RCT characteristics

The database includes a total of 365 RCTs from 38 different countries. These RCTs were published in 130 different journals in 8 languages between 1986 and 2017.


Trial characteristics Results
Design -
Pilot trial 48 of 365 (13.2%)
Medication intervention 214 of 365 (58.6%)
Blinding reported 189 of 365 (51.8%)
High risk of bias 164 of 365 (44.9%)
Centres -
Multicentred 71 of 365 (19.5%)
Number of centres in multicentred 6 (2, 15) min=2 max=104 n=71
Countries -
Multinational 28 of 365 (7.7%)
International collaboration 65 of 365 (17.8%)
Funding -
Any funding reported 178 of 365 (48.8%)
Any commercial funding reported 46 of 365 (12.6%)
Results -
Total children randomized 44834
Children randomized per RCT 51 (33, 101) min=6 max=4947 n=365
Children analyzed 50 (30.5, 97.5) min=6 max=4947 n=350
Stopped early 49 of 182 (26.9%)
Results statistically significant 84 of 226 (37.2%)
Impact -
Journal impact factor 3.8 (2.2, 6.3) min=0.4 max=55.9 n=302
Citations per year 1.7 (0.7, 3.3) min=0 max=37.5 n=289

Children randomized per RCT

require(ggplot2)
x_limits_year <- as.numeric(c(1985, current_year))
x_limits_year[2] <- x_limits_year[2] +1

ggplot(RCT, aes(RCT$Date__Publication__Year_c, RCT$Num__Rand__Total_c)) +
  geom_hline(yintercept = median(RCT$Num__Rand__Total_c), colour = "red") +
  geom_hline(yintercept = quantile(RCT$Num__Rand__Total_c, 0.25), colour = "red", linetype = "dashed") +
  geom_hline(yintercept = quantile(RCT$Num__Rand__Total_c, 0.75), colour = "red", linetype = "dashed") +
  geom_point(shape = 1, position = position_jitter(height = 0),na.rm = TRUE) +
  coord_trans(y = "log") +
  scale_x_continuous(breaks = seq(1985, as.numeric(current_year), 5), limits = x_limits_year) +
  scale_y_continuous(breaks = c(10, 25, 50, 100, 250, 500, 1000, 2500, 5000), limits = c(6, 5000)) +
  labs(x = "Year of publication",
       y = "Children randomized") + 
  theme_bw() +
  theme_plot # my custom theme (defined in functions file)

The solid red line shows the overall median number of children randomized per RCT and the dashed red lines show the 1st and 3rd quartile.


Types of interventions tested

require(ggplot2)
make.treemap(intervention, "Set2")

The area of each block is proportional to the number of RCTs in that category. The number in parentheses is the number of RCTs in that category.


Indication for interventions tested

require(ggplot2)
make.treemap(indication, "Set3")

The area of each block is proportional to the number of RCTs in that category. The number in parentheses is the number of RCTs in that category.


Population studied

require(ggplot2)
make.treemap(population, "Paired")

The area of each block is proportional to the number of RCTs in that category. The number in parentheses is the number of RCTs in that category.


Report generated: 20-Aug-17 at 16:47h
Database version: 2017Q2
Date of last searches: 03-Jul-17