Department of Biomedical Data Science and The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.
Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA.
Health Serv Res. 2022 Feb;57(1):182-191. doi: 10.1111/1475-6773.13882. Epub 2021 Nov 3.
To examine whether the correlation between a provider's effect on one population of patients and the same provider's effect on another population is underestimated if the effects for each population are estimated separately as opposed to being jointly modeled as random effects, and to characterize how the impact of the estimation procedure varies with sample size.
Medicare claims and enrollment data on emergency department (ED) visits, including patient characteristics, the patient's hospitalization status, and identification of the doctor responsible for the decision to hospitalize the patient.
We used a three-pronged investigation consisting of analytical derivation, simulation experiments, and analysis of administrative data to demonstrate the fallibility of stratified estimation. Under each investigation method, results are compared between the joint modeling approach to those based on stratified analyses.
DATA COLLECTION/EXTRACTION METHODS: We used data on ED visits from administrative claims from traditional (fee-for-service) Medicare from January 2012 through September 2015.
The simulation analysis demonstrates that the joint modeling approach is generally close to unbiased, whereas the stratified approach can be severely biased in small samples, a consequence of joint modeling benefitting from bivariate shrinkage and the stratified approach being compromised by measurement error. In the administrative data analyses, the estimated correlation of doctor admission tendencies between female and male patients was estimated to be 0.98 under the joint model but only 0.38 using stratified estimation. The analogous correlations for White and non-White patients are 0.99 and 0.28 and for Medicaid dual-eligible and non-dual-eligible patients are 0.99 and 0.31, respectively. These results are consistent with the analytical derivations.
Joint modeling targets the parameter of primary interest. In the case of population correlations, it yields estimates that are substantially less biased and higher in magnitude than naive estimators that post-process the estimates obtained from stratified models.
如果分别估计每个群体的效果而不是将其联合建模为随机效应，那么评估提供者对一个患者群体的效果与同一提供者对另一个患者群体的效果之间的相关性是否会被低估，并描述估计过程的影响如何随样本量而变化。
医疗保险索赔和急诊部 (ED) 就诊的登记数据，包括患者特征、患者住院状态以及确定负责决定患者住院的医生的身份。
我们使用了包括分析推导、模拟实验和行政数据分析在内的三管齐下的方法来证明分层估计的不可靠性。在每种调查方法下，比较联合建模方法与基于分层分析的结果。
数据收集/提取方法：我们使用了来自传统（按服务收费）医疗保险行政索赔的 ED 就诊数据，数据来自 2012 年 1 月至 2015 年 9 月。
模拟分析表明，联合建模方法通常接近无偏，而分层方法在小样本中可能会严重偏倚，这是联合建模受益于双变量收缩而分层方法因测量误差而受损的结果。在行政数据分析中，联合模型估计的男女患者医生入院倾向的估计相关性为 0.98，而分层估计则为 0.38。白人患者和非白人患者的类似相关性分别为 0.99 和 0.28，医疗保险双重合格患者和非双重合格患者的相关性分别为 0.99 和 0.31。这些结果与分析推导一致。
联合建模针对主要关注的参数。在群体相关性的情况下，它产生的估计值与从分层模型中获得的后处理估计值相比，偏差大大降低，幅度更大。
