Template-Type: ReDIF-Article 1.0 Author-Name: Niels Henrik Bruun Author-Workplace-Name: Aalborg University Hospital, Denmark Author-Email: nbru@rn.dk Author-Person: pbr821 Title: The self-controlled case-series (SCCS) research design and the sccsdta command Journal: Stata Journal Pages: 646-658 Issue: 3 Volume: 25 Year: 2025 Month: September Abstract: The self-controlled case-series (SCCS) design is a powerful analytical approach that is particularly useful in pharmacological and epidemiological studies. It inherently controls for known and unknown time-invariant confounders. The SCCS uses data from exposed individuals who have experienced the event of interest within a defined observation period. Thus, the study is less resource intensive than other designs that need data from a larger population. The SCCS design is well suited to investigate acute events and temporary exposures. It is frequently used to study the safety of vaccines and pharmaceutical drugs. In this article, I introduce a new command, sccsdta, and illustrate its use with real-world data. Keywords: sccsdta, self-controlled case series, SCCS, conditional Poisson regression File-URL: http://www.stata-journal.com/article.html?article=st0784 File-Function: link to article purchase X-DOI: 10.1177/1536867X251365496 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-3/st0784 Handle:RePEc:tsj:stataj:v:25:y:2025:i:3:p:646-658 Template-Type: ReDIF-Article 1.0 Author-Name: Jan Ditzen Author-Workplace-Name: Free University of Bozen-Bolzano Author-Email: jan.ditzen@unibz.it Author-Person: pdi434 Author-Name: Yiannis Karavias Author-Workplace-Name: Brunel University of London Author-Email: yiannis.karavias@brunel.ac.uk Author-Person: pka744 Author-Name: Joakim Westerlund Author-Workplace-Name: Lund University Author-Workplace-Name: Deakin University Author-Email: joakim.westerlund@nek.lu.se Author-Person: pwe289 Title: Testing and estimating structural breaks in time series and panel data in Stata Journal: Stata Journal Pages: 526-560 Issue: 3 Volume: 25 Year: 2025 Month: September Abstract: Identifying structural change is a crucial step when analyzing time series and panel data. The longer the time span, the higher the likelihood that the model parameters have changed because of major disruptive events such as the 2007–2008 financial crisis and the 2020 COVID-19 outbreak. Detecting the existence of breaks and dating them is therefore necessary for not only estima- tion but also understanding drivers of change and their effect on relationships. In this article, we introduce a new community-contributed command called xtbreak, which provides researchers with a complete toolbox for analyzing multiple struc- tural breaks in time series and panel data. xtbreak can detect the existence of breaks, determine their number and location, and provide break-date confidence intervals. We use xtbreak in examples to explore changes in the relationship be- tween COVID-19 cases and deaths in the US using both aggregate and state-level data and in the relationship between approval ratings and consumer confidence using a panel of eight countries. Keywords: xtbreak, structural breaks, change points, time-series data, panel data, interactive fixed effects, cross-sectional dependence File-URL: http://www.stata-journal.com/article.html?article=st0781 File-Function: link to article purchase X-DOI: Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-3/st0781 Handle:RePEc:tsj:stataj:v:25:y:2025:i:3:p:526-560 Template-Type: ReDIF-Article 1.0 Author-Name: David M. Drukker Author-Workplace-Name: Clemson University Author-Email: ddrukke@clemson.edu Author-Person: pdr184 Author-Name: Di Liu Author-Workplace-Name: StataCorp Author-Email: dil@stata.com Title: posis: Command for the sure-independence-screening Neyman orthogonal estimator Journal: Stata Journal Pages: 561-586 Issue: 3 Volume: 25 Year: 2025 Month: September Abstract: Inference for structural parameters in a high-dimensional model has become increasingly popular. Belloni, Chernozhukov, and Wei (2016, Journal of Business and Economic Statistics 34: 606–619) proposed a lasso-based Neyman orthogonal estimator that produces valid inference for the coefficients of interest in the generalized linear model. Drukker and Liu (2022, Econometric Reviews 41: 1047–1076) extend their estimator by using a Bayesian information criterion (BIC) stepwise-based Neyman orthogonal estimator, and the simulations show the advantage of using BIC-based stepwise as the covariate-selection technique. However, the BIC-stepwise-based Neyman orthogonal estimator becomes compu- tationally infeasible when there are many more control variables. To overcome this computational bottleneck, Drukker and Liu (2022) proposed combining the sure- independence-screening technique with BIC-based stepwise to improve the compu- tational speed while maintaining similar or better statistical performance. In this article, we present posis, a command for an iterative-sure-independence-screening- based Neyman orthogonal estimator for the high-dimensional linear, logit, and Poisson models. Keywords: posis, isis, sparse high-dimensional model, partialing-out, sure-independence screening, Neyman orthogonal, generalized linear model, postselection inference File-URL: http://www.stata-journal.com/article.html?article=st0782 File-Function: link to article purchase X-DOI: 10.1177/1536867X251365455 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-3/st0782 Handle:RePEc:tsj:stataj:v:25:y:2025:i:3:p:561-586 Template-Type: ReDIF-Article 1.0 Author-Name: Milena Falcaro Author-Workplace-Name: Queen Mary University of London Author-Email: m.falcaro@qmul.ac.uk Author-Name: Roger B. Newson Author-Workplace-Name: Queen Mary University of London Author-Email: r.newson@qmul.ac.uk Title: tabagree: Nonparametric measures of agreement and disagreement in paired ordinal data Journal: Stata Journal Pages: 627-645 Issue: 3 Volume: 25 Year: 2025 Month: September Abstract: In this article, we describe tabagree, a new command for assessing the level of agreement and disagreement in paired ordinal data. tabagree implements some of the nonparametric measures proposed by Svensson (1993, Analysis of Systematic and Random Differences Between Paired Ordinal Categorical Data [Almqvist and Wiksell]) and allows the user to evaluate systematic disagreement separately from random differences. For example, the command can be used in interrater and intrarater reliability studies or in analyses of change. Keywords: tabagree, agreement, ordinal paired data, relative concentration, relative position, relative rank variance File-URL: http://www.stata-journal.com/article.html?article=st0783 File-Function: link to article purchase X-DOI: 10.1177/1536867X251365495 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-3/st0783 Handle:RePEc:tsj:stataj:v:25:y:2025:i:3:p:627-645 Template-Type: ReDIF-Article 1.0 Author-Name: Leonardo Guizzetti Author-Workplace-Name: Ottawa, Canada Author-Email: leonardo.guizzetti@gmail.com Title: Stata tip 163: Nesting and expanding macros Journal: Stata Journal Pages: 685-688 Issue: 3 Volume: 25 Year: 2025 Month: September File-URL: http://www.stata-journal.com/article.html?article=st0786 File-Function: link to article purchase DOI: 10.1177/1536867X251365503 Handle:RePEc:tsj:stataj:v:25:y:2025:i:3:p:685-688 Template-Type: ReDIF-Article 1.0 Author-Name: Leonardo Guizzetti Author-Workplace-Name: Ottawa, Canada Author-Email: leonardo.guizzetti@gmail.com Title: Stata tip 164: Finding terms in lists Journal: Stata Journal Pages: 689-695 Issue: 3 Volume: 25 Year: 2025 Month: September File-URL: http://www.stata-journal.com/article.html?article=st0787 File-Function: link to article purchase DOI: 10.1177/1536867X251365515 Handle:RePEc:tsj:stataj:v:25:y:2025:i:3:p:689-695 Template-Type: ReDIF-Article 1.0 Author-Name: Giuseppe De Luca Author-Workplace-Name: University of Palermo Author-Email: giuseppe.deluca@unipa.it Author-Person: pde440 Author-Name: Jan R. Magnus Author-Workplace-Name: Vrije Universiteit Amsterdam Author-Email: jan@janmagnus.nl Author-Person: pma753 Title: Weighted-average least squares: Improvements and extensions Journal: Stata Journal Pages: 587-626 Issue: 3 Volume: 25 Year: 2025 Month: September Abstract: This article presents version 3.0 of the wals command, which im- plements the weighted-average least-squares estimator of Magnus, Powell, and Prüfer (2010, Journal of Econometrics 154: 139–153). Version 3.0 improves ear- lier versions of wals in several respects: a new syntax supporting factor variables, time-series operators, and weights; an enlarged set of prior distributions; extended quadrature methods for computing the posterior mean; new plugin estimates of the sampling moments; simulation-based confidence intervals; and other options to control accuracy, computational speed, and output of wals. We also offer three new postestimation commands: the predict command associated with wals; the lcwals command, which estimates linear combinations of the parameters; and the margwals command, which estimates smooth, possibly nonlinear functions of the parameters at given values of regressors. Finally, we compare our new commands with two suites of commands for tackling issues of model uncertainty. Keywords: wals, wals postestimation, lcwals, margwals, weighted-average least squares, linear model, Bayesian shrinkage, inference, postestimation File-URL: http://www.stata-journal.com/article.html?article=st0239_1 File-Function: link to article purchase X-DOI: 10.1177/1536867X251365494 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-3/st0239_1 Handle:RePEc:tsj:stataj:v:25:y:2025:i:3:p:587-626 Template-Type: ReDIF-Article 1.0 Author-Name: Simon R. Parker Author-Workplace-Name: London School of Hygiene and Tropical Medicine Author-Email: simon.parker@lshtm.ac.uk Author-Name: Linda Vesel Author-Workplace-Name: Ariadne Labs, Brigham and Women’s Hospital Author-Email: lvesel@ariadnelabs.or Author-Name: Eric O. Ohuma Author-Workplace-Name: London School of Hygiene and Tropical Medicine Author-Email: eric.ohuma@lshtm.ac.uk Title: gigs: A package for standardizing fetal, neonatal, and child growth assessment with extensions to egen Journal: Stata Journal Pages: 499-525 Issue: 3 Volume: 25 Year: 2025 Month: September Abstract: In this article, we describe gigs, the Guidance for International Growth Standards package for Stata. gigs contains multiple egen functions for converting between fetal biometry or anthropometric measurements and z scores or centiles in both the World Health Organization child growth standards and the INTERGROWTH-21st standards. We also describe an additional Stata command that wraps these functions to provide growth outcome classification for newborns and infants up to five years of age using a suite of international growth standards. Clear and consistent commands with the functionality described in this article have not been available to Stata users prior to the release of this article. These features will be instrumental for standardizing methods for growth assessment in fetal, newborn, and child health research, as well as implementation, clinical practice, and population-level comparisons. Keywords: gigs, GIGS, WHO child growth standards, INTERGROWTH-21st standards, egen, z scores, centiles, anthropometry File-URL: http://www.stata-journal.com/article.html?article=st0780 File-Function: link to article purchase X-DOI: 10.1177/1536867X251365448 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-3/st0780 Handle:RePEc:tsj:stataj:v:25:y:2025:i:3:p:499-525 Template-Type: ReDIF-Article 1.0 Author-Name: Patrick Taffé Author-Workplace-Name: University of Lausanne Author-Email: Patrick.Taffe@unisante.ch Title: ctl: A package for assessing agreement based on clinical tolerance limits Journal: Stata Journal Pages: 659-676 Issue: 3 Volume: 25 Year: 2025 Month: September Abstract: The ctl command implements a recently proposed statistical method- ology to assess the level of agreement or interchangeability between two quantita- tive measurement methods. It is based on tolerance limits that are specified by the user. The methodology requires repeated measurements by at least one of the two measurement methods. It accommodates heteroskedastic measurement errors and often performs well even when the user has only one measurement by one of the two measurement methods and at least five repeated measurements from the other. It provides a more direct assessment of the agreement level than the Bland–Altman limits of agreement method and circumvents some of its deficiencies. Keywords: ctl, agreement, tolerance limits, differential bias, proportional bias, method comparison File-URL: http://www.stata-journal.com/article.html?article=st0785 File-Function: link to article purchase X-DOI: 10.1177/1536867X251365501 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-3/st0785 Handle:RePEc:tsj:stataj:v:25:y:2025:i:3:p:659-676 Template-Type: ReDIF-Article 1.0 Author-Name: Richard Williams Author-Workplace-Name: University of Notre Dame Author-Email: rwilliam@nd.edu Title: Review of Maximum Likelihood Estimation with Stata, Fifth Edition, by Pitblado, Poi, and Gould Journal: Stata Journal Pages: 677-684 Issue: 3 Volume: 25 Year: 2025 Month: September Abstract: I review Maximum Likelihood Estimation with Stata, Fifth Edition, by Pitblado, Poi, and Gould (2024 [Stata Press]). Keywords: book review, maximum likelihood, Stata programming File-URL: http://www.stata-journal.com/article.html?article=gn0104 File-Function: link to article purchase X-DOI: Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-3/gn0104 Handle:RePEc:tsj:stataj:v:25:y:2025:i:3:p:677-684