？
Jessica A Myers,Thomas A Louis
Jessica A Myers
In observational studies of treatments or interventions, propensity score (PS) adjustment is often useful for controlling bias in estimation of treatment effects. Regression on PS is used most often and can be highly efficient, but it can l...
State Investments in Psychiatric Innovation: Investigating Unmeasured State Factors [0.03%]
精神卫生创新的州投资：探究未测量的州因素
Marisa Elena Domino,Christopher Alan Beadles
Marisa Elena Domino
We apply three separate panel data estimation methods to examine the diffusion of technologies at the state-level. These methods include the Hausman-Taylor random effects model, the fixed effects vector decomposition (FEVD), and generalized...
Assessing the accuracy of profiling methods for identifying top providers: performance of mental health care providers [0.03%]
评估识别顶级提供者的方法的准确性：精神健康护理提供者的绩效
Victoria Y Ding,Rebecca A Hubbard,Carolyn M Rutter et al.
Victoria Y Ding et al.
Provider profiling as a means to describe and compare the performance of health care professionals has gained momentum in the past decade. As a key component of pay-for-performance programs profiling has been increasingly used to identify t...
Evaluating long-term effects of a psychiatric treatment using instrumental variable and matching approaches [0.03%]
利用工具变量匹配方法评估精神疾病治疗的长期效果
Bo Lu,Sue Marcus
Bo Lu
Evaluating treatment effects in non-randomized studies is challenging due to the potential unmeasured confounding and complex form of observed confounding. Propensity score based approaches, such as matching or weighting, are commonly used ...
Applying standardized drug terminologies to observational healthcare databases: a case study on opioid exposure [0.03%]
基于观察性医疗数据库的用药术语标准化研究-以阿片类药物为例
Frank J Defalco,Patrick B Ryan,M Soledad Cepeda
Frank J Defalco
Observational healthcare databases represent a valuable resource for health economics, outcomes research, quality of care, drug safety, epidemiology and comparative effectiveness research. The methods used to identify a population for study...
Instrumental variable specifications and assumptions for longitudinal analysis of mental health cost offsets [0.03%]
纵向分析心理健康费用替代效应的工具变量规范和假设
A James O&#x;Malley
A James O&#x;Malley
Instrumental variables (IVs) enable causal estimates in observational studies to be obtained in the presence of unmeasured confounders. In practice, a diverse range of models and IV specifications can be brought to bear on a problem, partic...
Bias and variance trade-offs when combining propensity score weighting and regression: with an application to HIV status and homeless men [0.03%]
倾向值加权与回归结合时的偏差和方差权衡问题——以艾滋病状况和无家可归男性为例研究
Daniela Golinelli,Greg Ridgeway,Harmony Rhoades et al.
Daniela Golinelli et al.
The quality of propensity scores is traditionally measured by assessing how well they make the distributions of covariates in the treatment and control groups match, which we refer to as "good balance". Good balance guarantees less biased e...
Assessing the Sensitivity of Treatment Effect Estimates to Differential Follow-Up Rates: Implications for Translational Research [0.03%]
治疗效果估计对随访率差异的敏感性评估：对转化研究的影响
Beth Ann Griffin,Daniel McCaffrey,Rajeev Ramchand et al.
Beth Ann Griffin et al.
We develop a new tool for assessing the sensitivity of findings on treatment effectiveness to differential follow-up rates in the two treatment conditions being compared. The method censors the group with the higher response rate to create ...
Degrees of health disparities: Health status disparities between young adults with high school diplomas, sub-baccalaureate degrees, and baccalaureate degrees [0.03%]
学历差异对健康不平等的影响：高中毕业生、副学士学位和学士学位的年轻成人健康状况的比较研究
J Rosenbaum
J Rosenbaum
Community colleges have increased post-secondary educational access for disadvantaged youth, but it is unknown how community college degrees fit into the educational gradient of health status disparities. Using data from high school graduat...
Using AIC in Multiple Linear Regression framework with Multiply Imputed Data [0.03%]
多重插补数据在多重线性回归框架中使用AIC
Ashok Chaurasia,Ofer Harel
Ashok Chaurasia
Many model selection criteria proposed over the years have become common procedures in applied research. However, these procedures were designed for complete data. Complete data is rare in applied statistics, in particular in medical, publi...