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Two‐stage model for time‐varying effects of discrete longitudinal covariates with applications in analysis of daily process data
dc.contributor.authorYang, Hanyuen_US
dc.contributor.authorCranford, James A.en_US
dc.contributor.authorLi, Runzeen_US
dc.contributor.authorBuu, Anneen_US
dc.date.accessioned2015-02-19T15:40:16Z
dc.date.available2016-04-01T15:21:07Zen
dc.date.issued2015-02-20en_US
dc.identifier.citationYang, Hanyu; Cranford, James A.; Li, Runze; Buu, Anne (2015). "Two‐stage model for time‐varying effects of discrete longitudinal covariates with applications in analysis of daily process data." Statistics in Medicine 34(4): 571-581.en_US
dc.identifier.issn0277-6715en_US
dc.identifier.issn1097-0258en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/110542
dc.publisherGuilford Pressen_US
dc.publisherWiley Periodicals, Inc.en_US
dc.subject.othermixed modelen_US
dc.subject.otherdaily process dataen_US
dc.subject.othermeasurement erroren_US
dc.subject.otheralcohol useen_US
dc.subject.otherfunctional dataen_US
dc.titleTwo‐stage model for time‐varying effects of discrete longitudinal covariates with applications in analysis of daily process dataen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelPublic Healthen_US
dc.subject.hlbsecondlevelMedicine (General)en_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/110542/1/sim6368.pdf
dc.identifier.doi10.1002/sim.6368en_US
dc.identifier.sourceStatistics in Medicineen_US
dc.identifier.citedreferenceBarta WD, Tennen H, Litt MD. Measurement reactivity in diary research. In Handbook of research methods for studying daily life, Mehl MR, Conner TS (eds). Guilford Press: New York, 2012; 108 – 123.en_US
dc.identifier.citedreferenceCollins LR, Kashdan TB, Koutsky JR, Morsheimer ET, Vetter CJ. A self‐administered timeline followback to measure variations in underage drinkers' alcohol intake and binge drinking. Addictive Behaviors 2008; 33: 196 – 200.en_US
dc.identifier.citedreferenceGwaltney CJ, Magill M, Barnett NP, Apodaca TR, Colby SM, Monti PM. Using daily drinking data to characterize the effects of a brief alcohol intervention in an emergency room. Addictive Behaviors 2011; 36: 248 – 250.en_US
dc.identifier.citedreferenceBardone AM, Krahn DD, Goodman BM, Searles JS. Using interactive voice response technology and timeline follow‐back methodology in studying binge eating and drinking behavior: different answers to different forms of the same question. Addictive Behaviors 2000; 25: 1 – 11.en_US
dc.identifier.citedreferenceSimpson TL, Kivlahan DR, Bush KR, McFall ME. Telephone self‐monitoring among alcohol use disorder patients in early recovery: a randomized study of feasibility and measurement reactivity. Drug and Alcohol Dependence 2005; 79: 241 – 250.en_US
dc.identifier.citedreferenceTucker JA, Blum ER, Xie L, Roth DL, Simpson CA. Interactive voice response self‐monitoring to assess risk behaviors in rural substance users living with HIV/AIDS. AIDS Behav 2012; 16: 432 – 440.en_US
dc.identifier.citedreferenceStritzke WGK, Dandy J, Durkin K, Houghton S. Use of interactive voice response (IVR) technology in health research with children. Behavior Research Methods 2005; 37: 119 – 126.en_US
dc.identifier.citedreferenceTourangeau R, Yan T. Sensitive questions in surveys. Psychological Bulletin 2007; 133: 859 – 883.en_US
dc.identifier.citedreferenceGreen PJ, Silverman BW. Nonparametric Regression and Generalized Linear Models. Chapman and Hall: London, 1994.en_US
dc.identifier.citedreferenceLin X, Breslow NE. Bias correction in generalized linear mixed models with multiple components of dispersion. Journal of the American Statistical Association 1996; 91: 1007 – 1016.en_US
dc.identifier.citedreferenceGreen PJ. Penalized likelihood for general semi‐parametric regression models. International Statistical Review 1987; 55: 245 – 259.en_US
dc.identifier.citedreferenceZhang D, Lin X, Raz J, Sowers MF. Semiparametric stochastic mixed models for longitudinal data. Journal of the American Statistical Association 1998; 93: 710 – 719.en_US
dc.identifier.citedreferenceBeck AT, Steer RA, Brown GK. Manual for Beck Depression Inventory‐II. Psychological Corporation: San Antonio, TX, 1996.en_US
dc.identifier.citedreferenceAmerican Psychiatric Association. Diagnostic and Statistical Manual 4th edition American Psychiatric Association: Washington DC, 1994.en_US
dc.identifier.citedreferenceCranford JA, Tennen H, Zucker RA. Feasibility of using interactive voice response to monitor daily drinking, moods, and relationship processes on a daily basis in alcoholic couples. Alcoholism: Clinical and Experimental Research 2010; 34: 499 – 508.en_US
dc.identifier.citedreferenceLin X, Zhang D. Inference in generalized additive mixed models by using smoothing splines. Journal of the Royal Statistical Society, Series B 1999; 61: 381 – 400.en_US
dc.identifier.citedreferenceRamsay JO, Silverman BW. Functional Data Analysis Second Editon. Springer: New York, 2005.en_US
dc.identifier.citedreferenceMuller HG, Stadtmuller U. Generalized functional linear models. The Annals of Statistics 2005; 33: 774 – 805.en_US
dc.identifier.citedreferenceZhang D, Lin X, Sowers MF. Two‐stage functional mixed models for evaluating the effect of longitudinal covariate profiles on a scalar outcome. Biometrics 2007; 63: 351 – 362.en_US
dc.identifier.citedreferenceMundt JC, Perrine MW, Searles JS, Walter D. An application of interactive voice response (IVR) technology to longitudinal studies of daily behavior. Behavior Research Methods, Instruments, & Computers 1995; 27: 351 – 357.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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