Skip to content

Commit

Permalink
Add urine records #91
Browse files Browse the repository at this point in the history
  • Loading branch information
Jeffrey Dickinson committed Mar 28, 2024
1 parent 89cae1c commit b9063f3
Show file tree
Hide file tree
Showing 4 changed files with 87 additions and 31 deletions.
48 changes: 36 additions & 12 deletions data-raw/pc.R
View file
Original file line number Diff line number Diff line change
Expand Up @@ -64,6 +64,9 @@ for (t0 in t) {
(dmex1$k0 * (1 - exp(-dmex1$K * t0))) / (dmex1$V * dmex1$K), 0
)
}
# Urine estimate
dmex1$Urine <- -dmex1$K * t0 + dmex1$V / 2.5

if (t0 == -0.5) { # If first timepoint

PC <- dmex1
Expand All @@ -83,10 +86,22 @@ PC$PCSPEC <- "PLASMA"
PC$PCLLOQ <- 0.01
PC$PCTPTNUM <- PC$t

## PCSEQ; ----
PC <- PC %>%
group_by(STUDYID, USUBJID) %>%
dplyr::mutate(PCSEQ = row_number())
## PCTPT ----
PC$PCTPT <- ifelse(PC$PCTPTNUM == -0.5, "Pre-dose",
ifelse(PC$PCTPTNUM == 0.08, "5 Min Post-dose",
ifelse(PC$PCTPTNUM == 0.5, "30 Min Post-dose", paste0(PC$PCTPTNUM, "h Post-dose"))))


PC_Urine <- PC %>%
filter(PCTPTNUM %in% c(6.00, 12.00, 24.00, 48.00)) %>%
mutate(PCSPEC = "URINE",
PCTPT = case_when(PCTPT == "6h Post-dose" ~ "0-6h Post-dose",
PCTPT == "12h Post-dose" ~ "6-12h Post-dose",
PCTPT == "24h Post-dose" ~ "12-24h Post-dose",
PCTPT == "48h Post-dose" ~ "24-48h Post-dose"),
Conc = Urine)

PC <- bind_rows(PC, PC_Urine)

## Concentration-related code ----
### Remove neg values due to pre-dose negative time ----
Expand All @@ -105,13 +120,6 @@ PC$Conc <- ifelse(PC$Conc < 0.01, "<BLQ", PC$Conc)
PC$PCORRES <- PC$Conc
PC$PCSTRESC <- PC$Conc

## PCTPT ----
PC$PCTPT <- ifelse(PC$PCTPTNUM == -0.5, "Pre-dose",
ifelse(PC$PCTPTNUM == 0.08, "5 Min Post-dose",
ifelse(PC$PCTPTNUM == 0.5, "30 Min Post-dose", paste0(PC$PCTPTNUM, "h Post-dose"))
)
)

## PCDTC ----
PC$PCDTC <- format(as.Date(PC$EXSTDTC) + minutes(round(PC$t * 60)), "%Y-%m-%dT%H:%M:%S")
PC$PCDY <- ifelse(PC$t == -0.5, -1,
Expand All @@ -120,6 +128,11 @@ PC$PCDY <- ifelse(PC$t == -0.5, -1,
)
)

## PCSEQ; ----
PC <- PC %>%
group_by(STUDYID, USUBJID) %>%
dplyr::mutate(PCSEQ = row_number())

## Select vars of interest ----
PC <- subset(PC, select = c(
"STUDYID", "DOMAIN", "USUBJID", "PCSEQ", "PCTESTCD", "PCTEST",
Expand Down Expand Up @@ -157,8 +170,19 @@ pc <- pc %>%
PCTPTNUM = "Planned Time Point Number"
)

## Subset for plots
pc_plasma <- pc %>%
filter(PCSPEC == "PLASMA")

pc_urine <- pc %>%
filter(PCSPEC == "URINE")

## Test to look the overall figure ----
plot <- ggplot(pc, aes(x = PCTPTNUM, y = PCSTRESN, group = USUBJID)) +
plot <- ggplot(pc_plasma, aes(x = PCTPTNUM, y = PCSTRESN, group = USUBJID)) +
geom_line() +
geom_point()

plot2 <- ggplot(pc_urine, aes(x = PCTPTNUM, y = PCSTRESN, group = USUBJID)) +
geom_line() +
geom_point()

Expand Down
70 changes: 51 additions & 19 deletions data-raw/pp.R
View file
Original file line number Diff line number Diff line change
Expand Up @@ -20,21 +20,30 @@ blq_usubjid <- pc %>%
## Remove from PC, subjects with all blq (placebos) ----
remove_usubjid <- blq_usubjid %>% filter(PPORRES == 0)

pc1 <- anti_join(pc, remove_usubjid, by = c("STUDYID", "DOMAIN", "USUBJID"))
pc0 <- anti_join(pc, remove_usubjid, by = c("STUDYID", "DOMAIN", "USUBJID")) %>%
rename(PPCAT = PCTEST, PPSPEC = PCSPEC)

pc1 <- pc0 %>%
filter(PPSPEC == "PLASMA")

pc1u <- pc0 %>%
filter(PPSPEC == "URINE")

# PP usually only present values for applicable subjects;
## Calculate Cmax ----
pp_cmax <- pc1 %>%
group_by(STUDYID, DOMAIN, USUBJID) %>%
group_by(STUDYID, DOMAIN, USUBJID, PPCAT, PPSPEC) %>%
summarise(CMAX = max(PCSTRESN, na.rm = TRUE))

## Calculate Tmax ----
pp_tmax <- pc1 %>%
group_by(STUDYID, DOMAIN, USUBJID) %>%
group_by(STUDYID, DOMAIN, USUBJID, PPCAT, PPSPEC) %>%
filter(PCSTRESN == max(PCSTRESN, na.rm = TRUE)) %>%
arrange(STUDYID, DOMAIN, USUBJID, PCTPTNUM)
arrange(STUDYID, DOMAIN, USUBJID, PCTPTNUM) %>%
glimpse()

pp_tmax$TMAX <- pp_tmax$PCTPTNUM
pp_tmax <- subset(pp_tmax, select = c("STUDYID", "DOMAIN", "USUBJID", "TMAX"))
pp_tmax <- subset(pp_tmax, select = c("STUDYID", "DOMAIN", "USUBJID", "PPCAT", "PPSPEC", "TMAX"))

## AUC 0_tlast ----
pc2 <- pc1
Expand All @@ -52,51 +61,64 @@ for (idx in 1:nrows) {
}

pp_AUC <- pc2 %>%
group_by(STUDYID, DOMAIN, USUBJID) %>%
group_by(STUDYID, DOMAIN, USUBJID, PPCAT, PPSPEC) %>%
summarise(AUC = max(AUC, na.rm = TRUE))

pc2 <- subset(pc2, select = -AUC)

# Elimination rate
pc3 <- merge(pc2, pp_tmax, by = c("STUDYID", "DOMAIN", "USUBJID"))
pc3 <- merge(pc2, pp_tmax, by = c("STUDYID", "DOMAIN", "USUBJID", "PPCAT", "PPSPEC"))
pc3 <- pc3 %>% filter(PCTPTNUM >= TMAX)

pp_npts <- pc3 %>%
group_by(STUDYID, DOMAIN, USUBJID) %>%
group_by(STUDYID, DOMAIN, USUBJID, PPCAT, PPSPEC) %>%
summarise(npts = n())

## Break up pc3 by usubjid, then fit the specified model to each piece ----
# return a list
models <- plyr::dlply(pc3, c("STUDYID", "DOMAIN", "USUBJID"), function(df) {
models <- plyr::dlply(pc3, c("STUDYID", "DOMAIN", "USUBJID", "PPCAT", "PPSPEC"), function(df) {
lm(-log(PCSTRESN, base = exp(1)) ~ PCTPTNUM, data = df)
})
# summarym2=lapply(models,summary)
# lapply(summarym2,"[[","adj.r.squared") #This provides the Rs
# Apply coef to each model and return a data frame
pp_Ke <- plyr::ldply(models, coef)
pp_Ke$Ke <- pp_Ke$PCTPTNUM
pp_Ke <- subset(pp_Ke, select = c("STUDYID", "DOMAIN", "USUBJID", "Ke"))
pp_Ke <- subset(pp_Ke, select = c("STUDYID", "DOMAIN", "USUBJID", "PPCAT", "PPSPEC", "Ke"))


## Halflife ----
pp_lambda <- pp_Ke
pp_lambda$lambda <- 0.693 / pp_lambda$Ke
pp_lambda <- subset(pp_lambda, select = c("STUDYID", "DOMAIN", "USUBJID", "lambda"))
pp_lambda <- subset(pp_lambda, select = c("STUDYID", "DOMAIN", "USUBJID", "PPCAT", "PPSPEC", "lambda"))

## AUC_0_inf ----
pc4 <- pc3 %>%
group_by(STUDYID, DOMAIN, USUBJID) %>%
group_by(STUDYID, DOMAIN, USUBJID, PPCAT, PPSPEC) %>%
mutate(min = min(PCSTRESN, na.rm = TRUE))

pp_Clast <- pc4
pp_Clast$Clast <- pp_Clast$min
pp_Clast <- subset(pp_Clast, select = c("STUDYID", "DOMAIN", "USUBJID", "Clast"))
pp_Clast <- subset(pp_Clast, select = c("STUDYID", "DOMAIN", "USUBJID", "PPCAT", "PPSPEC", "Clast"))

pc5 <- merge(pc4, pp_Ke, by = c("STUDYID", "DOMAIN", "USUBJID"))
pp_AUC_inf <- merge(pc5, pp_AUC, by = c("STUDYID", "DOMAIN", "USUBJID"))
pc5 <- merge(pc4, pp_Ke, by = c("STUDYID", "DOMAIN", "USUBJID", "PPCAT", "PPSPEC"))
pp_AUC_inf <- merge(pc5, pp_AUC, by = c("STUDYID", "DOMAIN", "USUBJID", "PPCAT", "PPSPEC"))

pp_AUC_inf$AUC_inf <- pp_AUC_inf$AUC + (pp_AUC_inf$min) / pp_AUC_inf$Ke
pp_AUC_inf <- subset(pp_AUC_inf, select = c("STUDYID", "DOMAIN", "USUBJID", "AUC_inf"))
pp_AUC_inf <- subset(pp_AUC_inf, select = c("STUDYID", "DOMAIN", "USUBJID", "PPCAT", "PPSPEC", "AUC_inf"))

# Urine parameters
# Ae
pp_Ae <- pc1u %>%
group_by(STUDYID, DOMAIN, USUBJID, PPCAT, PPSPEC) %>%
summarise(pp_Ae = sum(PCSTRESN, na.rm = TRUE)) %>%
glimpse()

# CLR
pp_AUC_sub <- subset(pp_AUC, select = c("STUDYID", "DOMAIN", "USUBJID", "PPCAT", "AUC"))
pp_CLR <- merge(pp_Ae, pp_AUC_sub, by = c("STUDYID", "DOMAIN", "USUBJID", "PPCAT")) %>%
mutate(pp_CLR = pp_Ae / AUC * 1000 / 60 ) %>%
glimpse()

## Add all require variables -----
pp_AUC$PPTESTCD <- "AUCLST"
Expand Down Expand Up @@ -140,15 +162,25 @@ pp_tmax$PPORRESU <- "h"
pp_tmax$PPORRES <- pp_tmax$TMAX
# R2ADJ C85553 R Squared Adjusted

# Urine parameters
pp_Ae$PPTESTCD <- "RCAMINT"
pp_Ae$PPTEST <- "Ae"
pp_Ae$PPORRESU <- "mg"
pp_Ae$PPORRES <- pp_Ae$pp_Ae

pp_CLR$PPTESTCD <- "RENALCL"
pp_CLR$PPTEST <- "CLR"
pp_CLR$PPORRESU <- "mL/min"
pp_CLR$PPORRES <- pp_CLR$pp_CLR


## Join all data ----
PP <- bind_rows(pp_tmax, pp_npts, pp_lambda, pp_Ke, pp_cmax, pp_Clast, pp_AUC, pp_AUC_inf)
PP <- bind_rows(pp_tmax, pp_npts, pp_lambda, pp_Ke, pp_cmax, pp_Clast, pp_AUC, pp_AUC_inf, pp_Ae, pp_CLR)

# Constant variables
PP$PPCAT <- unique(pc$PCTEST)
PP$PPSTRESC <- PP$PPORRES
PP$PPSTRESN <- PP$PPORRES
PP$PPSTRESU <- PP$PPORRESU
PP$PPSPEC <- "PLASMA"
PP$DOMAIN <- "PP"

## Sort ----
Expand Down
Binary file modified data/pc.rda
View file
Binary file not shown.
Binary file modified data/pp.rda
View file
Binary file not shown.

0 comments on commit b9063f3

Please sign in to comment.