Calculation of estimated refractive error
Factors correlating the average SE at the end of three years were evaluated by logistic regression. Logistic regression analysis of factors correlating mean SE is given in Table 4 . For this purpose, the onset biometric and demographic (age, gender, weight, and height) data were included in the logistic regression model. The onset data of SE (β = 0.916, p < 0.001), AL (β = -0.451, p < 0.001), ACD (β = 0.430, p = 0.005), and Kmean (β = -0.172, p < 0.001) were found to be significantly associated with the mean SE at the final data. However, demographic onset data were not significantly correlated with the mean SE at the final data (p>0.05). The coefficient of determination (R2 ) of regression was set at 0.761 for these variables. To calculate the estimated SE following three years,equation 1 was established via the logistic model.
\(\text{SE}_{3}=\left[16.46+\ \left(0.916\ x\ \text{SE}\right)+\ \left(0.430\ x\ ACD\right)\right]-\ \left[\left(0.451\ x\ AL\right)+\left(0.172\ x\ K_{\text{mean}}\right)\ \right]\)(1)
\(\text{SE}_{3}\) shows the estimated spherical equivalent following three years; SE shows the onset spherical equivalent; ACD shows the onset anterior chamber depth; AL shows the onset axial length;\(K_{\text{mean}}\) shows the onset arithmetic mean of keratometry.
Lastly, reliability and validity checks of the estimation model were carried out deductively using our onset dataset created by MS Excel.