Multiple imputation using chained equations for missing data in survival models applied to multidrug-resistant tuberculosis and HIV data

Abstract

Background. 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 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, AIC and 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 for 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.

Assessing of case–cohort design: a case study for breast cancer patients in Xinjiang, China

“…The likelihood ratio test was used to evaluate Cox regression models in general and reflect the fitting effect of the models ( 25 ), based on the following formula,…”

“…To decrease the sampling error produced by simple random sampling, Prentice (17) proposed the case-cohort design in 1986. On the basis of simple random sampling, the case-cohort design analyzes those patients who have experienced outcome events in the full cohort, which is suitable for these studies with lower incidence of disease outcomes or higher costs of covariate collections (18)(19)(20), and compared with the simple random sampling, the case-cohort design may decrease the sample error (21,22). Yu et al (18) separately investigated the relationship between demographic characteristics, tumor histology, and time of onset and recurrence of nephroblastoma patients, under a case-cohort design.…”

“…Cai et al (19) employed a case-cohort design to identify the influencing factors of fungal infection in patients with hematopoietic cell transplant. Particularly, the case-cohort design is widely used to analyze the factors influencing morbidity or mortality of breast cancer (20)(21)(22). For example, based on the case-cohort design, Yang et al (20) used additive risk model to explore the major prognostic factors of patients with breast cancer.…”

“…The case-cohort design was employed to evaluate the prospective associations between perfluoroalkyl substances and breast cancer risk in (21). Yao et al (22) used case-cohort design to investigate the association of serum biomarker of vitamin D status, 25-hydroxyvitamin D values with breast cancer recurrence, and survival prognosis. It was indicated that the results based on the case-cohort design with fewer samples were similar to those based on the full cohort.…”