Data management and analysis
infection, wart, multiple infections
WART, INFECTIONS, WARTS, INFECTION, REGRESSION
Data management and analysis were conducted using STATA version 17 (Corporation, College Station, Texas, USA). In order to summarize data regarding patient- and wart-specific characteristics descriptive statistics such as numbers and percentages with 95% CIs were applied for categorical variables, and arithmetic mean, standard deviation, and median were used for numerical variables. The presence of any detectable viral load of a specific HPV type was considered diagnostic for infection with that specific type.Kruskal–Wallis test was used to compare patients’ age, number of warts, number of multiple infections and estimated viral loads between different HPV types. Student T-test was used to assesses the number of warts between the two genders and differences in viral load between single and multiple infections. Results were considered statistically significant at Potential predictors of wart persistence were assessed via a multiple logistic regression model, in which wart persistence was defined as index wart duration of more than 6 months combined with resistance to previous treatment. Several variables were created for analysis. Age was dichotomized into two categories: ≥ 13 years and < 12 years, as patients younger than 12 years are known to have higher rates of wart clearanceAs no other predictors were identified, a multivariable logistic regression analysis was performed to simultaneously control for potential confounders. For variables that were not dichotomized, interaction terms were fitted, and linear assumptions were tested. Both the Akaike information criterion (AIC) and the Bayesian Information Criterion (BIC) were used as two different measures of model fit, along with the Variance Inflation Factor (VIF), to detect multicollinearity to ensure that yielded values were within acceptable rangesTo obviate problems related to the stability of parameter estimates that arose for the variable HPV 63 due to the flatness of the likelihood method, the Penalization likelihood method was used instead of the standard Maximum likelihood method