## 2 Materials and Methods
### Statistical Analysis  
For the demographic data (Table 1), the chi-square test (with continuity correction) was used for categorical variables, and one-way analysis of variance was used for continuous variables. AGE, WSTC, PA_WALK, SMOKE_DRT, HTDZ, HTN, and ETC had significant differences in diagnosis code groups. BMI, PA_MD, PA_VD, STK, DM, and DLD had no significant differences in diagnosis code groups. Cox proportional hazards (PHs) regression for survival data [15] was used to create a risk calculator to measure the probability of 5-year survival without PCa. This study used \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Diagnosis Codes** & **C60** & **C61** & **C62** & **C63** & **None** & \(p\)**-Test * \\ \hline N (Total = 347,575) & 5 & 1928 & 18 & 14 & 345,610 & \\ \hline SICK\_CODE & 5 & 1928 & 18 & 14 & 345,610 & \\ (\%) & (0.001) & (0.555) & (0.005) & (0.004) & (99.435) & \\ \hline AGE & 58.60 & 59.46 & 53.56 & 57.07 & 52.76 & \\ (mean (SD)) & (11.55) & (7.78) & (7.66) & (6.57) & (6.40) & \textless{}0.001 *** \\ \hline BMI & 25.94 & 23.86 & 23.71 & 24.15 & 23.95 & \\ (mean (SD)) & (1.91) & (2.96) & (3.50) & (3.07) & (2.92) & 0.393 \\ \hline WSTC & 87.20 & 84.11 & 81.94 & 82.50 & 83.56 & \\ (mean (SD)) & (5.93) & (7.67) & (8.54) & (6.81) & (7.51) & 0.013 * \\ \hline PA\_MD & 0.20 & 1.40 & 0.61 & 0.79 & 1.40 & \\ (mean (SD)) & (0.45) & (1.84) & (1.14) & (1.05) & (1.72) & 0.093 \\ \hline PA\_VD & 0.00 & 1.27 & 0.61 & 0.71 & 1.30 & \\ (mean (SD)) & (0.00) & (1.72) & (0.92) & (0.91) & (1.63) & 0.074 \\ \hline PA\_WALK & 4.20 & 2.71 & 1.61 & 1.71 & 2.60 & \\ (mean (SD)) & (3.03) & (2.43) & (2.12) & (1.82) & (2.30) & 0.017 * \\ \hline SMOKE\_DRT & 30.40 & 30.26 & 24.72 & 21.50 & 25.36 & \\ (mean (SD)) & (11.76) & (11.95) & (12.63) & (13.39) & (9.49) & \textless{}0.001 *** \\ \hline STK = 1 & 0 & 15 & 0 & 0 & 1774 & \\ (\%) & (0.0) & (0.8) & (0.0) & (0.0) & (0.5) & 0.589 \\ \hline HTDZ = 1 & 0 & 58 & 0 & 0 & 5401 & \\ (\%) & (0.0) & (3.0) & (0.0) & (0.0) & (1.6) & \\ \hline HTN = 1 & 3 & 583 & 3 & 4 & 64,065 & \\ (\%) & (60.0) & (30.2) & (16.7) & (28.6) & (18.5) & \textless{}0.001 *** \\ \hline DM = 1 & 0 & 176 & 1 & 2 & 27,714 & \\ (\%) & (0.0) & (9.1) & (5.6) & (14.3) & (8.0) & 0.339 \\ \hline DLD = 1 & 0 & 62 & 0 & 1 & 10,066 & \\ (\%) & (0.0) & (3.2) & (0.0) & (7.1) & (2.9) & 0.699 \\ \hline ETC = 1 & 0 & 143 & 0 & 0 & 11,382 & \\ (\%) & (0.0) & (7.4) & (0.0) & (0.0) & (3.3) & \textless{}0.001 *** \\ \hline \hline \end{tabular}  
* chisq.test for categorical variables (with continuity correction) and onews.test for continuous variables (with equal variance assumption, i.e., regular ANOVA). Two-group ANOVA is the equivalent of a \(t\)-test.
* \(p\) \(<\) 0.05, **
* \(p\) \(<\) 0.001.
* C60: Malignant neoplasm of penis, C61: malignant neoplasm of prostate, C62: malignant neoplasm of testis, C63: malignant neoplasm of the epididymis.  
\end{table} 
Table 1: Dataset of 2013–2020 from the National Health Insurance Sharing Service, stratified by records of SICK_CODE (C60,C61,C62,C63) * between 2015 and 2020.  
the R package survival (version 3.5.5) for the Cox PH analysis. The Cox PH is frequently used in multivariate survival analysis in the medical field. Survival analysis uses a hazard model to predict the probability of an event occurring within a certain period. The common hazard model uses a constant baseline hazard number, which represents the hazard of the model when all other covariates are 0. Cox PH replaces the baseline hazard with the baseline hazard function, making it a robust survival analysis method even without a baseline hazard [16]. The survival time to the diagnosis of PCa was the target outcome of the risk calculator from Cox PH. This model analyzed 1928 C61 individuals diagnosed with SICK_CODE (PCa) and 345,610 individuals diagnosed with non-cancers out of a total of 347,538 registered male individuals. Statistical analysis was conducted in R (version 4.0.4; R Foundation for Statistical Computing, Vienna, Austria).