Stata software ver 16

Patients diagnosed with cervical cancer were categorized by age (<50 years and ≥50 years), period of diagnosis (1999–2008 and 2009–2018), histological type (SCCA, adenosquamous carcinoma, and adenocarcinoma), and Surveillance, Epidemiology, and End Results (SEER) summary staging (localized, regional, and distant) [13 ]. Data analyses according to SEER summary staging were performed on the women diagnosed from 2006, since the stage variable is available from 2006 onward in the KCCR (Table S1). ASRs per 100,000 women for cervical cancer were calculated using Segi’s standard population [14 ]. The trends in ASRs were estimated as an annual percent changes (APCs) using the following formula: [exp(β)−1]×100, where β was the slope calculated from a linear regression for the natural log transformed ASRs [15 ]. Overall survival (OS) was estimated using the Kaplan-Meier method [16 ] and compared using the log-rank test. The p-value<0.05 was considered statistically significant. Statistical analyses were conducted using SAS software ver. 9.4 (SAS Institute Inc., Cary, NC, USA) and STATA software ver. 16 (StataCorp LLC, College Station, TX, USA). The study protocol was reviewed and approved by the institutional review board of the National Cancer Center (NCC2021–0344), and the requirement for informed consent was waived because it was a secondary analysis of de-identified data.

Patients were grouped by age: (1) children ≤ 14 years, (2) adolescents and young adults (AYA) 15–39 years, (3) adults 40–64 years, and (4) elderly ≥ 65 years. Age-specific incidence rates and age-standardized incidence rates (ASR) using Segi’s world standard population were calculated and expressed per million [13 ]. Incidence rates and clinical characteristics were compared between different age groups based on sex, period of diagnosis (1999–2003, 2004–2008, 2009–2013, 2014–2017), site of origin (bone or soft tissue), tumor location, the Surveillance, Epidemiology, and End Results (SEER) summary stage [14 ] (since 2006), and the first course of treatment within 4 months after diagnosis.
Overall survival rates were calculated using the Kaplan-Meier method [15 ]. Survival differences among patients grouped according to clinical variables were compared using the log-rank test. The Cox proportional hazards model [16 ] was applied to estimate the effect of covariates on hazard rates. All statistical tests were two-tailed, and results were considered statistically significant at p-values < 0.05. Statistical analyses were conducted using SAS software ver. 9.4 (SAS Institute Inc., Cary, NC) and STATA software ver. 16 (StataCorp LLC, College Station, TX).

In the database, the variables collected were the Petri dish ID, CFUs/mL, microorganism species and inoculum concentrations. Data analysis and statistical computations were performed using Microsoft Excel software (ver. 16) for preliminary statistical evaluations of empirical data and Stata software Ver 16 for the statistical analysis. The results of each experiment in triplicate were expressed as mean CFU/mL for each test for the experiments involving bacteria. The mean logarithmic reduction and its 95% confidence interval were calculated from the replicates data of the microorganisms and compared with positive controls.