Heterogeneity in Treatment Effect Timing in Geriatrics and Palliative Care Studies

背景：在老年和姑息治疗方案、政策或治疗、治疗的真实世界研究中 启动可能需要以调查人员无法控制的方式在各个单元之间错开。如果不同 跨队列或在与治疗时机和结果相关的组织特征中 在分析中加以控制，它们可能会模糊对真实治疗效果的估计。异质治疗 在大多数现有的方法中，时间问题都没有得到充分的解决。目前对治疗效果进行解释的方法 时间的异质性不允许将治疗的效果与相关混杂因素的影响分开 既有时机，也有结果。 意义：政策和实践变化的交错推出在退伍军人管理局内很常见-时间安排在 新的干预措施(例如，退伍军人定向护理)的推出并不总是可控的。 创新和影响：潜在的解决方案包括调整治疗权重的反向概率(IPTW) 用于通过接收时间定义的跨治疗组的混淆，但IPTW导致在 比较多种治疗方法的横断面评估。我们开发了另一种方法，矢量法- 基于核加权(VBKW)，在横截面评估中优于IPTW。在多大程度上 与IPTW相比，VBKW在纵向应用中减少了偏置并提高了效率，但尚未被探索。 在以下情况下，熵平衡(EB)权重也可以产生无偏和有效的治疗效果估计 与DD结合。研究人员需要实用的指导，以确定何时使用DD调整权重可能是 在差异差异研究中使用更好的分析方法来解释治疗时机的异质性，或 因为当传统的双向固定效应(TWFE)方法可能足够时。 具体目标：我们的目标是比较使用VBKW、IPTW、EB和TWFE估计的偏差和效率 在一个队列内错开治疗时间的回溯性队列研究的分析方法(目标1) 以及来自纵向小组研究的数据，在这些研究中，治疗效果可能在数据队列之间和队列内有所不同 集合(目标2)。我们将确定VBKW、IPTW、EB和TWFE所需的异质性程度，以 导致不同的推论(目标3)。 方法：我们将使用蒙特卡罗(MC)和等离子体模式模拟来评估偏差、效率、协变量 从具有交错的观察性研究中获得的数据中的每种策略的平衡和处理时间 治疗时机。对调查员生成的数据(n=600、900、9600)进行MC模拟将使我们能够检查 不同分析情景的影响(例如，样本跨治疗时间组的分布、动态 效应)对估计器的相对性能产生影响。等离子体模式模拟将使我们能够验证我们的结果 对数据生成过程具有健壮性，并将通过对退伍军人自我的观察分析得出 定向护理服务(使用企业数据仓库数据)和健康和退休研究 (小时)。 下一步/实施：我们将确定VBKW、EB、IPTW或TWFE在评估 不同时间提供的治疗效果，并在实际指导中总结我们的结果，我们将 与HSRD社区共享。我们的结果将提高调查人员生成严格证据的能力 来自对退伍军人的老年和姑息治疗的研究，在这些研究中，治疗或事件时间无法控制。

Background: In real-world studies of geriatric and palliative care programs, policies, or treatments, treatment initiation may need to be staggered across units in ways that are outside of investigators’ control. If differences across cohorts or in organizational characteristics associated with both treatment timing and outcome are not controlled for in analyses, they may obscure estimates of true treatment effects. Heterogeneous treatment timing is inadequately addressed in most existing methods. Current methods to account for treatment effect timing heterogeneity do not allow a treatment’s effect to be isolated from effects of confounders associated with both timing and outcomes. Significance: Staggered rollouts of policies and practice changes are common within VA — the timing at which a new intervention (e.g., Veteran Directed Care) is rolled out cannot always be controlled. Innovation and Impact: A potential solution involves inverse probability of treatment weights (IPTW) to adjust for confounding across treatment groups defined by receipt timing, but IPTWs lead to biased estimates in cross-sectional evaluations comparing multiple treatments. We have developed an alternative method, vector- based kernel weighting (VBKW), that outperforms IPTW in cross-sectional evaluations. The degree to which VBKW reduces bias and improves efficiency over IPTW in longitudinal applications has not yet been explored. Entropy balancing (EB) weights also may produce unbiased and efficient treatment effect estimates when combined with DD. Researchers need practical guidance for when weighting adjustments with DD may be a superior analytic method to account for treatment timing heterogeneity in a difference-in-differences study, or for when traditional two-way fixed effects (TWFE) approaches may be sufficient. Specific Aims: We aim to compare bias and efficiency of estimates using VBKW, IPTW, EB, and TWFE approaches in analyses of retrospective cohort studies with staggered treatment timing within a cohort (Aim 1) and on data from longitudinal panel studies where treatment effects may vary across and within cohorts of data collection (Aim 2). We will identify the degree of heterogeneity required for VBKW, IPTW, EB, and TWFE to lead to different inferences (Aim 3). Methodology: We will use Monte Carlo (MC) and plasmode simulations to evaluate bias, efficiency, covariate balance, and processing time for each strategy in data obtained from observational studies with staggered treatment timing. MC simulations on investigator-generated data (n=600, 900, 9600) will allow us to examine the impact of different analytic scenarios (e.g., sample distribution across treatment timing groups, dynamic effects) on the relative performance of estimators. Plasmode simulations will allow us to verify that our results are robust to data generating process and will be derived from an observational analysis of Veterans’ self- directed care services (using Corporate Data Warehouse data) and from the Health and Retirement Study (HRS). Next Steps/Implementation: We will identify when VBKW, EB, IPTW, or TWFE is superior for estimating the effect of a treatment provided at different times and summarize our results in practical guidance that we will share with the HSRD community. Our results will improve investigators’ ability to generate rigorous evidence from studies of Veterans’ geriatric and palliative care in which treatment or event timing cannot be controlled.