Evaluation of a geriatrics primary care model using prospective matching to guide enrollment.
Evaluation of a geriatrics primary care model using prospective matching to guide enrollment.
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DOI：
10.1186/s12874-021-01360-4
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发表时间：
2021-08-16
影响因子：
4
通讯作者：
Hastings SN
中科院分区：
文献类型：
--
作者：
Smith VA;Van Houtven CH;Lindquist JH;Hastings SN
对于需要纵向原始数据收集的非随机计划和政策进行严格的大规模前瞻性评估，目前几乎没有明确的指导方针。在退伍军人事务部 (VA)，我们发现有必要了解老年病初级保健模式（称为 GeriPACT）的影响；然而，将患者随机分配至 GeriPACT 与传统 PACT 是不可行的，因为 GeriPACT 已在全国范围内推广，并且从 PACT 过渡到 GeriPACT 的决定是由患者和提供者共同做出的。我们描述了我们的研究设计，用于评估 GeriPACT 与传统初级保健模式（称为 PACT）在患者体验和护理质量指标方面的比较有效性。我们使用前瞻性匹配来指导 57 个 VA 医疗中心的 GeriPACT-PACT 患者二人组的登记。首先，我们使用粗化精确匹配和距离函数匹配相结合的方法，对 11 个已识别的可能充当混杂因素的关键变量进行粗化精确匹配和距离函数匹配，从而根据一系列管理得出的特征来确定匹配。一旦 GeriPACT 患者入组，就会根据距离函数使用预先分配的优先级类别联系匹配的 PACT 患者进行招募；如果符合条件并同意，患者将被登记并接受为期 18 个月的电话调查。我们成功近乎实时地招募了 275 名匹配的二人组，招募 GeriPACT 患者和密切匹配的 PACT 患者之间的中位时间为 7 天。几乎所有基线变量的标准化平均差异< 0.2表明基线协变量平衡良好。调查收集的基线协变量在匹配时无法获得的异常平衡表明我们的程序成功地控制了许多已知但在管理上未观察到的进入 GeriPACT 的驱动因素。我们提出了一个重要的过程，用于在随机化不可行时前瞻性评估不同治疗的效果，并为可能有兴趣实施类似方法的研究人员提供指导。治疗前时期的丰富匹配变量反映了治疗分配机制，创建了一个高质量的对照组供招募。该设计利用国家行政数据的力量以及患者报告结果的收集，从而能够对非随机计划或政策进行严格评估。
Few definitive guidelines exist for rigorous large-scale prospective evaluation of nonrandomized programs and policies that require longitudinal primary data collection. In Veterans Affairs (VA) we identified a need to understand the impact of a geriatrics primary care model (referred to as GeriPACT); however, randomization of patients to GeriPACT vs. a traditional PACT was not feasible because GeriPACT has been rolled out nationally, and the decision to transition from PACT to GeriPACT is made jointly by a patient and provider. We describe our study design used to evaluate the comparative effectiveness of GeriPACT compared to a traditional primary care model (referred to as PACT) on patient experience and quality of care metrics. We used prospective matching to guide enrollment of GeriPACT-PACT patient dyads across 57 VA Medical Centers. First, we identified matches based an array of administratively derived characteristics using a combination of coarsened exact and distance function matching on 11 identified key variables that may function as confounders. Once a GeriPACT patient was enrolled, matched PACT patients were then contacted for recruitment using pre-assigned priority categories based on the distance function; if eligible and consented, patients were enrolled and followed with telephone surveys for 18 months. We successfully enrolled 275 matched dyads in near real-time, with a median time of 7 days between enrolling a GeriPACT patient and a closely matched PACT patient. Standardized mean differences of < 0.2 among nearly all baseline variables indicates excellent baseline covariate balance. Exceptional balance on survey-collected baseline covariates not available at the time of matching suggests our procedure successfully controlled many known, but administratively unobserved, drivers of entrance to GeriPACT. We present an important process to prospectively evaluate the effects of different treatments when randomization is infeasible and provide guidance to researchers who may be interested in implementing a similar approach. Rich matching variables from the pre-treatment period that reflect treatment assignment mechanisms create a high quality comparison group from which to recruit. This design harnesses the power of national administrative data coupled with collection of patient reported outcomes, enabling rigorous evaluation of non-randomized programs or policies.
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影响因子：
2.3
作者：
Sullivan, Jennifer L.;Shin, Marlena H.;Solimeo, Samantha
通讯作者：
Solimeo, Samantha
影响因子：
5.7
作者：
Knopman, David S.;Roberts, Rosebud O.;Rocca, Walter A.
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Rocca, Walter A.
减少观察研究中混杂影响的倾向评分方法简介。
影响因子：
3.8
作者：
Austin PC
通讯作者：
Austin PC
影响因子：
3.4
作者：
Ali, M. Sanni;Groenwold, Rolf H. H.;Klungel, Olaf H.
通讯作者：
Klungel, Olaf H.
影响因子：
3
作者：
Wang, Li;Porter, Brian;Fihn, Stephan D.
通讯作者：
Fihn, Stephan D.