Closes #90 #91 Update PPCAT and add urine records

NEWS.md

@@ -1,3 +1,10 @@
+# pharmaversesdtm 0.3.0
+
+## New Features
+
+ - Add URINE records to `pc` and `pp`. (#90)
+ - Update `PPCAT` so that it corresponds to `PCTEST`. (#91)
+
 # pharmaversesdtm 0.2.0
 
 ## New Features

data-raw/pc.R

@@ -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
@@ -83,10 +86,28 @@ 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 ----
@@ -105,13 +126,6 @@ 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,
@@ -120,6 +134,11 @@ 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",
@@ -157,8 +176,19 @@ 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()
 

data-raw/pp.R

@@ -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
@@ -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$PPTESTCD <- "AUCLST"
@@ -140,15 +162,25 @@ pp_tmax$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$PPCAT <- "COMPARTMENTAL"
 PP$PPSTRESC <- PP$PPORRES
 PP$PPSTRESN <- PP$PPORRES
 PP$PPSTRESU <- PP$PPORRESU
-PP$PPSPEC <- "PLASMA"
 PP$DOMAIN <- "PP"
 
 ## Sort ----