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Application of Random Effects Models and Other Methods to the Analysis of Multidimensional Quality of Life Data in an Aids Clinical Trial

Dados Bibliográficos

ID9104970
AutoresAlbert W Wu (0000-0001-6189-7120, Johns Hopkins University, autor correspondente), Sarah M Gray (0000-0002-4025-6487, Johns Hopkins University), Ronald Brookmeyer (Johns Hopkins University)
Ano1999
Volume37
Fascículo3
Páginas249-258
Data de publicação1999-03-01
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoMedical Care (JOURNAL)
Identificadores do periódicoISSN: 0025-7079 • E-ISSN: 1537-1948
EditoraOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/00005650-199903000-00005
PMID10098569
OpenAlexW2072215970
IdiomaEN
Citações recebidas3
Referências citadas22

BACKGROUND: Current analytic methods applied to multidimensional health-related quality of life (HRQOL) data do not borrow strength across analyses and do not produce summary estimates of effect. OBJECTIVES: To compare a random effects modelling approach for the analysis of multidimensional HRQOL data to the following: (1) separate analyses for each dimension; (2) O'Brien's global test procedure; and (3) multivariate analysis of variance (MANOVA). RESEARCH DESIGN: Randomized clinical trial comparing 3 treatments (Trimethoprim-Sulfamethoxazole [TS], Dapsone-Trimethoprim [DT], and Clindamycin-Primaquine [CP] for Pneumocystis carinii pneumonia [PCP]). SUBJECTS: Patients with PCP enrolled in AIDS Clinical Trials Group Protocol 108. MEASURES: A 33-item battery assessing 7 dimensions of HRQOL: physical functioning, pain, energy, general health perceptions, disability, pulmonary symptoms, and constitutional symptoms. RESULTS: Analyses focused on changes in score from baseline to Day 7 (n = 145). Separate analyses for each dimension suggested a trend favoring CP versus TS, but using a Bonferroni correction no differences were statistically significant. O'Brien's global procedure for a test of no-treatment effect versus superiority of one treatment yielded P = 0.07. MANOVA did not reveal significant differences among treatment groups. A random effects model using fixed treatment and dimension effects and separate random effects for each person showed a significant overall treatment effect (P = 0.02); changes in scores for CP averaged 10 points greater than for TS. CONCLUSIONS: Random-effects models provide a flexible class of models for analyzing multidimensional quality of life data and estimating treatment effects because they borrow strength across dimensions

Analysis of variance · Bonferroni correction · Clinical trial · Meta-analysis · Multivariate analysis of variance · Quality of life (healthcare) · Random effects model · Randomized controlled trial · Repeated measures design · Statistics · HIV/AIDS Research and Interventions · Internal Medicine · Mathematics · Medicine · Pneumocystis jirovecii pneumonia detection and treatment · Spondyloarthritis Studies and Treatments

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Obras citantes distintas3
Citações por ano0,12
Intervalo de citações2001 - 2003 (3)
Velocidade de citaçãohistorical
Altamente citadoNão
Tipos de citaçãoNeutras: 3
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