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dc.contributor.authorKnafl, George J.
dc.contributor.authorFennie, Kristopher P.
dc.contributor.authorBova, Carol A.
dc.contributor.authorDieckhaus, Kevin D.
dc.contributor.authorWilliams, Ann B.
dc.date2022-08-11T08:09:04.000
dc.date.accessioned2022-08-23T16:17:00Z
dc.date.available2022-08-23T16:17:00Z
dc.date.issued2004-02-26
dc.date.submitted2008-06-16
dc.identifier.citationStat Med. 2004 Mar 15;23(5):783-801. <a href="http://dx.doi.org/10.1002/sim.1624">Link to article on publisher's site</a>
dc.identifier.issn0277-6715 (Print)
dc.identifier.doi10.1002/sim.1624
dc.identifier.pmid14981675
dc.identifier.urihttp://hdl.handle.net/20.500.14038/34456
dc.description.abstractAn adaptive approach to Poisson regression modelling is presented for analysing event data from electronic devices monitoring medication-taking. The emphasis is on applying this approach to data for individual subjects although it also applies to data for multiple subjects. This approach provides for visualization of adherence patterns as well as for objective comparison of actual device use with prescribed medication-taking. Example analyses are presented using data on openings of electronic pill bottle caps monitoring adherence of subjects with HIV undergoing highly active antiretroviral therapies. The modelling approach consists of partitioning the observation period, computing grouped event counts/rates for intervals in this partition, and modelling these event counts/rates in terms of elapsed time after entry into the study using Poisson regression. These models are based on adaptively selected sets of power transforms of elapsed time determined by rule-based heuristic search through arbitrary sets of parametric models, thereby effectively generating a smooth non-parametric regression fit to the data. Models are compared using k-fold likelihood cross-validation.
dc.language.isoen_US
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=14981675&dopt=Abstract ">Link to article in PubMed</a>
dc.relation.urlhttp://dx.doi.org/10.1002/sim.1624
dc.subjectAntiretroviral Therapy, Highly Active
dc.subjectHIV Infections
dc.subjectHumans
dc.subjectLikelihood Functions
dc.subjectMonitoring, Physiologic
dc.subjectPatient Compliance
dc.subject*Poisson Distribution
dc.subjectNursing
dc.subjectPublic Health and Community Nursing
dc.titleElectronic monitoring device event modelling on an individual-subject basis using adaptive Poisson regression
dc.typeArticle
dc.source.journaltitleStatistics in medicine
dc.source.volume23
dc.source.issue5
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/gsn_pp/13
dc.identifier.contextkey531228
html.description.abstract<p>An adaptive approach to Poisson regression modelling is presented for analysing event data from electronic devices monitoring medication-taking. The emphasis is on applying this approach to data for individual subjects although it also applies to data for multiple subjects. This approach provides for visualization of adherence patterns as well as for objective comparison of actual device use with prescribed medication-taking. Example analyses are presented using data on openings of electronic pill bottle caps monitoring adherence of subjects with HIV undergoing highly active antiretroviral therapies. The modelling approach consists of partitioning the observation period, computing grouped event counts/rates for intervals in this partition, and modelling these event counts/rates in terms of elapsed time after entry into the study using Poisson regression. These models are based on adaptively selected sets of power transforms of elapsed time determined by rule-based heuristic search through arbitrary sets of parametric models, thereby effectively generating a smooth non-parametric regression fit to the data. Models are compared using k-fold likelihood cross-validation.</p>
dc.identifier.submissionpathgsn_pp/13
dc.contributor.departmentCenter for Infectious Disease and Vaccine Research
dc.contributor.departmentGraduate School of Nursing
dc.source.pages783-801


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