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.