Estimating overall exposure effects for the clustered and censored outcome using random effect Tobit regression models.
The random effect Tobit model is a regression model that accommodates both left- and/or right-censoring and within-cluster dependence of the outcome variable. Regression coefficients of random effect Tobit models have conditional interpretations on a constructed latent dependent variable and do not...
| Publicado en: | Statistics in Medicine Vol. 35; no. 27; pp. 4948 - 4961 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
11/30/2016
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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=119202394&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 119202394 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: 11/30/2016 vid: 35 iid: 27 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 119202394 119202394 NLM27449636 10.1002/sim.7045 NLM27449636 119202394 ppf: 4948 ppct: 13 formats: tig: atl: Estimating overall exposure effects for the clustered and censored outcome using random effect Tobit regression models. aug: au: Wang, Wei Griswold, Michael E. affil: Center of Biostatistics and Bioinformatics, New Guyton Research Building G562, University of Mississippi Medical Center, 2500 North State Street, Jackson MS, 39216, U.S.A. sug: subj: Probability Treatment Outcomes Computer Simulation Models, Statistical Algorithms ab: The random effect Tobit model is a regression model that accommodates both left- and/or right-censoring and within-cluster dependence of the outcome variable. Regression coefficients of random effect Tobit models have conditional interpretations on a constructed latent dependent variable and do not provide inference of overall exposure effects on the original outcome scale. Marginalized random effects model (MREM) permits likelihood-based estimation of marginal mean parameters for the clustered data. For random effect Tobit models, we extend the MREM to marginalize over both the random effects and the normal space and boundary components of the censored response to estimate overall exposure effects at population level. We also extend the 'Average Predicted Value' method to estimate the model-predicted marginal means for each person under different exposure status in a designated reference group by integrating over the random effects and then use the calculated difference to assess the overall exposure effect. The maximum likelihood estimation is proposed utilizing a quasi-Newton optimization algorithm with Gauss-Hermite quadrature to approximate the integration of the random effects. We use these methods to carefully analyze two real datasets. Copyright © 2016 John Wiley & Sons, Ltd. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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