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http://hdl.handle.net/1942/368
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DC Field | Value | Language |
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dc.contributor.author | Lipsitz, Stuart R. | - |
dc.contributor.author | IBRAHIM, Joseph | - |
dc.contributor.author | MOLENBERGHS, Geert | - |
dc.date.accessioned | 2004-10-25T12:00:38Z | - |
dc.date.available | 2004-10-25T12:00:38Z | - |
dc.date.issued | 2000 | - |
dc.identifier.citation | JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 49(3). p. 287-296 | - |
dc.identifier.issn | 0035-9254 | - |
dc.identifier.uri | http://hdl.handle.net/1942/368 | - |
dc.description.abstract | We analyse longitudinal data on CD4 cell counts from patients who participated in clinical trials that compared two therapeutic treatments: zidovudine and didanosine. The investigators were interested in modelling the CD4 cell count as a function of treatment, age at base-line and disease stage at base-line. Serious concerns can be raised about the normality assumption of CD4 cell counts that is implicit in many methods and therefore an analysis may have to start with a transformation. Instead of assuming that we know the transformation (e.g. logarithmic) that makes the outcome normal and linearly related to the covariates, we estimate the transformation, by using maximum likelihood, within the Box–Cox family. There has been considerable work on the Box–Cox transformation for univariate regression models. Here, we discuss the Box–Cox transformation for longitudinal regression models when the outcome can be missing over time, and we also implement a maximization method for the likelihood, assumming that the missing data are missing at random. | - |
dc.description.sponsorship | We are grateful for the support provided by grants CA 57253, CA 55576, CA 70101-01, CA74015-01 and GM 29745 from the National Institutes of Health and by funding from the National Fonds voor Wetenschappelijk Onderzoek (Belgium, project `Sensitivity analysis for incomplete data'), and Nato collaborative research grant 950648. | - |
dc.language.iso | en | - |
dc.publisher | BLACKWELL PUBLISHING | - |
dc.rights | (C) 2000 Royal Statistical Society | - |
dc.subject | Longitudinal data | - |
dc.subject | Multivariate data | - |
dc.subject | Missing data | - |
dc.subject.other | CD4 cell counts; incomplete data; influence graph; maximum likelihood; sensitivity analysis | - |
dc.title | Using a Box-Cox transformation in the analysis of longitudinal data with incomplete responses | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 296 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 287 | - |
dc.identifier.volume | 49 | - |
local.bibliographicCitation.jcat | A1 | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.bibliographicCitation.oldjcat | A1 | - |
dc.identifier.doi | 10.1111/1467-9876.00192 | - |
dc.identifier.isi | 000087038300008 | - |
item.contributor | Lipsitz, Stuart R. | - |
item.contributor | IBRAHIM, Joseph | - |
item.contributor | MOLENBERGHS, Geert | - |
item.fullcitation | Lipsitz, Stuart R.; IBRAHIM, Joseph & MOLENBERGHS, Geert (2000) Using a Box-Cox transformation in the analysis of longitudinal data with incomplete responses. In: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 49(3). p. 287-296. | - |
item.fulltext | With Fulltext | - |
item.accessRights | Restricted Access | - |
item.validation | ecoom 2001 | - |
crisitem.journal.issn | 0035-9254 | - |
crisitem.journal.eissn | 1467-9876 | - |
Appears in Collections: | Research publications |
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Lipsitz_et_al-2000-Journal_of_the_Royal_Statistical_Society__Series_C_(Applied_Statistics).pdf Restricted Access | Published version | 169.73 kB | Adobe PDF | View/Open Request a copy |
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