Title of article :
Prevention of Disease Complications through Diagnostic Models: How to Tackle the Problem of Missing Data?
Baneshi, MR Reserch Center for Modeling in Health - Kerman University of Medical Sciences, Kerman , Faramarzi, H Shiraz HIV/AIDS Research Center - Shiraz University of Medical Sciences, Shiraz , Marzban, M Research Center for Traditional Medicine and History of Medicine - Shiraz University of Medical Sciences, Shiraz
Background: Diagnostic models are frequently used to assess the role of risk factors on disease complications, and
therefore to avoid them. Missing data is an issue that challenges the model making. The aim of this study was to
develop a diagnostic model to predict death in HIV/ AIDS patients when missing data exist.
Methods: HIV patients (n=1460) referred to Voluntary Consoling and Testing Center (VCT) of Shiraz southern Iran
during 2004-2009 were recruited. Univariate association between variables and death was assessed. Only variables
which had univariate P< 0.25 were selected to be offered to the Multifactorial models. First, patients with missing data
on candidate variables were deleted (C-C model). Then, applying Multivariable Imputation via Chained Equations
(MICE), missing data were imputed. Logistic regression was fitted to C-C and imputed data sets (MICE model). Models
were compared in terms of number of variables retained in the final model, width of confidence intervals, and
Result: About 22% of data were lost in C-C model. Number of variables retained in the C-C and MICE models was 2
and 6 respectively. Confidence Intervals (C.I.) corresponding to C-C model was wider than that of MICE. The MICE
model showed greater discrimination ability than C-C model (70% versus 64%).
Conclusion: The C-C analysis resulted to loss of power and wide CI's. Once missing data were imputed, more variables
reached significance level and C.I.'s were narrower. Therefore, we do recommend the application of the imputation
method for handling missing data.
HIV/AIDS , Missing Data , Imputation , MICE
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