Spss statistics for windows v25

The data were entered and managed in REDCap. They were analysed using Microsoft Excel (Microsoft, Redmond, WA) and IBM SPSS Statistics for Windows v25 (IBM Corp., Armonk, NY). The median and interquartile ranges are reported for continuous variables. Surgical specialties were categorised according to body system and/or clinical unit. The supply of opioids in days was calculated by dividing the number of tablets supplied on discharge by the number of equivalent tablets (based on oMEDD) taken over the 24-hour period prior to discharge. If no opioids were used over the 24-hour period prior to discharge, the quantity was reported as seven days, as this was the maximum quantity that may be supplied by the pharmacy department on discharge.
Comparison by speciality for oMEDD use 24 hours prior to discharge, opioid supply at discharge and proportion of patients having >50% or opioids remaining were conducted using the Mann–Whitney U-test for continuous data and Fisher’s exact test for categorical data. Comparison between minor and major surgery and proportion of patients having >50% of opioids remaining was also conducted using Fisher’s exact test. To assess non-response bias, demographic data of responders compared to non-responders were analysed using Fisher’s exact test and Mann–Whitney U-test.

Analysis of variance (ANOVA) for alpha lattice design of all studied traits in each location was performed in GenStat 18th edition.¹ We used Tukey’s honestly significant difference (HSD) test for environment–environment comparisons. Pearson’s correlation coefficient between traits in each environment was calculated in IBM SPSS Statistics for Windows v. 25 (IBM Corp., Armonk, NY, United States). Broad-sense heritability was estimated in Plant Breeding Tools v. 1.4.²To identify heat stress-tolerant genotypes, heat tolerance efficiency (HTE) was calculated as 100*(Y_si/Y_pi), where Y_si is GY under stress or in a hot environment and Y_pi is GY under an optimum or cold environment (Elbashir et al., 2017 (link)). In the first HTE (HTE1), we used the GY values from DON as the cold environment and MED/SD1 as the hot environment. In the second HTE (HTE2), we considered GY values from MED/SD1 as the cold environment and MED/SD2 as the hot environment.

We used IBM SPSS statistics for Windows v. 25 (SPSS Inc.). We presented categorical data as numbers and percentages (%), and continuous data as either mean and SD for normally distributed data and median and interquartile range for non-normally distributed data. We used the one-way ANOVA, Kruskal–Wallis H test, and χ² test as appropriate. Patient, initial cholecystectomy, BDI, operative, and postoperative data were factors analyzed to determine the risk factors for successful reconstruction. We analyzed the significant variables in the univariate analysis by a multivariate logistic regression model to detect the independent risk factors for successful reconstruction reporting as odds ratios (OR) with their 95% CI. The Kaplan–Meier was used for survival analysis to assess the time to re-intervention-free survivals, A P value less than or equal to 0.05 was considered statistically significant.

A statistician carried out statistical analysis with IBM SPSS Statistics for Windows, v.25 (Armonk, NY, IBM Corp.). Normal distribution of data was analysed by the Kolmogorov–Smirnov test. Binary logistic regression was executed to evaluate the factors that can be predictive for CHDs and NCHDs. Bayes' theorem was performed to reveal the probability that a newborn with a positive screening test was affected by CHDs and/or NCHDs. The likelihood ratio, sensibility, specificity, and positive predictive value and negative predictive values were established if the screening test had good screening tools. Differences with p < 0.05 were considered statistically significant.