Weak correlations in health services research: Weak relationships or common error?
Weak correlations in health services research: Weak relationships or common error?
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卫生服务研究中的弱相关性：弱关系还是常见错误？
DOI：
10.1111/1475-6773.13882
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发表时间：
2022
影响因子：
3.4
通讯作者：
McWilliams,JohnMichael
中科院分区：
文献类型：
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作者：
O'Malley,AlistairJames;Landon,BruceE;Zaborski,LawrenceA;Roberts,EricT;Khidir,HazarH;Smulowitz,PeterB;McWilliams,JohnMichael
目的：如果对每个人群的影响分别进行估计，而不是联合建模为随机效应，则检查提供者对一个患者群体的影响与同一提供者对另一个人群的影响之间的相关性是否被低估，并表征估计过程的影响如何随样本量而变化。数据来源急诊科（ED）就诊的医疗保险索赔和登记数据，包括患者特征、患者住院状态以及负责决定患者住院的医生的身份。研究设计我们采用了三管齐下的调查，包括分析推导、模拟实验和行政数据分析，以证明分层估计的错误性。在每种调查方法下，将联合建模方法与分层分析方法的结果进行了比较。数据收集/提取方法我们使用了2012年1月至2015年9月来自传统医疗保险（按服务收费）行政索赔的急诊科就诊数据。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存在严重偏倚，这是由于联合建模受益于双变量收缩和分层方法受到测量误差的影响。在行政数据分析中，联合模型估计男女患者就诊倾向的相关系数为0.98，而分层估计仅为0.38。白人和非白人患者的类似相关性分别为0.99和0.28，医疗补助双重资格和非双重资格患者的类似相关性分别为0.99和0.31。这些结果与解析推导一致。结论联合建模针对主要感兴趣的参数。在人口相关性的情况下，它产生的估计值比从分层模型中获得的估计值进行后处理的朴素估计值偏差小得多，而且量级更高。
ObjectiveTo 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.Data sourcesMedicare 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.Study designWe 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 methodsWe used data on ED visits from administrative claims from traditional (fee‐for‐service) Medicare from January 2012 through September 2015.Principal findingsThe 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.ConclusionsJoint 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.