2.2 Statistical Analysis
Statistical analysis was done using the SciPy stats package and figure generation was done using the MatPlotLib package for python [11, 12]. To determine the rate of reporting of race, the percentage of studies that reported race as a demographic and those that did not report race were calculated. Out of the studies that did report race, the percent of those studies that reported on each racial group was also determined. The same method was also used to determine the rate of reporting of sex.
To determine whether disparities in clinical trials’ participation exist, the mean participation rate of each race and sex across all the studies that reported race or sex was calculated and the 95% confidence interval for the mean of each demographic was determined. Then, to compare participation rates among different races, the percent of white participation from each study was compared with the percent participation of the remaining races (Asian, African American, Hispanic, Other, and Unknown) using a 2-sample t-test and a p-value < 0.01 was considered statistically significant. A one-way ANOVA test was also used to determine whether a statistically significant difference in participation among the Asian, African American, Hispanic, Other, and Unknown groups exists and a p-value < 0.01 was considered statistically significant. To compare participation rates between sex, a 2-sample t-test was used and a p-value < 0.01 was considered statistically significant.
Lastly, to determine how participation rate has changed over time, the participation rates of different races from each individual study was plotted on a scatter plot using the MatPlotLib package [12]. Using those datapoints, the SciPy stats package was used to calculate the Pearson correlation coefficient, the p-value (with p <0.05 considered statistically significant), and to draw a line of best fit [11]. The data points for White participation were plotted with each of the remaining races to serve as a comparison as to how participation rates have changed with time.