THE APPLICATION OF PATTERN MIXTURE MODELS AND TIPPING POINT ANALYSIS IN SOCIAL RESEARCH

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

References (in Latin): Translation / Transliteration/ Transcription
1. Kovtun N. V., Fataliieva A.-N. Y. New trends in evidence-based statistics: data imputation problems. Statistics of Ukraine. 2019. № 87(4).
P. 4–13.
2. Mishchuk O. S., Tkachenko R. O. Methods of processing and filling of missing parameters in ecological monitoring data. Scientific Bulletin of
UNFU. 2019. № 29(6). P. 119–122.
3. Ratitch B., O’Kelly M.. Implementation of Pattern-Mixture Models Using Standard SAS/STAT Procedures. Proceedings of PharmaSUG. 2011.
URL: https://www.pharmasug.org/proceedings/2011/SP/PharmaSUG-2011-SP04.pdf
4. Yuan Y. Sensitivity Analysis in Multiple Imputation for Missing Data. Paper SAS Institute Inc. 2014.
5. Smuk M. Missing Data Methodology: Sensitivity analysis after multiple imputation. PhD thesis, London School of Hygiene & Tropical
Medicine. 2015.
6. Little R. J. A. Pattern-Mixture Models for Multivariate Incomplete Data. Journal of the American Statistical Association. 1993. № 88. P. 125–134.
7. Ratitch B., O’Kelly M., Tosiello R. Missing data in clinical trials: from clinical assumptions to statistical analysis using pattern mixture models. Pharmaceutical Statistics. 2013. Vol. 12, Is. 6. P. 337–347.
8. Tipping point analysis – multiple imputation for stress test under missing not at random (MNAR). URL: https://onbiostatistics.blogspot.com/
2015/08/tipping-point-analysis-multiple.html
9. Rubin D. B. Multiple imputation for nonresponse in surveys. New York : John Wiley & Sons, Inc., 1987.
10. Little R., Yau L. Intent-to-treat analysis for longitudinal studies with drop-outs. Biometrics. 1996. Vol. 52. P.1324–1333.
11. Brand J. P. L. Development, implementation, and evaluation of multiple imputation strategies for the statistical analysis of incomplete data
sets. Ph.D. thesis, Erasmus University, 1999. URL: https://core.ac.uk/download/pdf/18508128.pdf
12. Berglund P. and Heeringa S. Multiple imputation of missing data using SAS, Cary. NC : SAS Institute Inc., 2014. URL: https://support.sas.com/
content/dam/SAS/support/en/books/multiple-imputation-of-missing-data-usingsas/65370_excerpt.pdf
13. Kenward M. G. The handling of missing data in clinical trials. Clin. Investig. (Lond.). 2013. № 3. P. 241–250. URL: https://www.openaccessjournals.com/articles/the-handling-of-missing-data-in-clinical-trials.pdf
14. Molenberghs G., Kenward M. G. Missing data in clinical studies. New York : John Wiley & Sons, 2007. URL: https://download.ebookshelf.de/download/0000/5740/97/L-G-0000574097-0002359047.pdf
15. Molenberghs G. Incomplete data in clinical studies: analysis, sensitivity, and sensitivity analysis. Drug information journal. 2009. № 43(4).
P. 409–429.
16. Carpenter J. R., Kenward M. G. Multiple Imputation and Its Application. New York : John Wiley & Sons, 2013.
17. Van Buuren S. Flexible Imputation of Missing Data. Boca Raton, FL : Chapman & Hall/CRC, 2012.

Download