Template-Type: ReDIF-Article 1.0 Author-Name: Michael J. Crowther Author-Email: michael.crowther@ki.se Author-Workplace-Name: Karolinska Institutet Title: Simulating time-to-event data from parametric distributions, custom distributions, competing-risks models, and general multistate models Journal: Stata Journal Pages: 3-24 Issue: 1 Volume: 22 Year: 2022 Month: March X-DOI: 10.1177/1536867X221083853 Abstract: In this article, I describe some substantial extensions to the survsim command for simulating survival data. survsim can now simulate survival data from a parametric distribution, a custom or user-defined distribution, a fitted merlin model, a specified cause-specific hazards competing-risks model, or a spec- ified general multistate model (with multiple timescales). Left-truncation (delayed entry) is now also available for all settings. I illustrate the survsim command with some examples, demonstrating the huge flexibility that can be used to better eval- uate statistical methods. Keywords: survsim, survival analysis, Monte Carlo simulation, flexible parametric survival models, time to event, time to censoring, clinical trials, competing-risks models, multistate models, merlin File-URL: http://hdl.handle.net/10.1177/1536867X221083853 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-1/st0661/ Handle:RePEc:tsj:stataj:y:22:y:2022:i:1:p:3-24 Template-Type: ReDIF-Article 1.0 Author-Name: Marco Cococcioni Author-Workplace-Name: University of Pisa Author-Email: marco.cococcioni@unipi.it Author-Name: Marco Grazzi Author-Workplace-Name: Università Cattolica del Sacro Cuore Author-Email: marco.grazzi@unicatt.it Author-Person: pgr124 Author-Name: Le Li Author-Workplace-Name: Guangzhou College of Commerce Author-Email: lile7k@gmail.com Author-Name: Federico Ponchio Author-Workplace-Name: ISTI CNR Author-Email: federico.ponchio@isti.cnr.it Title: A toolbox for measuring heterogeneity and efficiency using zonotopes Journal: Stata Journal Pages: 25-59 Issue: 1 Volume: 22 Year: 2022 Month: March X-DOI: 10.1177/1536867X221083854 Abstract: In this work, we describe the new command zonotope, which, by resorting to a geometry-based approach, provides a measure of productivity that fully accounts for the existing heterogeneity across firms within the same industry. The method we propose also enables assessment of the extent of multidimensional het- erogeneity with applications to fields beyond that of production analysis. Finally, we detail the functioning of the software to perform the related empirical analysis, and we discuss the main computational issues encountered in its development. Keywords: zonotope, heterogeneity measures, production analysis, multivariate Gini coefficient File-URL: http://hdl.handle.net/10.1177/1536867X221083854 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-1/st0662/ Handle:RePEc:tsj:stataj:y:22:y:2022:i:1:p:25-59 Template-Type: ReDIF-Article 1.0 Author-Name: Luca J. Uberti Author-Workplace-Name: University of Luxembourg Author-Email: luca.uberti@uni.lu Title: Interpreting logit models Journal: Stata Journal Pages: 60-76 Issue: 1 Volume: 22 Year: 2022 Month: March X-DOI: 10.1177/1536867X221083855 Abstract: The parameters of logit models are typically difficult to interpret, and the applied literature is replete with interpretive and computational mistakes. In this article, I review a menu of options to interpret the results of logistic regressions correctly and effectively using Stata. I consider marginal effects, partial effects, (contrasts of) predictive margins, elasticities, and odds and risk ratios. I also show that interaction terms are typically easier to interpret in practice than implied by the recent literature on this topic. Keywords: logit, binary outcome models, nonlinear models, interpretation, interaction terms File-URL: http://hdl.handle.net/10.1177/1536867X221083855 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-1/st0663/ Handle:RePEc:tsj:stataj:y:22:y:2022:i:1:p:60-76 Template-Type: ReDIF-Article 1.0 Author-Name: Ariel Linden Author-Workplace-Name: Linden Consulting Group Author-Email: alinden@lindenconsulting.org Author-Person: pli1113 Title: Computing the fragility index for randomized trials and meta-analyses using Stata Journal: Stata Journal Pages: 77-88 Issue: 1 Volume: 22 Year: 2022 Month: March X-DOI: 10.1177/1536867X221083856 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. Correspond- ingly, 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. Keywords: fragility, metafrag, fragility index, meta-analysis, randomized controlled trials, research methodology, statistical significance File-URL: http://hdl.handle.net/10.1177/1536867X221083856 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-1/st0664/ Handle:RePEc:tsj:stataj:y:22:y:2022:i:1:p:77-88 Template-Type: ReDIF-Article 1.0 Author-Name: Sylvain Weber Author-Workplace-Name: University of Applied Sciences and Arts of Western Switzerland Author-Email: sylvain.weber@hesge.ch Author-Person: pwe150 Author-Name: Martin Péclat Author-Workplace-Name: Romande Energie SA Author-Email: martin.peclat@bluewin.ch Author-Name: August Warren Author-Workplace-Name: Office of the State Superintendent of Education Author-Email: augustjwarren@gmail.com Title: Travel distance and travel time using Stata: New features and major improvements in georoute Journal: Stata Journal Pages: 89-102 Issue: 1 Volume: 22 Year: 2022 Month: March X-DOI: 10.1177/1536867X221083857 Abstract: The community-contributed command georoute is designed to calculate travel distance and travel time between two addresses or two geographical points identified by their coordinates. Since its conception and description by We- ber and Péclat (2017, Stata Journal 17: 962–971), the command has been grad- ually maintained and enriched. The new version of georoute presented in this article encompasses major improvements, such as the possibility to specify trans- port mode and departure time. The new features open the way to a multitude of more sophisticated research applications. Keywords: georoute, georoutei, geocoding, travel distance, travel time File-URL: http://hdl.handle.net/10.1177/1536867X221083857 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-1/dm0092_1/ Handle:RePEc:tsj:stataj:y:22:y:2022:i:1:p:89-102 Template-Type: ReDIF-Article 1.0 Author-Name: Daoping Wang Author-Workplace-Name: Shanghai University of Finance and Economics Author-Email: daopingwang@outlook.com Author-Name: Kerui Du Author-Workplace-Name: Xiamen University Author-Email: kerrydu@xmu.edu.cn Author-Name: Ning Zhang Author-Workplace-Name: Shandong University Author-Email: zn928@naver.com Title: Measuring technical efficiency and total factor productivity change with undesirable outputs in Stata Journal: Stata Journal Pages: 103-124 Issue: 1 Volume: 22 Year: 2022 Month: March X-DOI: 10.1177/1536867X221083886 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. Tech- nical efficiency measures are obtained by solving linear programming problems. The gtfpch command we also describe here provides tools for measuring produc- tivity change, for example, the Malmquist–Luenberger index and the Luenberger indicator. We provide a brief overview of the nonparametric efficiency and produc- tivity 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. Keywords: teddf, gtfpch, data envelopment analysis, DEA, Malmquist–Luenberger index, Luenberger indicator, directional distance function, total factor productivity, radial Debreu–Farrell, nonradial Russell measures File-URL: http://hdl.handle.net/10.1177/1536867X221083886 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-1/st0665/ Handle:RePEc:tsj:stataj:y:22:y:2022:i:1:p:103-124 Template-Type: ReDIF-Article 1.0 Author-Name: Jeremy Freese Author-Workplace-Name: Stanford University Author-Email: jfreese@stanford.edu Author-Name: Sasha Johfre Author-Workplace-Name: Stanford University Author-Email: sjohfre@stanford.edu Title: Binary contrasts for unordered polytomous regressors Journal: Stata Journal Pages: 125-133 Issue: 1 Volume: 22 Year: 2022 Month: March X-DOI: 10.1177/1536867X221083900 Abstract: In observational studies, regression coefficients for categorical regressors are overwhelmingly presented in terms of contrasts with a reference category. For unordered regressors with many categories, however, this approach often focuses on contrasting different pairs of categories to one another with little substantive rationale for foregrounding some comparisons with others. Mean contrasts, which compare categories with the overall mean, provide an alternative to the reference category, but the magnitude of mean contrasts is conflated with the relative sizes of the categories. Instead, binary contrasts compare a category with all the other categories, allowing the familiar interpretation for dichotomous regressors. Our command binarycontrast computes binary contrasts. Keywords: binarycontrast, interpretation, contrast coding File-URL: http://hdl.handle.net/10.1177/1536867X221083900 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-1/st0666/ Handle:RePEc:tsj:stataj:y:22:y:2022:i:1:p:125-133 Template-Type: ReDIF-Article 1.0 Author-Name: Brian P. Shaw Author-Workplace-Name: Indiana University Author-Email: bpshaw@indiana.edu Title: Effect sizes for contrasts of estimated marginal effects Journal: Stata Journal Pages: 134-157 Issue: 1 Volume: 22 Year: 2022 Month: March X-DOI: 10.1177/1536867X221083901 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 η2 and ω2 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. Keywords: mces, svysd, effect size, margins, esize, marginal effects, contrasts of marginal effects File-URL: http://hdl.handle.net/10.1177/11536867X221083901 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-1/st0667/ Handle:RePEc:tsj:stataj:y:22:y:2022:i:1:p:134-157 Template-Type: ReDIF-Article 1.0 Author-Name: Werner Vach Author-Workplace-Name: University of Basel Author-Email: werner.vach@unibas.ch Author-Name: Cornelia Alder Author-Workplace-Name: University of Basel Author-Email: cornelia.alder@unibas.ch Author-Name: Jorge Rivera Author-Workplace-Name: University of Basel Author-Email: sandra.pichler@unibas.ch Title: Analyzing coarsened categorical data with or without probabilistic information Journal: Stata Journal Pages: 158-194 Issue: 1 Volume: 22 Year: 2022 Month: March X-DOI: 10.1177/1536867X221083902 Abstract: In some applications, only a coarsened version of a categorical outcome variable can be observed. Parametric inference based on the maximum likelihood approach is feasible in principle, but it cannot be covered computationally by standard software tools. In this article, we present two commands facilitating maximum likelihood estimation in this situation for a wide range of parametric models for categorical outcomes—in the cases both of a nominal and an ordinal scale. In particular, the case of probabilistic information about the possible values of the outcome variable is also covered. Two examples motivating this scenario are presented and analyzed. Keywords: pccfit, pccprob, coarsened data, multinomial distribution, multinomial regression, ordinal outcome variables, ordered regression, human osteoarchaeology, palaeodemography, diagnostic accuracy studies, imperfect reference standard File-URL: http://hdl.handle.net/10.1177/1536867X221083902 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-1/st0668/ Handle:RePEc:tsj:stataj:y:22:y:2022:i:1:p:158-194 Template-Type: ReDIF-Article 1.0 Author-Name: Giovanni Cerulli Author-Workplace-Name: IRCrES-CNR Author-Email: giovanni.cerulli@ircres.cnr.it Author-Person: pce40 Author-Name: Rosaria Simone Author-Workplace-Name: University of Naples Federico II Author-Email: giovanni.cerulli@ircres.cnr.it Author-Name: Francesca Di Iorio Author-Workplace-Name: University of Naples Federico II Author-Email: giovanni.cerulli@ircres.cnr.it Author-Person: pdi74 Author-Name: Domenico Piccolo Author-Workplace-Name: University of Naples Federico II Author-Email: giovanni.cerulli@ircres.cnr.it Author-Person: ppi354 Author-Name: Christopher F Baum Author-Workplace-Name: Boston College Author-Workplace-Name: DIW Berlin Author-Email: baum@bc.edu Author-Person: pba1 Title: Fitting mixture models for feeling and uncertainty for rating data analysis Journal: Stata Journal Pages: 195-223 Issue: 1 Volume: 22 Year: 2022 Month: March X-DOI: 10.1177/1536867X221083927 Abstract: In this article, we present the command cub, which fits ordinal rating data using combination of uniform and binomial (CUB) models, a class of finite mixture distributions accounting for both feeling and uncertainty of the response process. CUB identifies the components that define the mixture in the baseline model specification. We apply maximum likelihood methods to estimate feeling and uncertainty parameters, which are possibly explained in terms of covariates. An extension to inflated CUB models is discussed. We also present a subcommand, scattercub, for visualization of results. We then illustrate the use of cub using a case study on students’ satisfaction for the orientation services provided by the University of Naples Federico II in Italy. Keywords: cub, scattercub, CUB, mixture models, rating data, maximum likelihood estimation File-URL: http://hdl.handle.net/10.1177/1536867X221083927 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-1/st0669/ Handle:RePEc:tsj:stataj:y:19:y:2019:i:1:p:195-223 Template-Type: ReDIF-Article 1.0 Author-Name: Nicholas J. Cox Author-Workplace-Name: Durham University Author-Email: n.j.cox@durham.ac.uk Author-Person: pco34 Title: Stata tip 145: Numbering weeks within months Journal: Stata Journal Pages: 224-230 Issue: 1 Volume: 22 Year: 2022 Month: March X-DOI: 1536867X221083928 File-URL: http://hdl.handle.net/10.1177/1536867X221083928 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-1/dm0107/ Handle:RePEc:tsj:stataj:y:22:y:2022:i:1:p:224-230 Template-Type: ReDIF-Article 1.0 Author-Name: Ariel Linden Author-Workplace-Name: Linden Consulting Group Author-Email: alinden@lindenconsulting.org Author-Person: pli1113 Title: Erratum: A comprehensive set of postestimation measures to enrich interrupted time-series analysis Journal: Stata Journal Pages: 231-233 Issue: 1 Volume: 22 Year: 2022 Month: March X-DOI: 1536867X221083928 File-URL: http://hdl.handle.net/10.1177/1536867X221083928 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-1/st0389_8/ Handle:RePEc:tsj:stataj:y:22:y:2022:i:1:p:231-233 Template-Type: ReDIF-Article 1.0 Author-Name: Christopher F Baum Author-Workplace-Name: Boston College Author-Email: baum@bc.edu Author-Person: pba1 Author-Name: Jesús Otero Author-Workplace-Name: Universidad del Rosario Author-Email: jesus.otero@urosario.edu.co Author-Person: pot11 Title: Erratum: Unit-root tests for explosive behavior Journal: Stata Journal Pages: 234-237 Issue: 1 Volume: 22 Year: 2022 Month: March X-DOI: 1536867X221083930 File-URL: http://hdl.handle.net/10.1177/1536867X221083930 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-1/st0659_1/ Handle:RePEc:tsj:stataj:y:22:y:2022:i:1:p:234-237 Template-Type: ReDIF-Article 1.0 Author-Name: Editors Author-Email: editors@stata.com Title: Software updates Journal: Stata Journal Pages: 238-241 Issue: 1 Volume: 22 Year: 2022 Month: March Abstract: Updates for previously published packages are provided. Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-1/st0376_3/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-1/st0528_1/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-1/st0651_1/ Note: Windows users should not attempt to download these files with a web browser. Handle:RePEc:tsj:stataj:y:22:y:2022:i:1:p:238-241