Nested g-computation: A causal approach to analysis of censored medical costs in the presence of time-varying treatment.
Nested g-computation: A causal approach to analysis of censored medical costs in the presence of time-varying treatment.
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DOI：
10.1111/rssc.12441
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发表时间：
2020-11
期刊：
影响因子：
--
通讯作者：
Mitra N
中科院分区：
文献类型：
--
作者：
Spieker AJ;Ko EM;Roy JA;Mitra N
医疗费用上涨是政策决定和资源分配规划方面的一个新挑战。当累积医疗费用是结果时，由于受试者之间的费用累积率的异质性，右删失会导致信息缺失。已经开发了反加权方法来解决平均成本估计中信息成本轨迹的挑战，尽管这些方法通常忽略基线后治疗变化。在子宫切除术后的子宫内膜癌患者中，来自医疗保险记录和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的数据显示，随着时间的推移，受试者内的治疗差异很大。在这种情况下，现有的意向治疗方法的效用通常是有限的。当计划改变治疗政策时，根据假设的随时间变化的治疗方案估计人群平均成本可以更好地帮助资源分配;这种估计必须固有地考虑到时间依赖性治疗和混杂因素。在本文中，我们开发了一个嵌套的g-计算方法的成本分析，以解决这一挑战，同时占审查。我们开发了一个程序，以评估敏感性偏离基线治疗可行性。我们进一步进行各种模拟，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。
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 program 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 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. In this paper, 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 two-year costs from endometrial cancer patients.
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