Statistical analysis

The SAS system for Windows (vers. 8.2, SAS Institute, Cary, NC) was used to perform all statistical analyses in this study. Chi-squared tests were carried out to explore differences in sociodemographic characteristics, hypertension, diabetes, hyperlipidemia, prostatitis, gonorrhea or chlamydia infection, testitis or epididymitis, obesity, and alcohol abuse/alcohol dependence syndrome between PC patients and controls. In addition, this study used ICD-9-CM codes to identify those cases with obesity (ICD-9-CM codes 278) and alcohol abuse/alcohol dependence syndrome (ICD-9-CM codes 291.1, 291.2, 291.5, 291.8, 291.9, 303.90–303.93, 305.00–305.03, V113). We then used conditional logistic regression analyses (conditioned on age, monthly income, geographical location, urbanization level of the patient’s residence, and index date) to calculate the odds ratio (OR) and the corresponding 95% confidence interval (CI) for AD between PC patients and controls. Additionally, the medical comorbidities, such as hypertension, diabetes, hyperlipidemia, prostatitis, gonorrhea or chlamydia infection, testitis or epididymitis, obesity, and alcohol abuse/alcohol dependence syndrome, were considered in the adjustment models in this study, because they were all potential confounders that might affect the association between AD and PC [14–19]. We used the conventional p ≤ 0.05 to assess statistical significance.