Multiple imputation procedures allow the rescue of missing data: An application to determine serum tumor necrosis factor (TNF) concentration values during the treatment of rheumatoid arthritis patients with anti-TNF therapy
Biol. Res
;
38(1): 7-12, 2005. ilus, tab
Artículo
en Inglés
| LILACS
| ID: lil-404822
RESUMO
Longitudinal studies aimed at evaluating patients clinical response to specific therapeutic treatments are frequently summarized in incomplete datasets due to missing data. Multivariate statistical procedures use only complete cases, deleting any case with missing data. MI and MIANALYZE procedures of the SAS software perform multiple imputations based on the Markov Chain Monte Carlo method to replace each missing value with a plausible value and to evaluate the efficiency of such missing data treatment. The objective of this work was to compare the evaluation of differences in the increase of serum TNF concentrations depending on the ¡308 TNF promoter genotype of rheumatoid arthritis (RA) patients receiving anti-TNF therapy with and without multiple imputations of missing data based on mixed models for repeated measures. Our results indicate that the relative efficiency of our multiple imputation model is greater than 98 percent and that the related inference was significant (p-value < 0.001). We established that under both approaches serum TNF levels in RA patients bearing the G/A ¡308 TNF promoter genotype displayed a significantly (p-value < 0.0001) increased ability to produce TNF over time than the G/G patient group, as they received successively doses of anti-TNF therapy.
Texto completo:
Disponible
Índice:
LILACS (Américas)
Asunto principal:
Artritis Reumatoide
/
Modelos Estadísticos
/
Regiones Promotoras Genéticas
/
Factor de Necrosis Tumoral alfa
/
Antirreumáticos
/
Anticuerpos Monoclonales
Tipo de estudio:
Evaluación Económica en Salud
/
Estudio observacional
/
Estudio pronóstico
/
Factores de riesgo
Límite:
Humanos
Idioma:
Inglés
Revista:
Biol. Res
Asunto de la revista:
Biologia
Año:
2005
Tipo del documento:
Artículo
País de afiliación:
Chile
Institución/País de afiliación:
University of Chile Clinical Hospital/CL
/
University of Chile/CL
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