{"title":"TRANSITIONS IN DRUG USE AMONG HIGH-RISK WOMEN: AN APPLICATION OF LATENT CLASS AND LATENT TRANSITION ANALYSIS.","authors":"Stephanie T Lanza, Bethany C Bray","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Latent class analysis (LCA) is a statistical approach to identifying underlying subgroups (i.e. latent classes) of individuals based on their responses to a set of observed categorical variables. Latent transition analysis (LTA) extends this framework to longitudinal data in order to estimate the incidence of transitions over time in latent class membership. This study provides an introduction to LCA and LTA, including the use of grouping variables and covariates, and demonstrates the use of two SAS ® procedures (PROC LCA and PROC LTA) to fit these models. The empirical demonstration involved data from 457 women who participated in the Women's Interagency HIV Study (WIHS). First, LCA was used to identify drug use latent classes based on reported use of tobacco, alcohol, marijuana, crack/cocaine/heroin and other drugs. Second, LTA was used to estimate the incidence of transitions in drug use latent classes over a one-year period. Third, racial differences in initial drug use and transitions over time were examined using multiple-groups LTA. Fourth, the effect of participation in an alcohol or drug treatment program on initial latent class membership and transitions over time were examined using LTA with covariates. Measurement invariance across time and groups is examined.</p>","PeriodicalId":89314,"journal":{"name":"Advances and applications in statistical sciences","volume":"3 2","pages":"203-235"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3171700/pdf/nihms227045.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30000313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaojun Hu, Gary L Gadbury, Qinfang Xiang, David B Allison
{"title":"Illustrations on Using the Distribution of a P-value in High Dimensional Data Analyses.","authors":"Xiaojun Hu, Gary L Gadbury, Qinfang Xiang, David B Allison","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Several statistical methods have recently been developed that use the distribution of P-values from multiple tests of hypotheses to analyze data from high-dimensional experiments. These methods are only as valid as the P-values that were derived from test statistics. If an incorrect distribution for a test statistic was used, the P-value will not be valid and the distribution of P-values from multiple test statistics could give misleading results. Moreover, if the correct distribution of a test statistic is used, a distribution of P-values may still give misleading results if P-values are correlated. A primary focus of this paper is on the distribution of a P-value under a null hypothesis, and the test statistic that is considered is the number of rejected null hypotheses. Two issues are demonstrated using six data examples, two that are simulated and four from actual microarray experiments. The results provide some insight into how much of an effect might be introduced into a distribution of P-values if invalid P-values are computed or if P-values are correlated. Additional illustration is given regarding the distribution of a P-value under an alternative hypothesis and some approaches to modeling it are presented.</p>","PeriodicalId":89314,"journal":{"name":"Advances and applications in statistical sciences","volume":"1 2","pages":"191-213"},"PeriodicalIF":0.0,"publicationDate":"2010-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4692473/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144182982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}