Authors: A.-N. Fataliieva, PhD Student, ORCID ID: 0000-0001-5541-8509,
D. Shamaida, PhD Student, ORCID ID: 0000-0001-6080-2425
Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
Annotation: Nowadays, social economics focuses on many critical issues; among them, public health and morbidity are among the highest priorities since they directly impact human capital formation, which is an important component in the development of the economy.
Within public health issues, one of the crucial directions is the analysis of the effectiveness of drugs, which is typically performed
on micro-level involving patients in hospitals. The data collected usually is not complete, and it causes problems during the
analysis as if a significant part of the critical data is missed, that invalidates finding. The multiple imputation method is one of the
most common approaches in dealing with this problem.
Both primary and sensitivity analysis were performed involving multiple imputation approaches. While the preliminary analysis
was performed assuming that the missing-data values are overlooked at random, the sensitivity analysis was conducted on the
two approaches of missing not-at-random algorithm – the pattern mixture models and the tipping point method. In the paper, the
methodological aspects of the usage of these methods were highlighted. Also, the practical implementation of these methods was
given in the example of imputing the missing values of the laboratory parameter at different time points with subsequent calculation
of AUC and testing the hypothesis of drug efficacy using the analysis of the covariance model. The primary analysis showed the
effectiveness of the new drug compared to the placebo. A sensitivity analysis proved the results of the primary analysis. The
tipping point method showed that if the assumption that the mean value of dropout is more significant than observed values for
more than 196, then the result of the primary analysis is questionable.
Keywords: public health; morbidity; human capital; missing data; multiple imputations.
Received: 05/09/2022
1st Revision: 13/09/2022
Accepted: 04/10/2022
DOI: https://doi.org/10.17721/1728-2667.2022/220-3/5
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