Stata statistical software version 14

Participation in this study was voluntary, and each participant provided written informed consent prior to her enrollment. All clinical procedures were performed in conformity with internationally accepted regulations in regard to the use of human subjects in research. Ethical approval (UNILU/CEM/075/2015) was granted by the ethics committee of the School of Public Health, University of Lubumbashi, DRC. Survey data were collected and then transcribed in a prepared Excel sheet. For continuous variables such as lung function parameters, group comparison was carried out using Student’s t-test, whereas Fisher’s exact test was performed for categorical or dichotomized variables. To determine the relationship between the characteristics of the participants and the reported respiratory manifestations, a bivariate analysis was performed with the use of a logistic regression test. Additionally, factors that showed associations in the bivariate analysis were entered in a multivariate logistic regression model. A p-value (double-sided) less than 0.05 was considered significant. Stata statistical software version 14 (Stata Corporation, College Station, TX, USA) was used for the analyses.

Frequency distribution of qualitative variables in both the wild type and the mutagenized populations were computed using STATA statistical software version 14 (StataCorp, 2015 ). Distributions of quantitative traits in the populations were determined using R software version 4.0.1. Excel was used to compute estimates of percentage germination, germination speed and number of seeds per plant. Student T-test or Z-test was used to determine significant difference between the wild type and mutants with performances above the control (wild type) range.

Data analysis consisted of three stages. Firstly, the baseline characteristics between the pre- and the post-CDPR periods were analysed as frequencies and percentages for categorical variables. Secondly, the outcome variables were calculated monthly between September 2016 and August 2018. Finally, ITSA was used to analyse the effects of implementing the CDPR. The Newey–West method was used to handle heteroscedasticity and autocorrelation. The Actest was used to test autocorrelation and determine the number of time-series lags to ensure that the model conforms to the correct autocorrelation structure [36 ]. The STATA statistical software version 14 was used for this analysis.

Descriptive analyses (mean, standard deviation, and median) were performed to evaluate the demographic and clinical information from the submission forms. Categorical analyses were performed using a Pearson’s chi-square test to determine the association between observations (age, breed, sex, clinical signs) and each infection group. Each infectious disease group was compared to the other infectious groups. To avoid interpretation bias when multiple pathogens were involved, only horses with a single pathogen were evaluated in each specific group. Horses with co-infections were grouped in a multiple pathogen group. Categorical analyses were also performed between viral (EIV, EHV-1, EHV-4, ERVs) and bacterial infections (S. equi). All statistical analyses were performed using commercial software (Stata Statistical Software, Version 14, College Station, TX, USA) and statistical significance was set at p < 0.05.

In all calculations, melanoma included both MIS and invasive melanoma. Differences in diagnostic frequencies between groups were calculated and compared using the χ² test. The NNE was calculated by dividing the total number of excised lesions by the number of melanomas. Accuracy assessment included positive predictive values (PPVs), benign to malignant and benign melanocytic to melanoma ratios, and correlations with physicians’ RCM experience (malignant lesions/lesions excised). Tests of proportions and equality of proportions for large-sample statistics were used to compare differences in NNE, based on hypothesized population value for no difference in proportions.
Analyses were performed according to the intention-to-treat principle. Subgroup analyses were summarized using descriptive statistics. Continuous variables were expressed as mean (SD) and compared using unpaired t test. Categorical variables were expressed as frequencies and compared using Pearson χ² test. Pearson correlation coefficient was used to assess associations between physicians’ years of RCM experience prior to study initiation and correct identification of malignant lesions at baseline with adjunctive RCM use. A P < .05 was considered statistically significant, and r > 80% was considered a very high correlation. Statistical analysis was performed using Stata statistical software, version 14 (StataCorp).