Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 7 3 2021 10 23 251 262 Sara Sabbaghian Tousi Department of Epidemiology and Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran Hamed Tabesh Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran Azadeh Saki Department of Epidemiology and Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran , Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. Ali Tagipour Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran , Department of Epidemiology, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran Mohammad Tajfard Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran , Department of Health Education and Health Promotion, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran. 2021 04 28 2021 08 14 Introduction: Propensity score matching (PSM) is a method to reduce the impact of essential and confounders. When the number of confounders is high, there may be a problem of matching, in which, finding matched pairs for the case group is difficult, or impossible. The propensity score (PS) minimizes the effect of the confounders, and it is reduced to one dimension. There are various algorithms in the field of PSM. This study aimed to compared the nearest neighbor and caliper algorithms.  Methods: Data obtained in this study were from patients undergoing angiography at Ghaem Hospital in Mashhad, between 2011-12. The study was a retrospective case-control using PSM. In total, 604 patients were included in the case and control groups. A logistic regression model was used to calculate the propensity score and adjust the variables, such as age, gender, Body Mass Index (BMI), systolic blood pressure, smoking status, and triglyceride. Then, the Odds Ratios (ORs) with 95% Confidence Intervals (CIs) for the raw data and two matching algorithms were determined to examine the relationship between type 2 diabetes and coronary artery disease (CAD).  Results: Propensity score in the nearest neighbor and caliper algorithms matched the total number of 604 samples, 200 and 178 pairs, respectively. All variables were significantly different between the two groups before matching (P<0.05). The gender was significantly different between the two groups after matching using the nearest neighbor algorithm (P=0.002). No variables created a significant difference between the two groups after matching with the caliper algorithm.  Conclusion: Bias reduction in the caliper algorithm was greater than for the nearest neighbor algorithm for all variables except the triglyceride variable.  https://jbe.tums.ac.ir/index.php/jbe/article/view/497