Statistical package for the social sciences spss software version 23

Statistical methods have been used to confirm the correlation between biochemical and PK parameter values. Pearson correlation is a statistical technique that describes the degree of linear association between two continuous quantitative variables normally distributed. Values between −1 and +1 indicate the strength (interpreting the coefficient value) and the direction (taking the sign of the coefficient) of linear association. The ‘+’ symbol indicates a direct proportionality relationship between the correlated values and the ‘–’ symbol, an inverse relationship. In addition, statistical analysis was performed on the PK parameters calculated by HPLC-UV and UPLC-ESI-MS/MS methods. All PK parameters determined by each quantification method were analyzed for statistical significance by Student's t-test with P < 0.05, indicating a significant difference. The Statistical Package for the Social Sciences (SPSS) software version 23 (IBM, NY, USA) was used for the statistical analysis.

Trials were categorized as either discontinued or completed. Continuous variables were reported as median (interquartile range [IQR]), while categorical variables were expressed as No. (%). All trial characteristics were calculated as a percentage of total trials. Time taken to publish studies was calculated from the trial completion date as reported by ClinicalTrials.gov to the date of first online or in print publication (whichever occurred first); χ² tests were used to evaluate associations between trial characteristics and trial completion and publication status. Multivariable logistic regression models assessing factors including funding source, interventions, trial phase, and masking were used to determine their impact on trial completion and publication, with completion or publication status as the dependent variable and the control variables modelled as a set of dummy variables. Because trial publications can be late, a sensitivity analysis excluding trials completed after 2015 was also conducted to observe any meaningful change in trial statistics. All statistical analyses were performed using Statistical Package for the Social Sciences (SPSS) software version 23 (International Business Machines Corporation, New York, USA). All comparisons were two‐tailed, and a P‐value <0.05 was considered statistically significant.

Data were analyzed using IBM Statistical Package for the Social Sciences (SPSS) software, version 23. Descriptive data were expressed as means, standard deviations and frequencies. Associations were determined using the χ² test, and correlations were determined using Pearson’s correlation. The mean numbers of medicines per prescription were compared using the one-way analysis of variance (ANOVA) test, followed by Tukey’s Honest Significant Difference post hoc test. For all analyses, significance was determined at P < 0.05.