Proportionally Less Difficult?: Reevaluating Keele’s “Proportionally Difficult”

IF 4.7 2区 社会学 Q1 POLITICAL SCIENCE Political Analysis Pub Date : 2022-06-20 DOI:10.1017/pan.2022.13
Shawna K. Metzger
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引用次数: 2

Abstract

Abstract Keele (2010, Political Analysis 18:189–205) emphasizes that the incumbent test for detecting proportional hazard (PH) violations in Cox duration models can be adversely affected by misspecified covariate functional form(s). In this note, I reevaluate Keele’s evidence by running a full set of Monte Carlo simulations using the original article’s illustrative data-generating processes (DGPs). I make use of the updated PH test calculation available in R’s survival package starting with v3.0-10. Importantly, I find the updated PH test calculation performs better for Keele’s DGPs, suggesting its scope conditions are distinct and worth further investigating. I also uncover some evidence for the traditional calculation suggesting it, too, may have additional scope conditions that could impact practitioners’ interpretation of Keele (2010). On the whole, while we should always be attentive to model misspecification, my results suggest we should also become more attentive to how frequently the PH test’s performance is affected in practice, and that the answer may depend on the calculation’s implementation.
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比例难度更低?:重新评价基尔的“比例难度”
Keele (2010, Political Analysis 18:189-205)强调,在Cox持续时间模型中,检测比例风险(PH)违规的在位检验可能会受到错误指定的协变量函数形式的不利影响。在本文中,我通过使用原始文章的说明性数据生成过程(dpp)运行一整套蒙特卡罗模拟来重新评估Keele的证据。我使用从v3.0-10开始的R生存包中提供的更新的PH测试计算。重要的是,我发现更新后的PH测试计算对Keele的dpps有更好的表现,这表明它的范围条件是独特的,值得进一步研究。我还发现了一些传统计算的证据,表明它也可能有额外的范围条件,可能影响从业者对Keele(2010)的解释。总的来说,虽然我们应该始终注意模型的错误规范,但我的结果表明,我们也应该更加注意PH测试的性能在实践中受到影响的频率,而答案可能取决于计算的实现。
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来源期刊
Political Analysis
Political Analysis POLITICAL SCIENCE-
CiteScore
8.80
自引率
3.70%
发文量
30
期刊介绍: Political Analysis chronicles these exciting developments by publishing the most sophisticated scholarship in the field. It is the place to learn new methods, to find some of the best empirical scholarship, and to publish your best research.
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