Rising medical costs are an emerging challenge in policy decisions and resource allocation planning. When cumulative medical cost is the outcome, right censoring induces informative missingness due to heterogeneity in cost accumulation rates across subjects. Inverse weighting approaches have been developed to address the challenge of informative cost trajectories in mean cost estimation, though these approaches generally ignore post‐baseline treatment changes. In post‐hysterectomy endometrial cancer patients, data from a linked database of Medicare records and the Surveillance, Epidemiology, and End Results programme of the National Cancer Institute reveal substantial within‐subject variation in treatment over time. In such a setting, the utility of existing intent‐to‐treat approaches is generally limited. Estimates of the population mean cost under a hypothetical time‐varying treatment regime can better assist with resource allocation when planning for a treatment policy change; such estimates must inherently take time‐dependent treatment and confounding into account. We develop a nested g‐computation approach to cost analysis to address this challenge, while accounting for censoring. We develop a procedure to evaluate sensitivity to departures from baseline treatment ignorability. We further conduct a variety of simulations and apply our nested g‐computation procedure to 2‐year costs from endometrial cancer patients.

中文翻译：

---

嵌套g运算：因时变治疗而分析被审查的医疗费用的因果方法

医疗费用上涨是政策决策和资源分配计划中的新兴挑战。当以累积医疗费用为结果时，由于各个受试者之间费用累积率的异质性，正确的审查会导致信息缺失。已经开发了反向加权方法来应对平均成本估算中信息成本轨迹的挑战，尽管这些方法通常会忽略基线后的治疗变化。在子宫切除术后子宫内膜癌患者中，来自医疗保险记录和美国国家癌症研究所的监测，流行病学和最终结果计划的链接数据库中的数据显示，随着时间的流逝，受试者之间存在很大的差异。在这种情况下，现有意图治疗方法的效用通常受到限制。在计划改变治疗政策时，假设的时变治疗方案下的人口平均成本估算可以更好地协助资源分配。这样的估计必须固有地考虑时间依赖性的处理和混淆。我们开发了一个嵌套g成本分析的计算方法，以应对这一挑战，同时应对审查。我们开发了一种程序来评估对偏离基线治疗可忽略性的敏感性。我们进一步进行了各种模拟，并将我们的嵌套g计算程序应用于子宫内膜癌患者的两年费用。