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.