Iranian Journal of

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Background and Objectives: Missing data exist in many studies, e.g. in regression models, and they decrease the model's efficacy. Many methods have been suggested for handling incomplete data: they have generally focused on missing outcome values. But covariate values can also be missing.
Materials and Methods: In this paper we study the missing imputation by the EM algorithm and auxiliary variable and compare the result with case-complete analysis in a logistic regression model dealing with factors that influence the choice of the delivery method.
Our data came from a cross-sectional study of factors associated with the choice of the delivery method in pregnant women. The sample size in this cross-sectional study was 365 and the data were collected through interviews, using questionnaires covering several demographic variables, delivery history, attitude, and some social factors. We used standard deviations to compare the efficiency of the two methods.
Results: The results show that maximum likelihood analysis by EM algorithm is more effective than case-complete analysis.
The problem of missing data is common in surveys and it causes bias and decreased model efficacy. Here we show that the EM algorithm for imputation in logistic regression with missing values for a discrete covariate is more effective than case-complete analysis.
Conclusion: On the other hand if missing values occur for a continuous covariate then we have to use other methods or change the variable into a discrete one.

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In the previous paper, the basic concepts of sample size calculation were presented. This paper explores main post-calculation adjustments of the sample size calculation in special circumstances such as multiple group comparisons, unbalanced studies (with unequal number of subjects in different groups) sample size correction for missing data, and adjustment for finite population size. In addition, the concept of design effect in multi-stage sampling

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  Background & Objectives : Longitudinal studies are used in many psychiatric researches to evaluate the effectiveness of treatment. The main characteristic of longitudinal studies is repeated measurements of the patients over time. Since observations from the same patient are not independent from each other, especial statistical methods must be used for analyzing the data. Missing data is an indispensable component in longitudinal. In this study, we examined the effect of comprehensive treatment on social-individual performance in patients with the first episode of psychosis.

  Methods : The data was from a clinical trial involving patients who were admitted to the clinics of Roozbeh Hospital between 2006_2008. We employed a random effect model for the analysis of longitudinal ordinal responses with non-monotone missingness using the R software version 3.0.2.

 Results: The results showed that comprehensive treatment with follow-up at home, age, and family history of the disease had a significant effect on the social-individual performance of the patients. The estimation of the coefficient of age and its standard deviation were 0.05 and 0.03, respectively. The estimation of the coefficient of family history of the disease was -0.82 with a standard deviation of 0.41, and the coefficient of comprehensive treatment with follow-up at home and its standard deviation, were estimated -1.04 and 0.44, respectively.

  Conclusion: The model used in this study showed that the comprehensive treatment with follow-up at home was better because individuals under this type of treatment are more likely to have social-individual performance.

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Background and Objectives: Missing data is a big challenge in the research. According to the type of the study and of the variables, different ways have been proposed to work with these data. This study compared five popular imputation approaches in addressing missing data in the questionnaires.

Methods: In this study, 500 questionnaires were used for self-medication in diabetic patients. Missing in the observations was artificially generated by random selection of questions and then deleting them. Five imputation ways included: 1) the mean of the questions, 2) the mean of the person, 3) the mode of the person, 4) linear regression, and 5) EM algorithm. For each method, the mean and standard deviation were compared with imputation. The Spearman correlation coefficient, the percentage of incorrectly classified and kappa statistic were also calculated.

Results: A kappa higher than 0.81 represented almost perfect agreement at 10% missingness. The EM algorithm showed the highest level of agreement with the results of actual data with a Kappa of 0.886. With increasing missingness to 30%, the EM algorithm and the mean of  the person showed a rather similar agreement with a Kappa of 0.697 and 0.687, respectively.

Conclusion: In this study, the EM algorithm was the most accurate method for handling missing data in all models. The mean of the person method is easy for handling missing data, especially for most non statisticians.