Weibull mixture regression for marginal inference in zero-heavy continuous outcomes.
Continuous outcomes with preponderance of zero values are ubiquitous in data that arise from biomedical studies, for example studies of addictive disorders. This is known to lead to violation of standard assumptions in parametric inference and enhances the risk of misleading conclusions unless manag...
| Publicado en: | Statistical Methods in Medical Research Vol. 26; no. 3; pp. 1476 - 1500 |
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| Autores principales: | , , , , |
| Formato: | clinical trial equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Jun2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=123549800&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 123549800 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09622802 31F jtl: Statistical Methods in Medical Research issn: 09622802 maglogo: Y pubinfo: dt: Jun2017 vid: 26 iid: 3 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 123549800 123549800 NLM25902801 123549800 10.1177/0962280215583402 NLM25902801 123549800 ppf: 1476 ppct: 24 formats: tig: atl: Weibull mixture regression for marginal inference in zero-heavy continuous outcomes. aug: au: Gebregziabher, Mulugeta Voronca, Delia Teklehaimanot, Abeba Ana, Elizabeth J. Santa Santa Ana, Elizabeth J affil: Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA sug: subj: Models, Statistical Substance Use Disorders Therapy Regression Statistics Behavior, Addictive Treatment Outcomes Probability Software Motivational Interviewing Substance Use Disorders Psychosocial Factors Clinical Trials Human ab: Continuous outcomes with preponderance of zero values are ubiquitous in data that arise from biomedical studies, for example studies of addictive disorders. This is known to lead to violation of standard assumptions in parametric inference and enhances the risk of misleading conclusions unless managed properly. Two-part models are commonly used to deal with this problem. However, standard two-part models have limitations with respect to obtaining parameter estimates that have marginal interpretation of covariate effects which are important in many biomedical applications. Recently marginalized two-part models are proposed but their development is limited to log-normal and log-skew-normal distributions. Thus, in this paper, we propose a finite mixture approach, with Weibull mixture regression as a special case, to deal with the problem. We use extensive simulation study to assess the performance of the proposed model in finite samples and to make comparisons with other family of models via statistical information and mean squared error criteria. We demonstrate its application on real data from a randomized controlled trial of addictive disorders. Our results show that a two-component Weibull mixture model is preferred for modeling zero-heavy continuous data when the non-zero part are simulated from Weibull or similar distributions such as Gamma or truncated Gauss. pubtype: Academic Journal doctype: clinical trial equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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