To match patients between the POAG and the NTG group according to age and axial length, propensity score analysis was performed. The propensity scores were estimated using multiple logistic regression analysis.
21 A propensity score was calculated using the logistic equation for each patient; age and axial length were the explanatory variables. Using predicted probabilities, we sought to match an individual in the POAG group with the closest individual in the NTG group using propensity score values. Using the Greedy 5→1 digit match
22 algorithm, we created propensity score-matched pairs without replacement (a 1:1 match). Specifically, we sought to match each patient with a propensity score that was identical to five digits. If this could not be done, the algorithm proceeded sequentially to the next highest digit match (four-, three-, two-, or one-digit match) until no further matches were possible. From the initial 125 eyes with POAG and 156 eyes with NTG, we were able to match 78 eyes from the POAG group with 78 eyes from the NTG group.
The independent t-test and χ2 test for independent samples were used to assess the differences between the two groups. To determine the factors related to the degree of ONH torsion, univariate and multivariate linear regression analyses were performed. The dependent variable was the degree of ONH torsion by OCT measurements. The independent variables were age, axial length, MD of the VF, vertical tilt degree, horizontal tilt degree, maximum tilt degree, PPA area, CCT, and diagnosis. Because the diagnosis was nominal in scale, it was investigated as an independent factor using a regression model, and dummy variables were performed using the POAG group as the standard. The variables that retained significance at P < 0.10 in the univariate analysis were included in the multivariate model. A probability value of P < 0.05 was considered statistically significant. SPSS for Windows (ver. 16.0.0; SPSS, Inc., Chicago, IL, USA) was used for the statistical analyses.