成本效果统计：考虑患者分层因素的建议
Cost Effectiveness Statistic: A Proposal To Take Into Account The Patient Stratification Factors
摘要：
该方法可用于随机临床试验的经济学分析。它基于统计学方法，旨在计算增量成本效果比（ICER）的修订版本，以考虑可能影响治疗选择的关键因素，从而引起适应症混淆。让我们以一种治疗癌症的新疗法为例，将其与现有疗法进行比较，其有效性为死亡时间。一个具有挑战性的问题是，ICER是根据整个治疗组的平均值定义的。它没有规定按死亡风险不同的患者群体进行分层。例如，为了进行公平和无偏见的分析，人们希望比较具有相似预期寿命的组中的死亡时间，这些预期寿命会受到年龄，性别，疾病严重程度等因素的影响。我们决定应用的方法是通过聚类分析借用的，目的是（i）丢弃分析集中可能出现的任何离群值，（ii）找出有“相似”主要因素的病人组别（即群组）。
英文摘要：
The solution here proposed can be used to conduct economic analysis in randomized clinical trials. It is based on a statistical approach and aims at calculating a revised version of the incremental costeffective ratio (ICER) in order to take into account the key factors that can influence the choice of therapy causing confounding by indication. Let us take as an example a new therapy to treat cancer being compared to an existing therapy with effectiveness taken as time to death. A challenging problem is that the ICER is defined in terms of means over the entire treatment groups. It makes no provision for stratification by groups of patients with differing risk of death. For example, for a fair and unbiased analysis, one would desire to compare time to death in groups with similar life expectancy which would be impacted by factors such as age, gender, disease severity, etc. The method we decided to apply is borrowed by cluster analysis and aims at (i) discard any outliers in the set under analysis that may arise, (ii) identify groups (i.e. clusters) of patients with "similar" key factors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
