Template-Type: ReDIF-Article 1.0 Author-Name: Richard Williams Author-Workplace-Name: University of Notre Dame Author-Email: rwilliam@nd.edu Author-Name: Paul D. Allison Author-Workplace-Name: University of Pennsylvania Author-Name: Enrique Moral-Benito Author-Workplace-Name: Banco de España Title: Linear dynamic panel-data estimation using maximum likelihood and structural equation modeling Journal: Stata Journal Pages: 293-326 Issue: 2 Volume: 18 Year: 2018 Month: June Abstract: Panel data make it possible both to control for unobserved confounders and to include lagged, endogenous regressors. However, trying to do both simulta- neously leads to serious estimation difficulties. In the econometric literature, these problems have been addressed by using lagged instrumental variables together with the generalized method of moments, while in sociology the same problems have been dealt with using maximum likelihood estimation and structural equa- tion modeling. While both approaches have merit, we show that the maximum likelihood–structural equation models method is substantially more efficient than the generalized method of moments method when the normality assumption is met and that the former also suffers less from finite sample biases. We introduce the command xtdpdml, which has syntax similar to other Stata commands for linear dynamic panel-data estimation. xtdpdml greatly simplifies the structural equation model specification process; makes it possible to test and relax many of the constraints that are typically embodied in dynamic panel models; allows one to include time-invariant variables in the model, unlike most related methods; and takes advantage of Stata’s ability to use full-information maximum likelihood for dealing with missing data. The strengths and advantages of xtdpdml are illustrated via examples from both economics and sociology. Copyright 2018 by StataCorp LP. Keywords: xtdpdml, linear dynamic panel-data, structural equation mod- eling, maximum likelihood File-URL: http://www.stata-journal.com/article.html?article=st0523 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj18-2/st0523/ File-Format: text/html Handle:RePEc:tsj:stataj:v:18:y:2018:i:2:p:293-326 Template-Type: ReDIF-Article 1.0 Author-Name: Susan Donath Author-Workplace-Name: University of Melbourne Author-Email: susan.donath@mcri.edu.au Title: baselinetable: A command for creating one- and two-way tables of summary statistics Journal: Stata Journal Pages: 327-344 Issue: 2 Volume: 18 Year: 2018 Month: June Abstract: In this article, I describe baselinetable, a community-contributed command for creating one- and two-way tables of summary statistics for a list of numeric variables. Unlike other Stata tabulation commands such as tabulate, table, or tabstat, the variables forming the table rows can be a mixture of contin- uous (summarized by mean, standard deviation, etc.) and categorical (summarized by percentages and frequencies). baselinetable provides considerable flexibility in the way the results are displayed. In particular, the summary statistics and their presentation can be different for each row variable. Additional features in- clude several options for displaying counts of missing and nonmissing data points and the ability to restrict results to subgroups of the data for individual row vari- ables. The contents of the table can be saved as a data file or text file, or they can be exported to Excel. The motivation for baselinetable is the descriptive table commonly seen in health research publications in which the baseline charac- teristics of two or more groups are compared. This descriptive table usually has only one column for each group, generally with at least two summary statistics in each column (for example, mean and standard deviation for continuous vari- ables or frequency and percentage for categorical variables). The baselinetable command supports reproducible research by enabling researchers to easily create tables whose contents can be used unchanged in publications. Copyright 2018 by StataCorp LP. Keywords: baselinetable, summary statistics table, health research tables File-URL: http://www.stata-journal.com/article.html?article=st0524 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj18-2/st0524/ File-Format: text/html Handle:RePEc:tsj:stataj:v:18:y:2018:i:2:p:327-344 Template-Type: ReDIF-Article 1.0 Author-Name: Marshall A. Taylor Author-Workplace-Name: University of Notre Dame Author-Email: mtaylo15@nd.edu Title: Simulating the central limit theorem Journal: Stata Journal Pages: 345-356 Issue: 2 Volume: 18 Year: 2018 Month: June Abstract: Understanding the central limit theorem is crucial for comprehend- ing parametric inferential statistics. Despite this, undergraduate and graduate students alike often struggle with grasping how the theorem works and why re- searchers rely on its properties to draw inferences from a single unbiased random sample. In this article, I outline a new command, sdist, that can be used to simulate the central limit theorem by generating a matrix of randomly generated normal or nonnormal variables and comparing the true sampling distribution stan- dard deviation with the standard error from the first randomly generated sample. The user also has the option of plotting the empirical sampling distribution of sample means, the first random variable distribution, and a stacked visualization of the two distributions. Copyright 2018 by StataCorp LP. Keywords: sdist, central limit theorem, simulation, runiform(), teaching File-URL: http://www.stata-journal.com/article.html?article=st0525 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj18-2/st0525/ File-Format: text/html Handle:RePEc:tsj:stataj:v:18:y:2018:i:2:p:345-356 Template-Type: ReDIF-Article 1.0 Author-Name: John A. Gallis Author-Workplace-Name: Duke University Author-Email: john.gallis@duke.edu Author-Name: Fan Li Author-Workplace-Name: Duke University Author-Email: frank.li@duke.edu Author-Name: Hengshi Yu Author-Workplace-Name: University of Michigan Author-Email: hengshi@umich.edu Author-Name: Elizabeth L. Turner Author-Workplace-Name: Duke University Author-Email: liz.turner@duke.edu Title: cvcrand and cptest: Commands for efficient design and analysis of cluster randomized trials using constrained randomization and permutation tests Journal: Stata Journal Pages: 357-378 Issue: 2 Volume: 18 Year: 2018 Month: June Abstract: Cluster randomized trials (CRTs), where clusters (for example, schools or clinics) are randomized to comparison arms but measurements are taken on in- dividuals, are commonly used to evaluate interventions in public health, education, and the social sciences. Because CRTs typically involve a small number of clusters (for example, fewer than 20), simple randomization frequently leads to baseline im- balance of cluster characteristics across study arms, threatening the internal valid- ity of the trial. In CRTs with a small number of clusters, classic approaches to bal- ancing baseline characteristics—such as matching and stratification—have several drawbacks, especially when the number of baseline characteristics the researcher desires to balance is large (Ivers et al., 2012, Trials 13: 120). An alternative de- sign approach is covariate-constrained randomization, whereby a randomization scheme is randomly selected from a subset of all possible randomization schemes based on the value of a balancing criterion (Raab and Butcher, 2001, Statistics in Medicine 20: 351–365). Subsequently, a clustered permutation test can be used in the analysis, which provides increased power under constrained randomization compared with simple randomization (Li et al., 2016, Statistics in Medicine 35: 1565–1579). In this article, we describe covariate-constrained randomization and the permutation test for the design and analysis of CRTs and provide an example to demonstrate the use of our new commands cvcrand and cptest to implement constrained randomization and the permutation test. Copyright 2018 by StataCorp LP. Keywords: cvcrand, cptest, covariate-constrained randomization, cluster randomized trials, permutation test File-URL: http://www.stata-journal.com/article.html?article=st0526 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj18-2/st0526/ File-Format: text/html Handle:RePEc:tsj:stataj:v:18:y:2018:i:2:p:357-378 Template-Type: ReDIF-Article 1.0 Author-Name: Mehmet F. Dicle Author-Workplace-Name: Loyola University New Orleans Author-Email: mfdicle@gmail.com Author-Name: Betul Dicle Author-Workplace-Name: Research and Teaching Associates Author-Email: bkdicle@gmail.com Title: Content analysis: Frequency distribution of words Journal: Stata Journal Pages: 379-386 Issue: 2 Volume: 18 Year: 2018 Month: June Abstract: Many academic fields use content analysis. At the core of most common content analysis lies frequency distribution of individual words. Websites and documents are mined for usage and frequency of certain words. In this article, we introduce a community-contributed command, wordfreq, to process content (online and local) and to prepare a frequency distribution of individual words. Additionally, another community-contributed command, wordcloud, is introduced to draw a simple word cloud graph for visual analysis of the frequent usage of specific words. Copyright 2018 by StataCorp LP. Keywords: wordfreq, wordcloud, word counting, frequency distribution, content analysis, word cloud File-URL: http://www.stata-journal.com/article.html?article=dm0094 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj18-2/dm0094/ File-Format: text/html Handle:RePEc:tsj:stataj:v:18:y:2018:i:2:p:379-386 Template-Type: ReDIF-Article 1.0 Author-Name: Christiaan H. Righolt Author-Workplace-Name: University of Manitoba Author-Email: Christiaan.Righolt@umanitoba.ca Author-Name: Salaheddin M. Mahmud Author-Workplace-Name: University of Manitoba Author-Email: Salah.Mahmud@umanitoba.ca Title: Attrition diagrams for clinical trials and meta-analyses in Stata Journal: Stata Journal Pages: 387-394 Issue: 2 Volume: 18 Year: 2018 Month: June Abstract: In this article, we present attrition, a suite of commands to sim- plify the maintenance and documentation of implemented exclusion criteria and attrition conditions using standard Stata facilities and to generate an attrition diagram. attrition can be used, both from the command line and in do-files, to keep the diagram up to date with the analysis it documents. Six subcommands (set, exclude, count, tab, list, graph) allow the diagram to be constructed in a versatile way. Copyright 2018 by StataCorp LP. Keywords: attrition set, attrition exclude, attrition count, attrition tab, attrition list, attrition graph, attrition diagram, inclusion variable, clinical trial, meta-analysis File-URL: http://www.stata-journal.com/article.html?article=st0527 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj18-2/st0527/ File-Format: text/html Handle:RePEc:tsj:stataj:v:18:y:2018:i:2:p:387-394 Template-Type: ReDIF-Article 1.0 Author-Name: Mónica Hernández-Alava Author-Workplace-Name: University of Sheffield Author-Email: monica.hernandez@sheffield.ac.uk Author-Name: Stephen Pudney Author-Workplace-Name: University of Sheffield Author-Email: steve.pudney@sheffield.ac.uk Title: eq5dmap: A command for mapping between EQ-5D-3L and EQ-5D-5L Journal: Stata Journal Pages: 395-415 Issue: 2 Volume: 18 Year: 2018 Month: June Abstract: In this article, we describe a new command, eq5dmap, for conditional prediction of the utility values of EQ-5D-5L (EQ-5D-3L) from observed or speci- fied values of EQ-5D-3L (EQ-5D-5L) conditional on age and gender. Predictions can be made either from the five-item health descriptions or from the (exact or approximate) utility score. The prediction process is based on a joint statistical model of the two variants of EQ-5D that have been fit to alternative reference datasets (the National Data Bank for Rheumatic Diseases and a EuroQol Group coordinated data-collection study). The underlying model is a system of ordinal regressions with a flexible residual distribution specified as Gaussian or as a copula mixture. Use of the command is illustrated with an application that includes an investigation of the sensitivity of the mapping outcomes to the choice of reference dataset. Keywords: eq5dmap, EQ-5D, EQ-5D-3L, EQ-5D-5L, mapping, conditional prediction, copula, mixture model File-URL: http://www.stata-journal.com/article.html?article=st0528 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj18-2/st0528/ File-Format: text/html Handle:RePEc:tsj:stataj:v:18:y:2018:i:2:p:395-415 Template-Type: ReDIF-Article 1.0 Author-Name: Michael J. Grayling Author-WorkPlace-Name: MRC Biostatistics Unit Author-Email: mjg211@cam.ac.uk Author-Name: James M. S. Wason Author-WorkPlace-Name: MRC Biostatistics Unit Author-Email: james.wason@mrc-bsu.cam.ac.uk Author-Name: Adrian P. Mander Author-WorkPlace-Name: MRC Biostatistics Unit Author-Email: adrian.mander@mrc-bsu.cam.ac.uk Title: Group sequential clinical trial designs for normally distributed outcome variables Journal: Stata Journal Pages: 416-431 Issue: 2 Volume: 18 Year: 2018 Month: June Abstract: In a group sequential clinical trial, accumulated data are analyzed at numerous time points to allow early decisions about a hypothesis of interest. These designs have historically been recommended for their ethical, administrative, and economic benefits. In this article, we first discuss a collection of new commands for computing the stopping boundaries and required group size of various classi- cal group sequential designs, assuming a normally distributed outcome variable. Then, we demonstrate how the performance of several designs can be compared graphically. Keywords: doubletriangular, haybittlepeto, innerwedge, powerfamily, triangular, wangtsiatis, clinical trial design, group sequential File-URL: http://www.stata-journal.com/article.html?article=st0529 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj18-2/st0529/ File-Format: text/html Handle:RePEc:tsj:stataj:v:18:y:2018:i:2:p:416-431 Template-Type: ReDIF-Article 1.0 Author-Name: Noori Akhtar-Danesh Author-Workplace-Name: McMaster University Author-Email: daneshn@mcmaster.ca Title: qfactor: A command for Q-methodology analysis Journal: Stata Journal Pages: 432-446 Issue: 2 Volume: 18 Year: 2018 Month: June Abstract: In this article, I introduce qfactor, a new command for Q-methodology analysis. Q-methodology is a combination of qualitative and quantitative tech- niques for studying subjectivity. Its quantitative component is based on a by- person factor analysis, usually followed by a factor-rotation technique. Currently, only a handful of programs with limited capability are available for Q-methodology analysis, and none of them are in the major commercial statistical programs such as Stata, SPSS, and SAS. qfactor offers an attractive set of options, including different factor-extraction and factor-rotation techniques in Stata. The use of qfactor is illustrated using a dataset representing 40 individuals’ perceptions on marijuana legalization. Keywords: qfactor, Q-methodology, by-person factor analysis, bipolar factor extraction File-URL: http://www.stata-journal.com/article.html?article=st0530 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj18-2/st0530/ File-Format: text/html Handle:RePEc:tsj:stataj:v:18:y:2018:i:2:p:432-446 Template-Type: ReDIF-Article 1.0 Author-Name: Chang Hyung Lee Author-Workplace-Name: University of California, Santa Barbara Author-Email: clee00@umail.ucsb.edu Author-Name: Douglas G. Steigerwald Author-Workplace-Name: University of California, Santa Barbara Author-Email: doug@ucsb.edu Title: Inference for clustered data Journal: Stata Journal Pages: 447-460 Issue: 2 Volume: 18 Year: 2018 Month: June Abstract: In this article, we introduce clusteff, a community-contributed com- mand for checking the severity of cluster heterogeneity in cluster–robust analyses. Cluster heterogeneity can cause a size distortion leading to underrejection of the null hypothesis. Carter, Schnepel, and Steigerwald (2017, Review of Economics and Statistics 99: 698–709) develop the effective number of clusters to reflect a reduction in the degrees of freedom, thereby mirroring the distortion caused by assuming homogeneous clusters. clusteff generates the effective number of clus- ters. We provide a decision tree for cluster–robust analysis, demonstrate the use of clusteff, and recommend methods to minimize the size distortion. Keywords: clusteff, cluster heterogeneity File-URL: http://www.stata-journal.com/article.html?article=st0531 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj18-2/st0531/ File-Format: text/html Handle:RePEc:tsj:stataj:v:18:y:2018:i:2:p:447-460 Template-Type: ReDIF-Article 1.0 Author-Name: Fausto Pacicco Author-WorkPlace-Name: LIUC–Università Carlo Cattaneo Castellanza Author-Email: fpacicco@liuc.it Author-Name: Luigi Vena Author-WorkPlace-Name: LIUC–Università Carlo Cattaneo Castellanza Author-Email: lvena@liuc.it Author-Name: Andrea Venegoni Author-WorkPlace-Name: LIUC–Università Carlo Cattaneo Castellanza Author-Email: avenegoni@liuc.it Title: Event study estimations using Stata: The estudy command Journal: Stata Journal Pages: 461-476 Issue: 2 Volume: 18 Year: 2018 Month: June Abstract: In this article, we introduce the community-contributed command estudy and illustrate how it can be used to perform an event study customizing the statistical framework, from the estimates of abnormal returns to the tests for their statistical significance. Our command significantly improves the existing commands in terms of both completeness and user comprehension. Keywords: estudy, event study, financial econometrics File-URL: http://www.stata-journal.com/article.html?article=st0532 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj18-2/st0532/ File-Format: text/html Handle:RePEc:tsj:stataj:v:18:y:2018:i:2:p:461-476 Template-Type: ReDIF-Article 1.0 Author-Name: Ying Xu Author-WorkPlace-Name: Duke–NUS Graduate Medical School Author-Email: tinayxu@gmail.com Author-Name: Yin Bun Cheung Author-WorkPlace-Name: Duke–NUS Graduate Medical School Author-Email: yinbun.cheung@duke-nus.edu.sg Title: Frailty models and frailty-mixture models for recurrent event times: Update Journal: Stata Journal Pages: 477-484 Issue: 2 Volume: 18 Year: 2018 Month: June Abstract: Xu and Cheung (2015, Stata Journal 15: 135–154) introduced the strmcure command, which fits frailty models and frailty-mixture models in the analysis of recurrent event times. In this article, we provide an update to strmcure. The update implements a two-step estimation procedure for a frailty-mixture model that allows the estimation of the effect of an intervention on the probability of cure and on the total effect on event rate in the noncured. To illustrate, we will use the same example dataset on respiratory exacerbations from the original article. Keywords: strmcure, frailty-mixture model, primary effect, total effect, two-step estimation procedure File-URL: http://www.stata-journal.com/article.html?article=st0374_1 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj18-2/st0374_1/ File-Format: text/html Handle:RePEc:tsj:stataj:v:18:y:2018:i:2:p:477-484 Template-Type: ReDIF-Article 1.0 Author-Name: Ariel Linden Author-WorkPlace-Name: Linden Consulting Group Author-Email: alinden@lindenconsulting.org Title: Review of Tenko Raykov and George Marcoulides’s A Course in Item Response Theory and Modeling with Stata Journal: Stata Journal Pages: 385-488 Issue: 2 Volume: 18 Year: 2018 Month: June Abstract: In this article, I review A Course in Item Response Theory and Modeling with Stata by Tenko Raykov and George A. Marcoulides (2018 [Stata Press]). Keywords: item response theory, survey development, measurement, instrument, construct, latent variable, Stata File-URL: http://www.stata-journal.com/article.html?article=gn0076 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj18-2/gn0076/ File-Format: text/html Handle:RePEc:tsj:stataj:v:18:y:2018:i:2:p:485-488 Template-Type: ReDIF-Article 1.0 Author-Name: Editors Author-Email: editors@stata.com Title: Software updates Journal: Stata Journal Pages: 489 Issue: 2 Volume: 18 Year: 2018 Month: June Abstract: Updates for previously published packages are provided. File-URL: http://www.stata-journal.com/software/sj18-2/st0386_2/ File-Format: text/html File-URL: http://www.stata-journal.com/software/sj18-2/st0508_2/ File-Format: text/html File-URL: http://www.stata-journal.com/software/sj18-2/st0511_1/ File-Format: text/html File-URL: http://www.stata-journal.com/software/sj18-2/st0516_1/ File-Format: text/html Note: Windows users should not attempt to download these files with a web browser. Handle:RePEc:tsj:stataj:v:18:y:2018:i:2:p:489