Mean Cost and Cost-Effectiveness Ratios with Censored Data: a Tutorial and SAS® Macros.

Q3 Medicine
Biostatistics and Epidemiology Pub Date : 2025-01-01 Epub Date: 2025-10-06 DOI:10.1080/24709360.2025.2565537
Eduard Poltavskiy, Dingning Liu, Shuai Chen, Heejung Bang, Hongwei Zhao
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引用次数: 0

Abstract

Censoring is an unignorable issue when analyzing survival data and/or medical cost data. Medical costs may be viewed as a type of survival data-in that they accrue over time until an endpoint such as death-or a 'mark' variable. Since Lin et al. (1997) and Mushlin et al. (1998) published landmark papers on this topic, censored cost data have been extensively studied. In this tutorial, we explain how to estimate mean cost and cost-effectiveness ratios, along with three examples under two different data scenarios: when only total cost data (so one observation per person) or longitudinal data (or cost history) are available. We also provide an updated literature review. SAS codes in the supplement could be useful to practitioners and data analysts.

平均成本和成本效益比与删节数据:教程和SAS®宏。
在分析生存数据和/或医疗费用数据时,审查是一个不可忽视的问题。医疗费用可以被视为一种生存数据,因为它们随着时间的推移而累积,直到一个终点(如死亡)或一个“标记”变量。自从Lin等人(1997)和Mushlin等人(1998)发表了关于这一主题的具有里程碑意义的论文以来,审查成本数据得到了广泛的研究。在本教程中,我们将解释如何估计平均成本和成本效益比率,并提供两种不同数据场景下的三个示例:只有总成本数据(即每人一次观察)或纵向数据(或成本历史)可用。我们还提供了最新的文献综述。附录中的SAS代码可能对从业人员和数据分析人员有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biostatistics and Epidemiology
Biostatistics and Epidemiology Medicine-Health Informatics
CiteScore
1.80
自引率
0.00%
发文量
23
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