第三卷.Stata最新且急需的程序系列汇编

凡是搞计量经济的，都关注这个号了

投稿：econometrics666@126.com

摘要：随机对照实验的事件结果通常用来分析既定比例风险(PH)的处理效果。样本大小的计算是基于log-rank检验或几乎相同的Cox检验，因此称为Cox/log-rank检验。然而，非比例风险(非PH)在实验中越来越常见，且其减少了处理效果和log-rank实验的解释力度，从而影响实验的成功。为解决此问题，Royston和Parmar（2016）提出了“联合检验”，即对每个实验相同生存曲线的全球无效性假设进行检验。Cox/log-rank检验与一种新的检验相结合，这种新检验源于两组试验对象在限制平均生存时间（RMST）时的最大标准化差异，测试数据是基于对几个预先选择时间点的RMST组间差异评估。联合检验涉及Cox/log-rank和RMST检验的最小p值，适当地标准化以在全球无效性假设下得到正确的分布。本文将介绍一个新的命令power_ct，它用来模拟联合检验的功效和样本大小的计算。power_ct支持PH或非PH处理效果，本文在PH和非PH情况下将联合检验与Cox/log-rank检验进行比较。最后，本文提供了样本大小的计算指南，以便在事件发生前实验时考虑到可能的非PH。

Abstract: Randomized controlled trials with a time-to-event outcome are usually designed and analyzed assuming proportional hazards (PH) of the treatment effect. The sample-size calculation is based on a log-rank test or the nearly identical Cox test, henceforth called the Cox/log-rank test. Nonproportional hazards (non-PH) has become more common in trials and is recognized as a potential threat to interpreting the trial treatment effect and the power of the log-rank test—hence to the success of the trial. To address the issue, in 2016, Royston and Parmar (BMC Medical Research Methodology 16: 16) proposed a “combined test” of the global null hypothesis of identical survival curves in each trial arm. The Cox/log-rank test is combined with a new test derived from the maximal standardized difference in restricted mean survival time (RMST) between the trial arms. The test statistic is based on evaluations of the between-arm difference in RMST over several preselected time points. The combined test involves the minimum p-value across the Cox/log-rank and RMST-based tests, appropriately standardized to have the correct distribution under the global null hypothesis. In this article, I introduce a new command, power_ct, that uses simulation to implement power and sample-size calculations for the combined test. power_ct supports designs with PH or non-PH of the treatment effect. I provide examples in which the power of the combined test is compared with that of the Cox/log-rank test under PH and non-PH scenarios. I conclude by offering guidance for sample-size calculations in time-to-event trials to allow for possible non-PH.

摘要：本文介绍了adfmaxur命令，它用来对观察值的数量和回归中因变量的滞后数进行Leybourne(1995，《牛津经济与统计公报》57:559-571)单位根统计。后者既可以由使用者指定，也可以使用默认设置。本文用一个实例来说明adfmaxur命令的使用。

Abstract: In this article, we present the command adfmaxur, which computes the Leybourne (1995, Oxford Bulletin of Economics and Statistics 57: 559–571) unit-root statistic for different numbers of observations and the number of lags of the dependent variable in the test regressions. The latter can be either specified by the user or endogenously determined. We illustrate the use of adfmaxurwith an empirical example.

摘要：主观测量量表常常用于测量临床研究、教育科学或心理学等不可观测被访者特征的领域。为确定问卷有效，问卷的得分结果必须经过验证，也就是说，必须证明心理测量的有效性、可靠性和敏感性。在本文中，我们给出了validscale命令，它提供相应的统计分析来验证主观测量量表的有效性。我们还开发了一个对话框，validscale命令不久就可在Stata中进行操作。

Abstract: Subjective measurement scales are used to measure nonobservable respondent characteristics in several fields such as clinical research, educational sciences, or psychology. To be useful, the scores resulting from the questionnaire must be validated; that is, they must provide the psychometric properties validity, reliability, and sensitivity. In this article, we present the validscale command, which carries out the required statistical analyses to validate a subjective measurement scale. We have also developed a dialog box, and validscale will soon be implemented online with Numerics by Stata.

摘要：在本文中，我们描述了betamix命令，它适用于在一个区间内对有界因变量的混合回归模型。该模型是在Pereira、Botter和Sandoval(2012，Communications in Statistics—Theory and Methods41:907-919)中引入的截断膨胀贝塔回归模型的推广，Verkuilen和Smithson (2012, Journal of Educational and Behavioral Statistics 37: 82-113)的混合贝塔回归模型用于在分布的顶部或底部截断变量。betamix接受定义在任何范围内的因变量，只要在估计之前将其转换为区间(0,1)即可。

Abstract: In this article, we describe the betamix command, which fits mixture regression models for dependent variables bounded in an interval. The model is a generalization of the truncated inflated beta regression model introduced in Pereira, Botter, and Sandoval (2012, Communications in Statistics—Theory and Methods 41: 907–919) and the mixture beta regression model in Verkuilen and Smithson (2012, Journal of Educational and Behavioral Statistics 37: 82–113) for variables with truncated supports at either the top or the bottom of the distribution. betamix accepts dependent variables defined in any range that are then transformed to the interval (0, 1) before estimation.

摘要：目前面板模型的序列相关检验使用起来很麻烦，不适合固定效应模型，仅局限于一阶自相关。为了填补这个空白，本文最近新开发了三个测试。

Abstract: Current serial correlation tests for panel models are cumbersome to use, not suited for fixed-effects models, or limited to first-order autocorrelation. To fill this gap, I implement three recently developed tests.

本文介绍ldagibbs命令，它在Stata中实现了潜在的Dirichlet分配。潜在Dirichlet分配是最流行的机器学习主题模型。该模型自动将文本文档聚集到用户选择的主题数量中，潜在Dirichlet分配将每个文档表示为主题的概率分布，将每个主题表示为单词上的概率分布。因此，潜在Dirichlet分配提供了一种分析大量未分类文本数据内容的方法和一种预定义文档分类的替代方法。

In this article, I introduce the ldagibbs command, which implements latent Dirichlet allocation in Stata. Latent Dirichlet allocation is the most popular machine-learning topic model. Topic models automatically cluster text documents into a user-chosen number of topics. Latent Dirichlet allocation represents each document as a probability distribution over topics and represents each topic as a probability distribution over words. Therefore, latent Dirichlet allocation provides a way to analyze the content of large unclassified text data and an alternative to predefined document classifications.

在水平和收益都显示的设置中，边际处理效果(MTE)允许我们超越局部平均处理效果并估计整个效果分布。本文概述了MTE背后的理论，并介绍了mtefe，它使用几种估计方法来拟合MTE模型。mtefe比现有的margte (Brave and Walstrum, 2014, Stata Journal 14: 191–217)更加先进和灵活，并能根据结果计算各种处理效应参数。我用几个例子来说明mtefe的用法。

In settings that exhibit selection on both levels and gains, marginal treatment effects (MTE) allow us to go beyond local average treatment effects and estimate the whole distribution of effects. In this article, I survey the theory behind MTE and introduce the package mtefe, which uses several estimation methods to fit MTE models. This package provides important improvements and flexibility over existing packages such as margte (Brave and Walstrum, 2014, Stata Journal 14: 191–217) and calculates various treatment-effect parameters based on the results. I illustrate the use of the package with examples.

在本文中，我们引入了社区贡献指令randcoef，它适用于Suri (2011, Econometrica 79: 159-209)中讨论的相关随机效应和相关随机系数模型。虽然这种方法已经存在了10年，但它的使用受到最优最小距离复杂的估计过程的限制。randcoef可以容纳最多5轮的面板数据，并提供了几种选择，包括用于估计内生解释变量的替代权重矩阵。我们还使用样本数据进行后估计分析，以便于对结果的理解和解释。

In this article, we introduce the community-contributed command randcoef, which fits the correlated random-effects and correlated random-coefficient models discussed in Suri (2011, Econometrica 79: 159–209). While this approach has been around for a decade, its use has been limited by the computationally intensive nature of the estimation procedure that relies on the optimal minimum distance estimator. randcoef can accommodate up to five rounds of panel data and offers several options, including alternative weight matrices for estimation and inclusion of additional endogenous regressors. We also present postestimation analysis using sample data to facilitate understanding and interpretation of results.

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