A Tutorial on Probabilistic Index Models: Regression Models for the Effect Size P(Y1 < Y2)
A Tutorial on Probabilistic Index Models: Regression Models for the Effect Size P(Y1 < Y2)
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DOI：
10.1037/met0000194
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发表时间：
2019-08-01
影响因子：
7
通讯作者：
De Neve, Jan
中科院分区：
文献类型：
--
作者：
De Schryver, Maarten;De Neve, Jan
概率指数（PI），也称为优效概率或通用语言效应量，是指随机选择的受试者的结局超过另一随机选择的受试者的结局的概率，条件是两个受试者的协变量值。该汇总指标有很长的历史，特别是对于2样本设计，其中协变量值通常指2种治疗中的1种。尽管PI具有一些吸引人的功能，但它通常不用于2样本设计之外。一个原因是缺乏一个灵活的回归框架，嵌入PI，并允许用户估计它更复杂的设计。然而，Thas，De Neve，Clement和Ottoy（2012）最近开发了这样一个回归框架，称为概率指数模型（PIM）。在本教程中，我们将介绍PIM，其中我们讨论了几个理论属性，动机为什么我们认为PIM可能对行为科学有用，并说明如何使用R包pim在实践中使用它。
The probabilistic index (PI), also known as the probability of superiority or the common language effect size, refers to the probability that the outcome of a randomly selected subject exceeds the outcome of another randomly selected subject, conditional on the covariate values of both subjects. This summary measure has a long history, especially for the 2-sample design where the covariate value typically refers to 1 of 2 treatments. Despite some of the attractive features of the PI, it is often not used beyond the 2-sample design. One reason is the lack of a flexible regression framework that embeds the PI and that allows the user to estimate it for more complicated designs. However, Thas, De Neve, Clement, and Ottoy (2012) recently developed such a regression framework, named probabilistic index models (PIMs). In this tutorial we provide an introduction to PIMs where we discuss several theoretical properties, motivate why we think PIMs could be useful for behavioral sciences, and illustrate how it can be used in practice using the R package pim.