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

Abstract

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% 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.

Effects of 1-year yoga on cardiovascular risk factors in middle-aged and older adults with metabolic syndrome: a randomized trial

“…Both per protocol analysis (PP) and intention-to-treat analysis (ITT) were performed. Intention-to-treat analysis was conducted by following a publish protocol with multiple imputation procedure [ 16 ]. All statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 21 for Windows.…”

Influence of the First Consultation on Adherence to Antiretroviral Therapy for HIV-infected Patients

“…Missing values were imputed using an iterative Markov Chain Monte Carlo (MCMC) method [ 33 ]. Categorical data are reported as frequency and percentage and continuous data as mean and standard deviation.…”

Integrating Epidermal Growth Factor Receptor Assay With Clinical Parameters Improves Risk Classification for Relapse and Survival in Head-and-Neck Squamous Cell Carcinoma

“…In patients without available tumor specimens for laboratory assays, missing values were imputed 10 times and the most frequently occurring model was selected from examining all 10 separate RPA trees. The relative risks comparing the survival classes from the 10 imputations were combined using SAS software and the PROC MI and MIANALYZE procedures (34). Terminal nodes were further combined into classes based on visual inspection of Kaplan-Meier plots.…”