First numbers

Number of RCTs relevant to burden, in total, per region
Number of patients enrolled in RCTs relevant to the burden, in total, per region
Number of RCTs and number of patients per region per disease

1. First, we evaluate if total number of RCTs relevant to the burden is similar across simulations



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data <- read.table("/media/igna/Elements/HotelDieu/Cochrane/MappingRCTs_vs_Burden/database_RCTs_regions_27diseases.txt")
nrow(data)



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data$Sample[data$Sample<10 | data$Sample>200000] <- NA
table(is.na(data$Sample))
sum(data$Sample,na.rm = TRUE)









    





 FALSE   TRUE 
107558   9622 






    




43952638



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library(data.table)



In [38]:

    
options(repr.plot.width=9, repr.plot.height=9)
par(mfrow=c(5,5),mar=c(1,1,3,1))
DF <- fread(paste(c("/media/igna/Elements/HotelDieu/Cochrane/MappingRCTs_vs_Burden/Replicates/Metrics_over_repl/Metrics_over_replicates_",
                    as.character(d),".txt"),collapse=""))
hist(DF$RCTs[DF$Reg=="All"],xlim=c(70000,90000),xlab=NULL,main="all")
for(i in 1:length(sms)){ 
    DF <- fread(paste(c("/media/igna/Elements/HotelDieu/Cochrane/MappingRCTs_vs_Burden/Replicates/Metrics_over_repl/",
                        sms[i]),collapse=""))
    hist(DF$RCTs[DF$Reg=="All" & DF$Dis=="all"],
         xlim=c(70000,90000),xlab=NULL,
         main=substr(sms[i],25,nchar(sms[i])-4))               
                        }



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Mgbd <- read.table("../Data/27_gbd_groups.txt")



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Mgbd$x[12]









    




Neoplasms



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PERF <- read.csv('../Tables/Performances_per_27disease_data.csv')



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PERF[12,]









    





X dis GBD TP_Dis FP_Dis TN_Dis FN_Dis TP_Oth FP_Oth TN_Oth FN_Oth

	12 12       12       Neoplasms 933      42       1763     25       1213     214      1198     138



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dprp <- DF[DF$Dis=="dis",c("RCTs","Patients")]/DF[DF$Dis=="all",c("RCTs","Patients")]
dprp$Reg <- DF$Region[DF$Dis=="dis"]
tapply(dprp$RCTs,dprp$Reg,function(x){100*quantile(x,probs=c(0.025,0.5,0.975))})
tapply(dprp$Patients,dprp$Reg,function(x){100*quantile(x,probs=c(0.025,0.5,0.975))})

DF[,quantile(.SD,probs=c(0.025,0.5,0.975)),by=.(Dis,Region),.SDcols=c("RCTs","Patients")]


tapply(DF$RCTs[DF$Dis=="dis"],DF$Reg[DF$Dis=="dis"],function(x){quantile(x,probs=c(0.025,0.5,0.975))})



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regs <- strsplit(as.character(data$Regions),"&")
DRY <- do.call('cbind',tapply(regs,data$year,function(x){table(unlist(x))}))

DRY <- DRY[order(apply(DRY,1,sum)),]

barplot(DRY[rownames(DRY)!="High-income",])