1Hitit University, Faculty of Medicine, Department of Biostatistics, Corum, Turkey
2Ankara University, Faculty of Medicine, Department of Biostatistics, Ankara, Turkey
3Hitit University, Osmancik Omer Derindere Vocational School, Department of Computer Technologies, Corum, Turkey
4Hitit University, Faculty of Medicine, Department of Gynecology and Obstetrics, Corum, Turkey
Aim: The aim of this study was to develop a new web-based R Shiny package that calculates propensity score using many algorithms such as logistic regression, machine learning, and performs matching analysis with balance evaluation. In addition, it was aimed to explain the process of matching analysis on a real data set by comparing the number of live births between those with methylenetetrahydrofolate reductase (MTHFR) homozygous mutations and those without mutations in women hospitalized due to abortion in the gynecology and obstetrics clinic.
Material and Methods: The web-based application was developed using R shiny. The “matchIt” library was used for matching analysis and PS prediction. The “cobalt” library was used to evaluate balance and generate plots.
Results: The abortion variable, which was statistically significantly different in the groups before matching (p=0.010), was similar in the groups after matching (p=0.743). In addition, when the descriptive statistics and p values of the other variables were examined, it was seen that almost full balance was achieved after matching and the confounder variables were similar distributed in groups. After matching analysis, it was determined that the result variable “livebirths” did not show statistically significant difference in the groups (p=0.864).
Conclusion: In this study, we developed an interactive web application for matching analysis based on propensity score. It is thought that this application will facilitate the studies of the researchers.
Keywords: Propensity score; matching; logistic regression; machine learning; R shiny