Excel 2013

Data collected were recorded into a template developed in Microsoft Excel 2013 and later exported to SPSS version 20 (IBM SPSS Statistics 22; Armonk, NY) for statistical analysis. All differences were considered statistically significant at P-values < 0.05. Proportion of Mf positivity was expressed as a percentage of the number examined at different time points of the follow ups. Chi-square test was used to check for significant differences in the positive rates between the screening techniques at different screening time points.
A web-based application described by Lim et al. [49 (link)] and based on Bayesian Latent Class Models (LCM) was used to determine the accuracy (sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV)) of the diagnostic tests using microscopy as an imperfect gold standard with the help of a simplified interface of three-tests in one-population model (Walter and Irwig model) [49 (link)]. In brief, Bayesian LCMs estimate accuracies of diagnostic tests based on the true disease status of each patient. Bayesian LCMs do not assume that any diagnostic test or combination of diagnostic tests is perfect [50 (link), 51 ]. Table S1 shows the data input into the Web-based application template.

Data on ADRs according to years, gender, age group, type of reporters, suspected medicines and category of ADRs were described as frequencies and percentages. The analysis was undertaken using Microsoft Excel 2013 and IBM SPSS Version 19.

Mainly descriptive analyses were performed due to the small sample size, using Excel 2013 and IBM SPSS Statistics 23.The total time spent per week was investigated, as well as the time spent on each step undertaken to manage supply problems and the time spent by employees of the hospital pharmacies. Results were displayed in minutes and relative numbers were presented as percentages. The considered sample size (N) was included in the result.
To check whether the time spent by hospital pharmacists was correlated to the number of beds or the number of employees, the Pearson’s correlation was calculated using IBM SPSS Statistics 23.
From the background information papers, the type and number of supply disruptions and drug shortages were analysed, together with the solutions for drug shortages. Only supply problems for which the question ‘whether or not the supply disruption caused a drug shortage’ was answered, were considered for further analyses. Supply problems were reported according to the WHO ATC-system [28 ].

All statistical calculations were carried out with the help of the statistical package IBM SPSS 23 and an Excel 2013 spreadsheet. Qualitative variables were presented as counts and percentages, while quantitative variables were described with the help of the arithmetic mean and standard deviation. To check whether quantitative variables came from a normally distributed population, the Shapiro–Wilk test was used. The Mann–Whitney U test and Student’s t-test were used to check the significance of differences between two groups, while the Kruskal–Wallis test was applied to check the significance between more than two groups. To predict and check the impact of several factors (independent variables) on dependent variables, multiple linear regression analysis was carried out. To determine the dependence between the strength and direction of variables, correlation analysis was performed by calculating Spearman’s rank correlation coefficients. In all calculations, the level of significance was set at p < 0.05.

All analyses will be performed with the use of Microsoft Excel 2013 and IBM SPSS Statistics v.20 software. Data will be analyzed by descriptive statistics. Demographic and descriptive continuous variables will be expressed as mean (standard deviation, SD) and median values (interquartile range, IQR). Categorical variables will be expressed as percentages. Chi square or Fisher’s exact test will be used for comparison of dichotomous and Mann-Whitney or t-test for continuous variables. Binary logistic regression analysis will be performed in order to identify variables associated with relapses, serious infections, hospitalizations, corticosteroid discontinuation and death.

As our experiment used a randomized block design, for each species, we used species‐specific linear mixed model analysis to examine the effects of N, P, and N × P on the leaf soluble sugar and starch concentrations, and N, P, and N:P ratios for each species in 2012, 2015 and 2017. Given the variation among species, the linear mixed models were used to test the difference on the soluble sugar and starch, N, P concentrations and N:P ratios. In these models, species (S), N addition (N), and P addition (P) were considered as the fixed effects, and block, nested by sampling year, was included in the models as a random factor. Relative effects (RE) were quantified by the ratio of the variable in the experimental group (+N, +P, +NP) to the control group (CK), minus one (In Figure 1). All data analyses were performed in Excel 2013 and IBM SPSS Statistics 19.0. Results are reported as significant when p < .05.