Heterogeneity in Treatment Effect Timing in Geriatrics and Palliative Care Studies

背景：在现实世界的老年和姑息治疗计划，政策或治疗，治疗 可能需要以调查人员无法控制的方式在各单位之间交错启动。差异是否 与治疗时机和结果相关的组织特征， 如果在分析中加以控制，它们可能会掩盖真实治疗效果的估计。非均相处理 在大多数现有的方法中没有充分解决定时问题。目前用于解释治疗效果的方法 时间异质性不允许将治疗效果与相关混杂因素的效果隔离开来 时间和结果都有关系 意义：政策和实践变化的交错推出在VA中很常见-时间为 其中新的干预（例如，退伍军人指导护理）的推出不能总是控制。 创新和影响：一个潜在的解决方案涉及治疗权重的逆概率（IPTW），以调整 根据接受时间定义的治疗组间混杂，但IPTW导致以下方面的偏倚估计值： 比较多种治疗的横断面评价。我们开发了一种替代方法，向量- 基于核加权（VBKW），在横截面评估中优于IPTW。的程度 VBKW在纵向应用中比IPTW减少偏差并提高效率尚未被探索。 熵平衡（EB）权重也可能产生无偏和有效的治疗效果估计， 结合DD。研究人员需要实际的指导，当加权调整与DD可能是一个 解释差异中差异研究中治疗时间异质性的上级分析方法，或 当传统的双向固定效应（TWFE）方法可能是足够的。 具体目标：我们的目标是比较使用VBKW、IPTW、EB和TWFE进行估计的偏倚和效率 队列内治疗时间交错的回顾性队列研究分析方法（目的1） 以及来自纵向小组研究的数据，其中治疗效果可能在数据队列之间和队列内存在差异 收集（目标2）。我们将确定VBKW、IPTW、EB和TWFE所需的异质性程度， 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倚、效率、协变量 平衡，以及从交错的观察性研究中获得的数据中每种策略的处理时间 治疗时机MC模拟的振荡器产生的数据（n=600，900，9600）将允许我们检查 不同分析方案的影响（例如，治疗时间组间样本分布，动态 影响）对估计量的相对性能。等离子体模模拟将使我们能够验证我们的结果 是强大的数据生成过程，将来自退伍军人的自我观察分析， 直接护理服务（使用企业数据仓库数据）和健康与退休研究 （HRS）。 后续步骤/实施：我们将确定VBKW、EB、IPTW或TWFE上级 在不同的时间提供的治疗效果，并总结我们的结果在实践中的指导，我们将 与HSRD社区分享。我们的研究结果将提高调查人员产生严格证据的能力 从退伍军人的老年病和姑息治疗的研究中，治疗或事件的时间不能控制。

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.