Improving Analysis of Endogenous Multimodal Treatments for Use in Geriatrics Health Outcomes Studies

现有的大型数据集（如老年病学和扩展护理数据和分析中心 [GEC-DAC]数据集）和前瞻性观察性/准实验性研究， 重要的研究问题，在重病的老年人，并探索新的模式提供护理。 随机对照试验可能会给重病患者带来负担，或者不可行， 不能产生可推广到感兴趣人群的结果。老年患者的观察数据分析 姑息治疗必须考虑到严重的治疗内分泌失调，当各种因素同时存在时， 与治疗可能性和结果相关。倾向分数是解决内分泌问题的一种方法。一 倾向评分是以一组观察到的协变量为条件的估计治疗接受概率 被认为与治疗可能性和结果相关。无偏倚的治疗效果 可以通过比较具有相似倾向分数的治疗个体和对照个体来估计。最 对倾向分数的指导仅限于将具有相似倾向分数的个体进行匹配的方法 两组（治疗组和未治疗组）。然而，许多治疗方法具有多个水平，并且限制 对二元指标的处理掩盖了群体之间的差异。按倾向分数加权是 当存在多个治疗组时，匹配的上级替代方案。本研究旨在开发最佳 使用倾向评分进行多模式治疗的实践，并加强研究人员使用 现有的VHA数据，以提高老年退伍军人的医疗保健价值和效率。具体而言，本研究将1） 使用模拟数据来确定哪个加权/估计组合（逆概率加权或 通过最大似然估计回归估计的倾向得分进行核加权，协变量- 平衡倾向得分估计或广义提升方法）提供了最有效的估计 在各种估计方案中具有最小偏差，2）确定哪种加权/估计策略 提供了观察到的最佳协变量平衡（倾向评分性能的次要指标）， 在各种模拟估计情景中的多个处理水平，以及3）确定 加权/估计策略最不容易受到残余混杂的影响。传统的蒙特卡洛和 将使用等离子体模式（基于经验的）模拟来实现这些目标。为了便于翻译结果， 我们将在具有不同样本量和预期治疗效果的经验数据集中重复目标2和3 异质性结果将通过估计镇静催眠药对住院死亡风险的影响来验证， 先前收集的数据来自一项对10万名患有癌症、心力衰竭、慢性 阻塞性肺病和/或艾滋病毒/艾滋病，以及对30万名服用阿片类药物的退伍军人的研究 处方.我们期望在共同的估计中识别策略的上级性能的模式 情景以及推理最有可能出现分歧的情景。我们将开发培训材料 根据我们的研究结果，并与观察数据分析领域的领导者咨询委员会合作， 广泛传播这些结果，并为非随机卫生保健干预研究提供信息（如 住院后转诊至Geri-PACT）以及使用VHA“大数据”资源的研究。

Increasingly, existing large datasets (such as the Geriatrics and Extended Care Data and Analysis Center [GEC-DAC] dataset) and prospective observational/quasi-experimental studies are being used to examine important research questions in seriously ill older adults and to explore new models of care delivery. Randomized controlled trials can be burdensome to seriously ill patients or infeasible to conduct, and they may not produce results generalizable to the population of interest. Observational data analyses in geriatric palliative care must account for severe treatment endogeneity, which occurs when factors are simultaneously associated with treatment likelihood and outcomes. Propensity scores are one way to address endogeneity. A propensity score is the estimated probability of treatment receipt, conditional on a set of observed covariates that are thought to be associated with both treatment likelihood and outcome. An unbiased treatment effect can be estimated by comparing treated and comparison individuals with similar propensity scores. Most guidance on propensity scores is restricted to methods for matching individuals with similar propensity scores across two groups (treatment, no treatment). Many treatments, however, have multiple levels, and restricting treatments to binary indicators obscures differences between groups. Weighting by propensity scores is a superior alternative to matching when there are multiple treatment groups. This study aims to develop best practices for using propensity scores for multimodal treatments and to strengthen researchers’ abilities to use existing VHA data to improve health care value and efficiency for older veterans. Specifically, this study will 1) Use simulated data to determine which weighting/estimation combination (inverse probability weighting or kernel weighting by propensity scores estimated via regression with maximum likelihood estimation, covariate- balancing propensity score estimation, or generalized boosting methods) provides the most efficient estimates with the least bias in a variety of estimation scenarios, 2) Determine which weighting/estimation strategy provides the best observed covariate balance (a secondary measure of propensity score performance) across multiple treatment levels in a variety of simulated estimation scenarios, and 3) Determine which weighting/estimation strategy is the least susceptible to residual confounding. Traditional Monte Carlo and plasmode (empirically based) simulations will be used to achieve the aims. To facilitate translation of results, we will repeat Aims 2 and 3 in empirical datasets with different sample sizes and expected treatment effect heterogeneity. Results will be verified by estimating effects of sedative-hypnotics on risk of in-hospital death in previously collected data from a study of 100,000 hospitalized veterans with cancer, heart failure, chronic obstructive pulmonary disease, and/or HIV/AIDS and from a study of 300,000 veterans with an opioid prescription. We expect to identify patterns of superior performance for strategies in common estimation scenarios as well as scenarios in which inferences are most likely to diverge. We will develop training materials based on our results and work with an advisory committee of leaders in observational data analysis to disseminate these results widely and inform studies of non-randomized health care interventions (such as post-hospitalization referral to Geri-PACT) as well as studies using VHA “big data” resources.