Construction and presentation of the predictive model
VTDR, T2DM
REGRESSION, EVENT
Logistic regression analysis is a statistical technique used to analyze the relationships between multiple variables, where the outcome variable is a categorical variable, including binary, unordered categorical, and ordered categorical variables. The number and types of independent variables are not limited. In this study, the well-organized data was randomly divided into training and testing sets in a 7:3 ratio. Single-factor analysis was conducted on all variables in the training set, and variables with a p-value< 0.05 were included in the multivariable logistic regression to identify independent influencing factors. The multivariable logistic regression is based on the logistic regression model, which establishes a linear equation to describe the relationship between independent variables and log odds. The log odds are then converted to probabilities for predicting and interpreting the outcome categories. The logistic regression model can be represented by the following equation:logit(p) represents the log odds, p is the probability of the event occurring, X1, X2,…, Xn are the independent variables, and β0, β1, β2,…, βn are the model parameters (coefficients). By estimating the parameter values, the best-fitting model can be obtained to predict the probability of the outcome variable.We establish a predictive model to identify independent influencing factors of VTDR in patients with T2DM and present it in the form of a Nomogram.