Pub Date : 2022-03-01DOI: 10.1177/1536867X221083856
Ariel Linden
In this article, I introduce two commands for computing the fragility index (FI): fragility, which is used for individual randomized controlled trials, and metafrag, which is used for meta-analyses. The FI for individual studies is defined as the minimum number of patients whose status would have to change from a nonevent to an event to nullify a statistically significant result. Correspondingly, the FI for meta-analyses is defined as the minimum number of patients from one or more trials included in the meta-analysis for which a modification of the event status (that is, changing events to nonevents or nonevents to events) would change the statistical significance of the pooled treatment effect to nonsignificant. Whether for an individual study or for a meta-analysis, a low FI indicates a more “fragile” study result, and a larger FI indicates a more robust result.
{"title":"Computing the fragility index for randomized trials and meta-analyses using Stata","authors":"Ariel Linden","doi":"10.1177/1536867X221083856","DOIUrl":"https://doi.org/10.1177/1536867X221083856","url":null,"abstract":"In this article, I introduce two commands for computing the fragility index (FI): fragility, which is used for individual randomized controlled trials, and metafrag, which is used for meta-analyses. The FI for individual studies is defined as the minimum number of patients whose status would have to change from a nonevent to an event to nullify a statistically significant result. Correspondingly, the FI for meta-analyses is defined as the minimum number of patients from one or more trials included in the meta-analysis for which a modification of the event status (that is, changing events to nonevents or nonevents to events) would change the statistical significance of the pooled treatment effect to nonsignificant. Whether for an individual study or for a meta-analysis, a low FI indicates a more “fragile” study result, and a larger FI indicates a more robust result.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45000835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/1536867x221083849
N. Cox, S. Jenkins
{"title":"Announcement of the Stata Journal Editors’ Prize 2022","authors":"N. Cox, S. Jenkins","doi":"10.1177/1536867x221083849","DOIUrl":"https://doi.org/10.1177/1536867x221083849","url":null,"abstract":"","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41403229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/1536867X221083901
B. Shaw
The statistical literature is replete with calls to report standardized measures of effect size alongside traditional p-values and null hypothesis tests. While effect-size measures such as Cohen’s d and Hedges’s g are straightforward to calculate for t tests, this is not the case for parameters in more complex linear models, where traditional effect-size measures such as η 2 and ω 2 face limitations. After a review of effect sizes and their implementation in Stata, I introduce the community-contributed command mces. This postestimation command reports standardized effect-size statistics for dichotomous comparisons of marginal-effect contrasts obtained from margins and mimrgns, including with complex samples, for continuous outcome variables. mces provides Stata users the ability to report straightforward estimates of effect size in many modeling applications.
{"title":"Effect sizes for contrasts of estimated marginal effects","authors":"B. Shaw","doi":"10.1177/1536867X221083901","DOIUrl":"https://doi.org/10.1177/1536867X221083901","url":null,"abstract":"The statistical literature is replete with calls to report standardized measures of effect size alongside traditional p-values and null hypothesis tests. While effect-size measures such as Cohen’s d and Hedges’s g are straightforward to calculate for t tests, this is not the case for parameters in more complex linear models, where traditional effect-size measures such as η 2 and ω 2 face limitations. After a review of effect sizes and their implementation in Stata, I introduce the community-contributed command mces. This postestimation command reports standardized effect-size statistics for dichotomous comparisons of marginal-effect contrasts obtained from margins and mimrgns, including with complex samples, for continuous outcome variables. mces provides Stata users the ability to report straightforward estimates of effect size in many modeling applications.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45812101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/1536867X221083886
Daoping Wang, Kerui Du, Ning Zhang
In this article, we introduce two community-contributed data envelopment analysis commands for measuring technical efficiency and productivity change in Stata. Over the last decades, an important theoretical progression of data envelopment analysis, a nonparametric method widely used to assess the performance of decision-making units, is the incorporation of undesirable outputs. Models able to deal with undesirable outputs have been developed and applied in empirical studies for assessing the sustainability of decision-making units. These models are getting more and more attention from researchers and managers. The teddf command discussed in the present article allows users to measure technical efficiency, both radial and nonradial, when some outputs are undesirable. Technical efficiency measures are obtained by solving linear programming problems. The gtfpch command we also describe here provides tools for measuring productivity change, for example, the Malmquist–Luenberger index and the Luenberger indicator. We provide a brief overview of the nonparametric efficiency and productivity change measurement accounting for undesirable outputs, and we describe the syntax and options of the new commands. We also illustrate with examples how to perform the technical efficiency and productivity analysis with the newly introduced commands.
{"title":"Measuring technical efficiency and total factor productivity change with undesirable outputs in Stata","authors":"Daoping Wang, Kerui Du, Ning Zhang","doi":"10.1177/1536867X221083886","DOIUrl":"https://doi.org/10.1177/1536867X221083886","url":null,"abstract":"In this article, we introduce two community-contributed data envelopment analysis commands for measuring technical efficiency and productivity change in Stata. Over the last decades, an important theoretical progression of data envelopment analysis, a nonparametric method widely used to assess the performance of decision-making units, is the incorporation of undesirable outputs. Models able to deal with undesirable outputs have been developed and applied in empirical studies for assessing the sustainability of decision-making units. These models are getting more and more attention from researchers and managers. The teddf command discussed in the present article allows users to measure technical efficiency, both radial and nonradial, when some outputs are undesirable. Technical efficiency measures are obtained by solving linear programming problems. The gtfpch command we also describe here provides tools for measuring productivity change, for example, the Malmquist–Luenberger index and the Luenberger indicator. We provide a brief overview of the nonparametric efficiency and productivity change measurement accounting for undesirable outputs, and we describe the syntax and options of the new commands. We also illustrate with examples how to perform the technical efficiency and productivity analysis with the newly introduced commands.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43665471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1177/1536867X211063143
H. Newton, N. Cox
Mark Edwin Schaffer was born in 1959 and grew up in New Jersey and Arizona. He received an AB degree in social studies magna cum laude from Harvard University in 1982 and master’s and PhD degrees in economics from the London School of Economics (LSE), Stanford, and LSE again. After teaching and research posts at Sussex and LSE, Schaffer joined Heriot-Watt University in Edinburgh in 1995. He has since occupied many senior management, research, and consultancy roles at Heriot-Watt, across consortia of Scottish universities, and internationally. Honors include election as a Fellow of the Royal Society of Edinburgh in 2009 and as a Fellow of the Royal Society of Arts in 2017.
Mark Edwin Schaffer出生于1959年，在新泽西州和亚利桑那州长大。他于1982年以优异成绩获得哈佛大学社会研究学士学位，并在斯坦福大学、伦敦经济学院和伦敦经济学院获得经济学硕士和博士学位。在苏塞克斯和伦敦经济学院担任教学和研究职位后，夏弗于1995年加入爱丁堡的赫瑞瓦特大学。此后，他在赫瑞瓦特大学(Heriot-Watt)、苏格兰大学财团和国际上担任过许多高级管理、研究和咨询职位。荣誉包括2009年当选为爱丁堡皇家学会会员，2017年当选为皇家艺术学会会员。
{"title":"The Stata Journal Editors’ Prize 2021: Mark E. Schaffer","authors":"H. Newton, N. Cox","doi":"10.1177/1536867X211063143","DOIUrl":"https://doi.org/10.1177/1536867X211063143","url":null,"abstract":"Mark Edwin Schaffer was born in 1959 and grew up in New Jersey and Arizona. He received an AB degree in social studies magna cum laude from Harvard University in 1982 and master’s and PhD degrees in economics from the London School of Economics (LSE), Stanford, and LSE again. After teaching and research posts at Sussex and LSE, Schaffer joined Heriot-Watt University in Edinburgh in 1995. He has since occupied many senior management, research, and consultancy roles at Heriot-Watt, across consortia of Scottish universities, and internationally. Honors include election as a Fellow of the Royal Society of Edinburgh in 2009 and as a Fellow of the Royal Society of Arts in 2017.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48032562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1177/1536867X211063405
Christopher F. Baum, Jesús Otero
We present a new command, radf, that tests for explosive behavior in time series. The command computes the right-tail augmented Dickey and Fuller (1979, Journal of the American Statistical Association 74: 427–431) unitroot test and its further developments based on supremum statistics derived from augmented Dickey–Fuller-type regressions estimated using recursive windows (Phillips, Wu, and Yu, 2011, International Economic Review 52: 201–226) and recursive flexible windows (Phillips, Shi, and Yu, 2015, International Economic Review 56: 1043–1078). It allows for the lag length in the test regression and the width of rolling windows to be either specified by the user or determined using data-dependent procedures, and it performs the date-stamping procedures advocated by Phillips, Wu, and Yu (2011) and Phillips, Shi, and Yu (2015) to identify episodes of explosive behavior. It also implements the wild bootstrap proposed by Phillips and Shi (2020, Handbook of Statistics: Financial, Macro and Micro Econometrics Using R, Vol. 42, 61–80) to lessen the potential effects of unconditional heteroskedasticity and account for the multiplicity issue in recursive testing. The use of radf is illustrated with an empirical example.
我们提出了一个新的命令，radf，用于测试时间序列的爆炸行为。该命令计算右尾增广Dickey和Fuller (1979, Journal of American Statistical Association, 74: 427-431)单位根检验及其进一步发展，该检验基于使用递归窗口(Phillips, Wu, and Yu, 2011, International Economic Review, 52: 201-226)和递归灵活窗口(Phillips, Shi, and Yu, 2015, International Economic Review, 56: 1043-1078)估计的增广Dickey - Fuller型回归得出的最大统计量。它允许测试回归的滞后长度和滚动窗口的宽度由用户指定或使用数据依赖程序确定，并且它执行Phillips, Wu, and Yu(2011)和Phillips, Shi, and Yu(2015)倡导的日期戳程序来识别爆炸行为的情节。它还实现了Phillips和Shi (2020, Handbook of Statistics: Financial, Macro and Micro Econometrics Using R, Vol. 42, 61-80)提出的野bootstrap，以减少无条件异方差的潜在影响，并解释递归检验中的多重性问题。通过一个实例说明了radf的应用。
{"title":"Unit-root tests for explosive behavior","authors":"Christopher F. Baum, Jesús Otero","doi":"10.1177/1536867X211063405","DOIUrl":"https://doi.org/10.1177/1536867X211063405","url":null,"abstract":"We present a new command, radf, that tests for explosive behavior in time series. The command computes the right-tail augmented Dickey and Fuller (1979, Journal of the American Statistical Association 74: 427–431) unitroot test and its further developments based on supremum statistics derived from augmented Dickey–Fuller-type regressions estimated using recursive windows (Phillips, Wu, and Yu, 2011, International Economic Review 52: 201–226) and recursive flexible windows (Phillips, Shi, and Yu, 2015, International Economic Review 56: 1043–1078). It allows for the lag length in the test regression and the width of rolling windows to be either specified by the user or determined using data-dependent procedures, and it performs the date-stamping procedures advocated by Phillips, Wu, and Yu (2011) and Phillips, Shi, and Yu (2015) to identify episodes of explosive behavior. It also implements the wild bootstrap proposed by Phillips and Shi (2020, Handbook of Statistics: Financial, Macro and Micro Econometrics Using R, Vol. 42, 61–80) to lessen the potential effects of unconditional heteroskedasticity and account for the multiplicity issue in recursive testing. The use of radf is illustrated with an empirical example.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44663169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1177/1536867X211063410
A. MacIsaac, Bruce Weaver
In this article, we review Interpreting and Visualizing Regression Models Using Stata, Second Edition, by Michael N. Mitchell (2021, Stata Press).
在这篇文章中，我们回顾解释和可视化回归模型使用Stata，第二版，由Michael N. Mitchell (2021, Stata出版社)。
{"title":"Review of Michael N. Mitchell’s Interpreting and Visualizing Regression Models Using Stata, Second Edition","authors":"A. MacIsaac, Bruce Weaver","doi":"10.1177/1536867X211063410","DOIUrl":"https://doi.org/10.1177/1536867X211063410","url":null,"abstract":"In this article, we review Interpreting and Visualizing Regression Models Using Stata, Second Edition, by Michael N. Mitchell (2021, Stata Press).","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41324905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1177/1536867X211063413
N. Cox
{"title":"Stata tip 144: Adding variable text to graphs that use a by() option","authors":"N. Cox","doi":"10.1177/1536867X211063413","DOIUrl":"https://doi.org/10.1177/1536867X211063413","url":null,"abstract":"","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45014011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1177/1536867X211063407
B. Shaw
Researchers and psychometricians have long used Cronbach’s α as a measure of reliability. However, there have been growing calls to replace Cronbach’s α with measures that have more defensible assumptions. One of the most common and straightforward recommended reliability estimates is ω. After a review of reliability and its estimation in Stata, I introduce the community-contributed command omegacoef. This command reports McDonald’s ω in a format similar to the base alpha command. omegacoef provides Stata users the ability to easily compute estimates of reliability with the confidence that the necessary statistical assumptions are met.
{"title":"Meeting assumptions in the estimation of reliability","authors":"B. Shaw","doi":"10.1177/1536867X211063407","DOIUrl":"https://doi.org/10.1177/1536867X211063407","url":null,"abstract":"Researchers and psychometricians have long used Cronbach’s α as a measure of reliability. However, there have been growing calls to replace Cronbach’s α with measures that have more defensible assumptions. One of the most common and straightforward recommended reliability estimates is ω. After a review of reliability and its estimation in Stata, I introduce the community-contributed command omegacoef. This command reports McDonald’s ω in a format similar to the base alpha command. omegacoef provides Stata users the ability to easily compute estimates of reliability with the confidence that the necessary statistical assumptions are met.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48766745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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