Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/5161
Title: Multiple imputation using chained equations for missing data in survival models : applied to multidrug-resistant tuberculosis and HIV data
Authors: Mbona, Sizwe Vincent
Ndlovu, Principal 
Mwambi, Henry
Ramroop, Shaun
Keywords: Missing data;Multiple imputation;Multidrug-resistance tuberculosis;Missing data;Multidrug-resistance tuberculosis;Multiple imputation
Issue Date: 2023
Publisher: PAGEPress Publications
Source: Mbona, S.V. et al. 2023. Multiple imputation using chained equations for missing data in survival models: applied to multidrug-resistant tuberculosis and HIV data. Journal of Public Health in Africa. 14(8): 2388-. doi:10.4081/jphia.2023.2388
Journal: Journal of Public Health in Africa; Vol. 14, Issue 8 
Abstract: 
Missing data are a prevalent problem in almost all types of data analyses, such as survival data analysis.

Objective

To evaluate the performance of multivariable imputation via chained equations in determining the factors that affect the survival of multidrug-resistant-tuberculosis (MDR-TB) and HIV-coinfected patients in KwaZulu-Natal.

Materials and methods

Secondary data from 1542 multidrug-resistant tuberculosis patients were used in this study. First, data from patients with some missing observations were deleted from the original data set to obtain the complete case (CC) data set. Second, missing observations in the original data set were imputed 15 times to obtain complete data sets using a multivariable imputation case (MIC). The Cox regression model was fitted to both the CC and MIC data, and the results were compared using the model goodness of fit criteria [likelihood ratio tests, Akaike information criterion (AIC), and Bayesian Information Criterion (BIC)].

Results

The Cox regression model fitted the MIC data set better (likelihood ratio test statistic =76.88 on 10 df with P<0.01, AIC =1040.90, and BIC =1099.65) than the CC data set (likelihood ratio test statistic =42.68 on 10 df with P<0.01, AIC =1186.05 and BIC =1228.47). Variables that were insignificant when the model was fitted to the CC data set became significant when the model was fitted to the MIC data set.

Conclusion

Correcting missing data using multiple imputation techniques for the MDR-TB problem is recommended. This approach led to better estimates and more power in the model.
URI: https://hdl.handle.net/10321/5161
ISSN: 2038-9922
2038-9930 (Online)
DOI: 10.4081/jphia.2023.2388
Appears in Collections:Research Publications (Applied Sciences)

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