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

越来越多的现有大型数据集(如老年病和长期护理数据和分析中心 [GEC-DAC]数据集)和前瞻性观测/准实验研究正在被用来检查 在重病老年人中的重要研究问题，并探索新的护理提供模式。 随机对照试验可能会给重病患者带来负担，或者无法进行，而且它们可能 不能产生对感兴趣的人群普遍适用的结果。老年医学的观测数据分析 姑息治疗必须考虑到严重的治疗内生性，当各种因素同时发生时 与治疗可能性和结果相关。倾向评分是解决内生性问题的一种方式。一个 倾向评分是根据一组观察到的协变量估计的接受治疗的概率 被认为与治疗可能性和结果有关的因素。不偏不倚的治疗效果 可以通过比较接受治疗的人和具有相似倾向得分的对照个人来估计。多数 对倾向分数的指导仅限于匹配具有相似倾向分数的个人的方法 两组(治疗组，不治疗组)。然而，许多治疗方法具有多个层次，并限制了 对二元指标的处理掩盖了组之间的差异。根据倾向性分数进行加权是一种 当有多个治疗组时，比匹配更好的选择。这项研究旨在最大限度地发展 使用倾向分数进行多模式治疗并增强研究人员的使用能力的实践 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。具体来说，这项研究将1) 使用模拟数据来确定哪种加权/估计组合(逆概率加权或 最大似然估计的回归估计的倾向性分数的核加权，协变量- 平衡倾向得分估计，或广义助推方法)提供最有效的估计 在各种估计方案中偏差最小的情况下，2)确定哪种加权/估计策略 提供最佳观察到的协变量平衡(倾向得分表现的次要衡量标准) 在各种模拟估计场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差混杂的影响。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。为了便于结果的翻译， 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和目标3 异质性。结果将通过评估镇静-催眠药对#年住院死亡风险的影响来验证。 之前收集的数据来自一项对10万名患有癌症、心力衰竭、慢性 阻塞性肺疾病和/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 处方药。我们期望在共同的评估中确定策略的优越性能模式 场景以及推论最有可能出现分歧的场景。我们将开发培训材料 基于我们的结果，并与观察性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(例如 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.