1、整合數(shù)據(jù)（最初的血藥濃度檢測(cè)結(jié)果數(shù)據(jù)與winnolin計(jì)算的參數(shù)結(jié)果數(shù)據(jù)）
2、計(jì)算方差
3、計(jì)算GMR、CI
install.packages("tidyverse")
install.packages("readxl")
install.packages("PowerTOST")
library(tidyverse)
library(readxl)
library(PowerTOST)
lx_cq <- as_tibble(read_xlsx("1.初始數(shù)據(jù).xlsx"))? #如果環(huán)境變量與數(shù)據(jù)集來(lái)源一致，可以不加路徑
lx_ba <- as_tibble(read_xls("2.PK參數(shù)結(jié)果.xls"))
lx_ba$Subject <- as.factor(lx_ba$Subject)
lx_ba$Treatment <- as.factor(lx_ba$Treatment)
lx_ba$AUClast <- as.double(lx_ba$AUClast) #轉(zhuǎn)化為因子，或是分類變量
lx_ba$AUClast
new_ba<-lx_ba %>%
? filter(!is.na(AUClast)) %>%
? transmute(Subject,Treatment,AUC=log(AUClast))，提取參數(shù)數(shù)據(jù)中的結(jié)果
new_ba
new_cq <- lx_cq %>%
? filter(Nominal_Time==0) %>%
? select(Subject,Sequence,Treatment,Period)
fact_c<-c("Subject","Sequence","Treatment","Period")
new_cq[fact_c] <- lapply(new_cq[fact_c],as.factor)#處理檢測(cè)數(shù)據(jù)集
new_cq
fina_join <- left_join(new_ba,new_cq,by=c("Subject","Treatment")) #整合兩個(gè)處理好的數(shù)據(jù)集
fina_join
mod <- lm(AUC~Subject+Treatment+Period,fina_join) ? #線性處理，得到方差
mse <- anova(mod)[[3]][4] ? 選擇方差位置，提取方差
mse
cv <- mse2CV(mse) ? CV計(jì)算函數(shù)
Mean <- fina_join %>%
? group_by(Treatment) %>%
? summarise(mean=mean(AUC)) ?? #按照參比制劑與受試制劑分類計(jì)算AUC的平均值
GMR <- exp(Mean[[2]][2]-Mean[[2]][1]) ?? #計(jì)算GMR
GMR?
CI.BE(pe=GMR,CV=cv,n=26) ? #計(jì)算置信區(qū)間的函數(shù)
