1,721,447 research outputs found
Analyzing Matched 2 x 2 Tables from all Corners
No full textSquared 2 x 2 tables with binary data from matched pairs are typically analyzed using Cochran-Mantel-Haenszel methodology, conditional logistic regression, or random intercepts logistic regression. These are all "pair-specific" type of approaches. However, many more methods and models for clustered binary data, including marginal models and marginalizable pair-specific models, can be applied. We provide a comprehensive overview of methods and apply them all to two well-known example datasets, the prime minister's performance and the myocardial infarction datasets. The simple setting of matched binary data allows us to compare and relate different models, methods and their estimates. A technical explanation is given for why in some settings boundary estimates are obtained. Supplementary materials for this article are available online
Asymptotic distribution theory for random u- and gm-statistics with application in sequential estimation
No full textNot availabl
Asymptotic distribution theory for random u- and gm-statistics with application in sequential estimation
No full textNot availabl
A solution to separation for clustered binary data
No full textThe presence of one or more covariates that perfectly or almost perfectly predict the outcome of interest (which is referred to as complete or quasi-complete separation, the latter denoting the case when such perfect prediction occurs only for a subset of observations in the data) has been extensively studied in the last four decades. Since 1984, when Albert and Anderson (1984) differentiated between complete and quasi-complete separation, several authors have studied this phenomenon and tried to provide answers or ways of identifying the problem (Lesaffre and Albert, 1989; Firth, 1993; Christmann and Rousseeuw, 2001; Rousseeuw and Christmann, 2003; Allison, 2004; Zorn, 2005; Heinze, 2006). From an estimation perspective, separation leads to infinite coefficients and standard errors, which makes the algorithm collapse or give inappropriate results. As a practical matter, separation forces the analyst to choose from a number of problematic alternatives for dealing with the problem, and in the past the elimination of such problematic variables were common practice to deal with such situations. In the last decade, solutions using penalized likelihood have been proposed, but always dealing with independent binary data. Here we will propose a Bayesian solution to the problem when we deal with clustered binary data using informative priors that are supported by the data and compare it with an alternative procedure proposed by Gelman et al. (2008).The authors gratefully acknowledge support from the fund of Scientific Research (FWO, Research Grant G.0151.05) and Belgian IUAP/PAI network P6/03 'Statistical Techniques and Modeling for Complex Substantive Questions with Complex Data' of the Belgian Government (Belgian Science Policy). We would also like to thank the Yves van der Stede (CODA, from Belgium) for providing the data which motivated this research
Bootstrapping multiparameter models, with applications to clustered binary data
No full textIt is shown how a one-step semiparametric bootstrap procedure can be applied to multiparameter models in different situations: for testing hypotheses, for the construction of simultaneous confidence intervals based on local polynomial smoothers and for improved estimation and bias correction. The method is illustrated on models for clustering binary dataThis project was partly supported by the NATO Collaborative Research Grant 950648. The research of Gerda Claeskens was supported by the Fund for Scientific Research - Flanders (Belgium) (FWO). We thank Professor R. Klein of the University of Wisconsin Madison, for kindly providing this data set (NIH grant EY 03083, Wisconsin Diabetic Retinopathy Study)
Bootstrapping multiparameter models, with applications to clustered binary data
No full textIt is shown how a one-step semiparametric bootstrap procedure can be applied to multiparameter models in different situations: for testing hypotheses, for the construction of simultaneous confidence intervals based on local polynomial smoothers and for improved estimation and bias correction. The method is illustrated on models for clustering binary dataThis project was partly supported by the NATO Collaborative Research Grant 950648. The research of Gerda Claeskens was supported by the Fund for Scientific Research - Flanders (Belgium) (FWO). We thank Professor R. Klein of the University of Wisconsin Madison, for kindly providing this data set (NIH grant EY 03083, Wisconsin Diabetic Retinopathy Study)
Factors associated with HIV serodiscordance among couples in Mozambique: Comparison of the 2009 INSIDA and 2015 IMASIDA surveys
No full textRecent studies suggest that a large proportion of new HIV-1 infections in mature epidemics occurs within discordant couples, making discordancy a major contributor to the spread of HIV/AIDS in Africa. This paper aims at assessing changes over a five-year period (2009-2015) on the (risk) factors associated with HIV serodiscordance among couples in Mozambique, using cross-sectional data from the INSIDA and IMASIDA surveys. The pooled data of both surveys were analyzed using a joint model for three parameters characterizing in a particular way disagreement and sero(con/dis)corance between the HIV statuses of couples, as introduced by Aerts et al.: the probability that the female partner is HIV positive, given that both partners differ in their HIV status, the probability that only one partner is HIV positive, given that at least one of the two partners is positive ("positive" serodiscordance), and the probability that both partners are negative given that at most one of the two partners is positive ("negative" seroconcordance). The results reveal similar significant factors and estimates as in Aerts et al. (HIV prevalence, union number for woman, STI for man, condom use by woman and wealth index), but the additional significant factors "condom use by man" (no use had a negative effect on the positive serodiscordance) and "union number for man" (for couples where the man has been married or co-habiting with a woman before had a decreased negative seroconcordance) were identified. The only factor that had a different effect over time (IMASIDA as compared to INSIDA) was the effect of "HIV prevalence of province" on the negative seroconcordance. The negative effect of a higher HIV prevalence was less pronounced in 2015 for negative seroconcordance.This study was financially supported by the Flemish Interuniversity Council (VLIR-UOS) in collaboration with Eduardo Mondlane University (UEM) through the DESAFIO Program, in Mozambique to AJCJ.Juga, AJC (corresponding author), Eduardo Mondlane Univ, Fac Sci, Dept Math & Informat, Maputo, Mozambique; Hasselt Univ, Data Sci Inst, I BioStat, Diepenbeek, Belgium.
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HIV risk factors among adolescent and young adults: A geospatial–temporal analysis of Mozambique AIDS indicator survey data
No full textIn a developing country, it is very crucial to know where the HIV/AIDS epidemic is much more prevalent and where direct interventions are needed, especially when managing limited and scarce resources. We therefore examine the spatial distribution of HIV in Mozambique, and also assess how the epidemic evolved over a six-year period (2009-2015), with respect to potential risk factors among adolescents and young adults. We used data from the 2009 and 2015 Mozambique AIDS indicator surveys. The data were analysed jointly, by extending the work of Muleia et al. (2020) to allow for different bivariate spatial smoothing functions for both surveys. The results showed considerable spatial variation. From 2009 to 2015, the probability to be HIV positive reduced by 43.6% for young women. The results also showed dependence of the probability for HIV infection on sociodemographic factors. The findings herein will help health officials design efficient target interventions.Universidade Eduardo Mondlane (UEM); Hasselt University (UH); Vlaamse Interuniversitaire Raad (VLIR-UOS) DESAFIO Progra
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