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. 2018 Dec 30;38(12):1503-1508.
doi: 10.12122/j.issn.1673-4254.2018.12.17.

[Subgroup identification based on the Logistic model]

[Article in Chinese]
Yanhong Zhang  1   2 , Xueyuan Li  3 , Zhijian Wang  4 , Shengli An  1
Affiliations

[Subgroup identification based on the Logistic model]

[Article in Chinese]
Yanhong Zhang et al. Nan Fang Yi Ke Da Xue Xue Bao. .

Abstract
in English, Chinese

We propose a subgroup identification method based on the Logistic model for data from a two-arm clinical trial with dichotomous outcome variables.In this method, binary Logistic regression models are established for each group to calculate the outcome probabilities of each patient for comparison.According to the established rules, the patients are classified into their corresponding subgroups to establish a multinomial Logistic regression model.We simulated the false rate, correct judgment rate, coincidence rate and model correct judgment rate for different sample sizes and carried out an example analysis.The results of simulation showed that for different sample sizes, the false rates of this method were below 0.07 and the correct judgment rates were all above 0.75 with adequate coincidence rates and model correct judgment rates, demonstrating the effectiveness and reliability of the proposed method for subgroup identification.

目的: 基于Logistic模型, 提出一种适用于结局为二分类变量的阳性对照、双臂临床试验的亚组识别方法, 并利用亚组相关协变量建立亚组人群判别模型。

方法: 首先基于两处理组, 分别建立二分类Logistic回归模型, 计算出每一个患者在不同处理组内的阳性概率并进行比较, 然后根据所建立的规则将患者分到各自对应的亚组中, 建立多分类Logistic模型。模拟不同样本量下的错判率、正确判断率、符合率和模型判对率且进行实例分析。

结果: 不同样本量下, 错判率均在0.07以下; 正确判断率均达到0.75以上; 符合率和模型判对率分别在72%以及92%以上。

结论: 本研究所提出的亚组识别方法是一种有效可靠的亚组识别方法。

Keywords: Logistic regression; Monte-Carlo simulation; clinical trials; subgroup identification.

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Figures

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1
错判率与样本量n的关系 Relationship between false rate and the sample size n.
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正确判断率、符合率和模型判对率与样本量n的关系 Relationship of correct judgment rate (CJR), coincidence rate (CR), and model correct judgment rate (MCJR) with the sample size n.
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考核数据集符合率和判对率与样本量n的关系 Relationship of the coincidence rate (CR) and model correct judgment rate (MCJR) with the sample size n.
See this image and copyright information in PMC

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