ABSTRACT
The aim of the article is demonstrating an application of multiple imputation [MI] for handling missing clinical data in the setting of rheumatologic surveys using data derived from 10291 people participating in the first phase of the Community Oriented Program for Control of Rheumatic Disorders [COPCORD] in Iran. Five data subsets were produced from the original data set. Certain demographics were selected as complete variables. In each subset, we created a univariate pattern of missingness for knee osteoarthritis status as the outcome variable [disease] using different mechanisms and percentages. The crude disease proportion and its standard error were estimated sgscrately for each complete data set to be used as true [baseline] values for percent bias calculation. The parameters of interest were also estimated for each incomplete data subset using two approaches to deal with missing data including complete case analysis [CCA] and MI with various imputation numbers. The two approaches were compared using appropriate analysis of variance. With CCA, percent bias associated with missing data was 8.67 [95% CI: 7.81-9.53] for the proportion and 13.67 [95% CI: 12.60-14.74] for the standard error. However, they were 6.42 [95% CI: 5.56-7.29] and 10.04 [95% CI: 8.97-11.11], respectively using the MI method [M=15]. Percent bias in estimating disease proportion and its standard error was significantly lower in missing data analysis using MI compared with CCA [P< 0.05]. To estimate the prevalence of rheumatic disorders such as knee osteoarthritis, applying MI using available demographics is superior to CCA
Subject(s)
Humans , Osteoarthritis, KneeABSTRACT
Different criteria for Behcet's diagnosis reveal that there isn't still consensus about the definition of this syndrome. Although a comprehensive study about the accuracy, the sensitivity and specificity of seven different types of diagnostic criteria for Behcet's disease in children, Ras not been performed we planned this study to compare these criteria in the Iranian children with Behcet's disease. Two random samples were selected from the patients less than 16 years of age referring to the Behcet's clinic of rheumatology research center. 177 children had definite diagnosis of Behcet's disease after being visited by the three professors of rheumatology experienced in Behcet's syndrome. Control group were children suspected to have Behcet's disease but it was ruled out. Sensitivity, specificity and accuracy of each of the Behcet's criteria were calculated. Mean age in Behcet's children was 13.6 years and in control group was 12.8 years. In the patients group female to male ratio was 1.1 and in the control group it was 1.3. Sensitivity of different criteria was as follow: Manson and Bames 55.1%, 0' Duffy 60.7%, International criteria 71.9%, Japan criteria 90.4%, Dilsen criteria 79.8%, Iran criteria 93.8%, and Iranian classification tree 94.4%. Specificity of the criteria was as following; M and B 99.5%, O Duffy 99.5%, International 100%, Japan 96.2%, Dilsen 94.6%, Iran 96.2%, classification tree 96.2%. Although some of the researchers believe that current diagnostic criteria of Behcet's diseases due to establishment on the adults clinical data, is not appropriate for children; our study revealed that the current seven Adult BD criteria are reliable and accurate in children too