# PK Model: Assessing the Impact of Joint Covariates Distributions and BSV on Drug Exposure

Here we illustrate the full distribution of covariates + BSV approach. A two-compartment pharmacokinetic (PK) model defined with ordinary differential equations (ODEs) is used. Covariates Weight, Albumin and Sex had effects on the Clearance (CL) model parameter while Weight and Sex had effects on the Volume of distribution (V) model parameter. For simplicity, there were no included covariates effects on other PK parameters such as peripheral clearance or volume. The approach is general and can be easily extended to any other ODEs model with multiple covariate effects on multiple model parameters.

## Specifying a PK Model using mrgsolve

codepkmodelcov <- '
$PARAM @annotated KA : 0.5 : Absorption rate constant Ka (1/h) CL : 4 : Clearance CL (L/h) V : 10 : Central volume Vc (L) Vp : 50 : Peripheral volume Vp (L) Qp : 10 : Intercompartmental clearance Q (L/h) CLALB : -0.8 : Ablumin on CL (ref. 45 g/L) CLSEX : 0.2 : Sex on CL (ref. Female) CLWT : 1 : Weight on CL (ref. 85 kg) VSEX : 0.07 : Sex on Vc (ref. Female) VWT : 1 : Weight on Vc (ref. 85 kg)$PARAM @annotated // reference values for covariate
WT    : 85    : Weight (kg)
SEX   : 0     : Sex (0=Female, 1=Male)
ALB   : 45    : Albumin (g/L)

$PKMODEL cmt="GUT CENT PER", depot=TRUE, trans=11$MAIN
double CLi = CL *
pow((ALB/45.0), CLALB)*
(SEX == 1.0 ? (1.0+CLSEX) : 1.0)*
pow((WT/85.0), CLWT)*exp(nCL);
double V2i = V *
(SEX == 1.0 ? (1.0+VSEX) : 1.0)*
pow((WT/85.0), VWT)*exp(nVC);

double KAi = KA;
double V3i = Vp *pow((WT/85.0),    1);
double Qi = Qp *pow((WT/85.0), 0.75);

$OMEGA @annotated @block nCL : 0.09 : ETA on CL nVC : 0.01 0.09 : ETA on Vc$TABLE
double CP   = CENT/V2i;

$CAPTURE CP KAi CLi V2i V3i Qi WT SEX ALB ' modcovsim <- mcode("codepkmodelcov", codepkmodelcov) partab <- setDT(modcovsim@annot$data)[block=="PARAM", .(name, descr, unit)]
partab <- merge(partab, melt(setDT(modcovsim@param@data), meas=patterns("*"), var="name"))
knitr::kable(partab)
name descr unit value
ALB Albumin g/L 45.00
CL Clearance CL L/h 4.00
CLALB Ablumin on CL ref. 45 g/L -0.80
CLSEX Sex on CL ref. Female 0.20
CLWT Weight on CL ref. 85 kg 1.00
KA Absorption rate constant Ka 1/h 0.50
Qp Intercompartmental clearance Q L/h 10.00
SEX Sex 0=Female, 1=Male 0.00
V Central volume Vc L 10.00
VSEX Sex on Vc ref. Female 0.07
VWT Weight on Vc ref. 85 kg 1.00
Vp Peripheral volume Vp L 50.00
WT Weight kg 85.00

### Simulate a Reference Subjects with BSV

We simulate the reference subject having the reference covariate values defined in the model which are:
Weight = 85 kg, Sex = Female and Albumin = 45 g/L. We also keep the between subject variability (BSV) to illustrate its effects on the concentration-time profiles on linear and log linear scales.

idata <- data.table(ID=1:nbsvsubjects, WT=85, SEX=0, ALB=45)

ev1 <- ev(time = 0, amt = 100, cmt = 1)
data.dose <- ev(ev1)
data.dose <- setDT(as.data.frame(data.dose))
data.all <- data.table(idata, data.dose)

outputsim <- modcovsim %>%
data_set(data.all) %>%
mrgsim(end = 24, delta = 0.25) %>%
as.data.frame %>%
as.data.table

outputsim$SEX <- factor(outputsim$SEX, labels="Female")

# Only plot a random sample of N=500
set.seed(678549)
plotdata <- outputsim[ID %in% sample(unique(ID), 500)]

# New facet label names for dose variable
albumin.labs <- c("albumin: 45 ng/mL")
names(albumin.labs) <- c("45")
wt.labs <- c("weight: 85 kg")
names(wt.labs) <- c("85")

p1 <- ggplot(plotdata, aes(time, CP, group = ID)) +
geom_line(alpha = 0.2, size = 0.1) +
facet_grid(~ WT + ALB + SEX,
labeller =  labeller(ALB = albumin.labs,
WT = wt.labs)) +
labs(y = "Plasma Concentrations", x = "Time (h)")

p2 <- ggplot(plotdata, aes(time, CP, group = ID)) +
geom_line(alpha = 0.2, size = 0.1) +
facet_grid(~ WT + ALB + SEX,
labeller =  labeller(ALB = albumin.labs,
WT = wt.labs)) +
scale_y_log10() +
labs(y = "Plasma~Concentrations\n(logarithmic scale)", x = "Time (h)")

egg::ggarrange(p1, p2, ncol = 2)

### Compute PK Parameters, Plot and Summarize BSV

In this section we compute the PK parameters of interest, provide a plot of the parameters as well as of the standardized ones. We also summarize and report the BSV as ranges of 50 and 90% of patients for each PK parameter. Later on we might choose to include these ranges in the coveffectsplot or not.

derive.exposure <- function(time, CP) {
n <- length(time)
x <- c(
Cmax = max(CP),
Clast = CP[n],
AUC = sum(diff(time) * (CP[-1] + CP[-n])) / 2
)
data.table(paramname=names(x), paramvalue=x)
}
refbsv <- outputsim[, derive.exposure(time, CP), by=.(ID, WT, SEX, ALB)]

p3 <- ggplot(refbsv, aes(
x      = paramvalue,
y      = paramname,
fill   = factor(..quantile..),
height = ..ndensity..)) +
facet_wrap(~ paramname, scales="free", ncol=1) +
stat_density_ridges(
quantile_lines=TRUE, rel_min_height=0.001, scale=0.9,
quantiles=c(0.05, 0.25, 0.5, 0.75, 0.95)) +
scale_fill_manual(
name   = "Probability",
values = c("white", "#FF000050", "#FF0000A0", "#FF0000A0", "#FF000050", "white"),
labels = c("(0, 0.05]", "(0.05, 0.25]",
"(0.25, 0.5]", "(0.5, 0.75]",
"(0.75, 0.95]", "(0.95, 1]")) +
theme_bw() +
theme(
legend.position = "none",
axis.text.y     = element_blank(),
axis.ticks.y    = element_blank(),
axis.title.y    = element_blank()) +
labs(x="PK Parameters", y="") +
scale_x_log10() +
coord_cartesian(expand=FALSE)

# Obtain the standardized parameter value by dividing by the median.
refbsv[, stdparamvalue := paramvalue/median(paramvalue), by=paramname]

p4 <- ggplot(refbsv, aes(
x      = stdparamvalue,
y      = paramname,
fill   = factor(..quantile..),
height = ..ndensity..)) +
facet_wrap(~ paramname, scales="free", ncol=1) +
stat_density_ridges(
quantile_lines=TRUE, rel_min_height=0.001, scale=0.9,
quantiles=c(0.05, 0.25, 0.5, 0.75, 0.95)) +
scale_fill_manual(
name="Probability",
values=c("white", "#FF000050", "#FF0000A0", "#FF0000A0", "#FF000050", "white"),
labels = c("(0, 0.05]", "(0.05, 0.25]",
"(0.25, 0.5]", "(0.5, 0.75]",
"(0.75, 0.95]", "(0.95, 1]")) +
theme_bw() +
theme(
legend.position = "none",
axis.text.y     = element_blank(),
axis.ticks.y    = element_blank(),
axis.title.y    = element_blank()) +
labs(x="Standardized PK Parameters", y="") +
scale_x_log10() +
coord_cartesian(expand=FALSE, xlim = c(0.3,3))

p3 + p4

Ranges of BSV for each PK Parameter:

bsvranges <- refbsv[,list(
P05 = quantile(stdparamvalue, 0.05),
P25 = quantile(stdparamvalue, 0.25),
P50 = quantile(stdparamvalue, 0.5),
P75 = quantile(stdparamvalue, 0.75),
P95 = quantile(stdparamvalue, 0.95)), by = paramname]
bsvranges
paramname P05 P25 P50 P75 P95
Cmax 0.7997350 0.9179000 1 1.086385 1.203140
Clast 0.4508577 0.7360868 1 1.289688 1.740782
AUC 0.6985306 0.8691598 1 1.141210 1.330691

## Importing Realistic Distributions of Covariates

Here we import from a dataset named covdatasim available in the package. Instead of simulating at specific covariate values we will use the full distribution.

### Visualize the dataset of Covariates:

reference.values <- data.frame(WT = 85, ALB = 45, SEX = 0)
covdatasim$SEX<- ifelse(covdatasim$SEX==0,1,0)
covdatasim$SEX <- as.factor(covdatasim$SEX )
covdatasim$SEX <- factor(covdatasim$SEX,labels = c("Female","Male"))
covdatasimpairs <- covdatasim
covdatasimpairs$Weight <- covdatasimpairs$WT
covdatasimpairs$Sex <- covdatasimpairs$SEX
covdatasimpairs$Albumin <- covdatasimpairs$ALB

ggpairsplot <- GGally::ggpairs(covdatasimpairs,
columns = c("Weight","Sex","Albumin"),mapping = aes(colour=SEX),
diag= list(
continuous = GGally::wrap("densityDiag", alpha = 0.3,colour=NA),
discrete   = GGally::wrap("barDiag",  alpha =0.3, position = "dodge2")
),
lower = list(
continuous = GGally::wrap("points", alpha = 0.2, size = 2),
combo = GGally::wrap("facethist", alpha =
0.2, position = "dodge2")
),
upper = list(
continuous = GGally::wrap("cor", size = 4.75, align_percent = 0.5),
combo = GGally::wrap("box_no_facet", alpha =0.3),
discrete = GGally::wrap("facetbar",  alpha = 0.3, position = "dodge2")
)
)
covdatasim$SEX <- as.numeric(covdatasim$SEX)-1
ggpairsplot + theme_bw(base_size = 12) +
theme(axis.text = element_text(size=9))

### Simulation With Full Distributions of Covariates

As a first step, we simulate without uncertainty and without BSV using zero_re() and provide a plot to visualize the effects.

idata <- data.table::copy(covdatasim)
idata$covname <- NULL ev1 <- ev(time=0, amt=100, cmt=1) data.dose <- as.data.frame(ev1) data.all <- data.table(idata, data.dose) outcovcomb<- modcovsim %>% data_set(data.all) %>% zero_re() %>% mrgsim(end=24, delta=0.25) %>% as.data.frame %>% as.data.table outcovcomb$SEX <- as.factor(outcovcomb$SEX ) outcovcomb$SEX <- factor(outcovcomb$SEX, labels=c("Female", "Male")) stat_sum_df <- function(fun, geom="ribbon", ...) { stat_summary(fun.data = fun, geom = geom, ...) } stat_sum_df_line <- function(fun, geom="line", ...) { stat_summary(fun.data = fun, geom = geom, ...) } fwt <- function(x, xcat, which, what, from, to, ...) { what <- sub("WT", "\nWeight", what) sprintf("%s %s [%s to %s[", which, what, signif_pad(from, 3, FALSE), signif_pad(to, 3, FALSE)) } f <- function(x, xcat, which, what, from, to, ...) { what <- sub("ALB", "\nAlbumin", what) sprintf("%s %s [%s to %s[", which, what, signif_pad(from, 3, FALSE), signif_pad(to, 3, FALSE)) } plotlines<- ggplot(outcovcomb, aes(time,CP,col=SEX ) )+ geom_line(aes(group=ID),alpha=0.1,size=0.1)+ facet_grid(table1::eqcut(ALB,2,f) ~ table1::eqcut(WT,2,fwt),labeller = label_value)+ labs(colour="Sex",caption ="Simulation without Uncertainty\nFull Covariate Distribution\nwithout BSV/Uncertainty", x = "Time (h)", y="Plasma Concentrations")+ coord_cartesian(ylim=c(0,3.5)) plotranges<- ggplot(outcovcomb, aes(time,CP,col=SEX,fill=SEX ) )+ stat_sum_df(fun="median_hilow",alpha=0.2, mapping = aes(group=interaction(table1::eqcut(WT,2,fwt), SEX, table1::eqcut(ALB,2,f)) ), colour = "transparent")+ stat_sum_df_line(fun="median_hilow",size =2, mapping = aes(linetype = SEX, group=interaction(table1::eqcut(WT,2,fwt), SEX,table1::eqcut(ALB,2,f))))+ facet_grid(table1::eqcut(ALB,2,f) ~ table1::eqcut(WT,2,fwt), labeller = label_value)+ labs(linetype="Sex",colour="Sex",fill="Sex", caption ="Simulation with Full Covariate Distribution with BSV 95% (Covariate Effects + BSV) Percentiles", x = "Time (h)", y="Plasma Concentrations")+ coord_cartesian(ylim=c(0,3.5)) plotranges ## Adding Uncertainty from a Varcov Matrix First, we will invent a varcov matrix by assuming 25% relative standard errors and correlations of 0.2 across the board. We then simulate a 100 set of parameters using a multivariate normal (kept at 100 for the vignette, use more replicates for a real project). Also, unless the model was written in a way to allow unconstrained parameter values, care should be taken to make sure the simulated parameters are valid and make sense. When available, use the set of parameters from bootstrap replicates. Variance Covariance Matrix of fixed effects: theta <- unclass(as.list(param(modcovsim))) theta[c("WT", "SEX", "ALB")] <- NULL theta <- unlist(theta) as.data.frame(t(theta)) KA CL V Vp Qp CLALB CLSEX CLWT VSEX VWT 0.5 4 10 50 10 -0.8 0.2 1 0.07 1  varcov <- cor2cov( matrix(0.2, nrow=length(theta), ncol=length(theta)), sd=theta*0.25) rownames(varcov) <- colnames(varcov) <- names(theta) as.data.frame(varcov) KA CL V Vp Qp CLALB CLSEX CLWT VSEX VWT KA 0.0156250 0.0250 0.06250 0.31250 0.06250 -5e-03 0.001250 0.006250 0.0004375 0.006250 CL 0.0250000 1.0000 0.50000 2.50000 0.50000 -4e-02 0.010000 0.050000 0.0035000 0.050000 V 0.0625000 0.5000 6.25000 6.25000 1.25000 -1e-01 0.025000 0.125000 0.0087500 0.125000 Vp 0.3125000 2.5000 6.25000 156.25000 6.25000 -5e-01 0.125000 0.625000 0.0437500 0.625000 Qp 0.0625000 0.5000 1.25000 6.25000 6.25000 -1e-01 0.025000 0.125000 0.0087500 0.125000 CLALB -0.0050000 -0.0400 -0.10000 -0.50000 -0.10000 4e-02 -0.002000 -0.010000 -0.0007000 -0.010000 CLSEX 0.0012500 0.0100 0.02500 0.12500 0.02500 -2e-03 0.002500 0.002500 0.0001750 0.002500 CLWT 0.0062500 0.0500 0.12500 0.62500 0.12500 -1e-02 0.002500 0.062500 0.0008750 0.012500 VSEX 0.0004375 0.0035 0.00875 0.04375 0.00875 -7e-04 0.000175 0.000875 0.0003063 0.000875 VWT 0.0062500 0.0500 0.12500 0.62500 0.12500 -1e-02 0.002500 0.012500 0.0008750 0.062500 ### Generating Sets of Parameters with Uncertainty Second, we generate the sim_parameters dataset using mvrnorm and then incorporate the uncertainty by simulating using a different set of parameters (row) for each replicate. First Few Rows of a Dataset Containing Simulated Fixed Effects with Uncertainty: set.seed(678549) # mvtnorm::rmvnorm is another option that can be explored sim_parameters <- MASS::mvrnorm(nsim, theta, varcov, empirical=T) %>% as.data.table head(sim_parameters) KA CL V Vp Qp CLALB CLSEX CLWT VSEX VWT 0.5378926 3.859760 11.776303 52.39755 7.724327 -0.8068285 0.2954971 1.1740211 0.0396454 1.3859991 0.2232708 3.419806 8.011096 56.04744 10.419219 -0.9223983 0.1840836 0.8403384 0.0638582 1.0879117 0.5308651 4.015689 13.664634 52.43240 8.704927 -1.0714217 0.2108198 0.8195740 0.0721642 0.6809932 0.6364762 2.250400 6.909049 51.94856 9.417977 -0.8955413 0.1682543 1.1129100 0.0713622 0.9283083 0.4949809 2.903534 11.522544 43.72084 10.580375 -0.4551942 0.1512482 0.6108703 0.0909812 1.0599149 0.4545920 4.312410 10.023194 21.07089 10.220731 -0.8837281 0.1635944 1.1535344 0.0610196 1.0494356 ### Iterative Simulation to Apply the Uncertainty Third, we illustrate how you can iterate over a set of parameters value using a for loop. We then overlay the previous simulation results without uncertainty on the one with uncertainty to visualize the effect of adding it. The user might want to use a parallel back-end to speed-up the simulations. The code that can simulate the uncertainty of BSV is commented out to keep the vignette fast. idata <- copy(covdatasim) ev1 <- ev(time=0, amt=100, cmt=1) data.dose <- as.data.frame(ev1) iter_sims <- NULL for(i in 1:nsim) { # you might want to resample your covariate database here # e.g. subsample from a large pool of patient # include uncertainty on your covariate distribution data.all <- data.table(idata, data.dose, sim_parameters[i]) out <- modcovsim %>% data_set(data.all) %>% #zero_re() %>% #omat(rxode2::cvPost(2000, matrix(c(0.09,0.01,0.01,0.09), 2, 2), #type = "invWishart")) %>% # unc on bsv uncomment and increase nsim for CPT:PSP paper mrgsim(start=0, end=24, delta=0.25) %>% as.data.frame %>% as.data.table out[, rep := i] iter_sims <- rbind(iter_sims, out) } f <- function(x, xcat, which, what, from, to, ...) { what <- sub("ALB", "\nAlbumin", what) sprintf("%s %s [%s to %s[", which, what, signif_pad(from, 3, FALSE), signif_pad(to, 3, FALSE)) } fwt <- function(x, xcat, which, what, from, to, ...) { what <- sub("WT", "\nWeight", what) sprintf("%s %s [%s to %s[", which, what, signif_pad(from, 3, FALSE), signif_pad(to, 3, FALSE)) } iter_sims_summary_all <- iter_sims %>% mutate(WT=table1::eqcut(WT,2,fwt),ALB=table1::eqcut(ALB,2,f)) %>% group_by(time,WT,ALB,SEX)%>% summarize( P50= median(CP) , P05 = quantile(CP,0.05), P95= quantile(CP,0.95)) iter_sims_summary_all$SEX <- as.factor(iter_sims_summary_all$SEX ) iter_sims_summary_all$SEX <- factor(iter_sims_summary_all$SEX,labels = c("Female","Male")) legendlabel<- "Median\n5%-95%" plotrangesunc<- ggplot(iter_sims_summary_all, aes(time,P50,col=SEX,fill=SEX,group=SEX,linetype=SEX) )+ geom_ribbon(aes(ymin=P05,ymax=P95),alpha=0.3,linetype=0)+ geom_line(size=1)+ facet_grid(ALB ~ WT, labeller = label_value)+ labs(linetype=legendlabel,colour=legendlabel,fill=legendlabel, caption ="Simulation with joint correlated covariate distributions with uncertainty and between subject variability", x = "Time (h)", y="Plasma Concentrations")+ coord_cartesian(ylim=c(0,3.5)) plotrangesunc+ theme(axis.title.y = element_text(size=12))+ coord_cartesian(ylim=c(0,4)) ### Compute PK Parameters and Boxplots Similar to an earlier section, we compute the PK parameters by patient and by replicate standardize by the computed median for reference subject by replicate and provide a plot. We add some data manipulation to construct more informative labels that will help in the plotting. out.df.parameters <- iter_sims[, derive.exposure(time, CP), by=.(rep, ID, WT, SEX, ALB)] refvalues <- out.df.parameters[,.(medparam = median(paramvalue)), by=.(paramname,rep)] Median Parameter Values for the Reference:  setkey(out.df.parameters, paramname, rep) out.df.parameters <- merge(out.df.parameters,refvalues) out.df.parameters[, paramvaluestd := paramvalue/medparam] out.df.parameters[, SEXCAT := ifelse( SEX==0,"Female","Male")] out.df.parameters[, REF := "All Subjects"] out.df.parameters[, WTCAT4 := table1::eqcut( out.df.parameters$WT,4,varlabel = "Weight")]
out.df.parameters[, ALBCAT3 := table1::eqcut( out.df.parameters$ALB,3,varlabel = "Albumin")] nca.summaries.long <- melt(out.df.parameters, measure=c("REF","WTCAT4","ALBCAT3","SEXCAT"), value.name = "covvalue",variable.name ="covname" ) nca.summaries.long$covvalue <- as.factor( nca.summaries.long$covvalue) nca.summaries.long$covvalue <- reorder(nca.summaries.long$covvalue,nca.summaries.long$paramvalue)

nca.summaries.long$covvalue <- factor(nca.summaries.long$covvalue,
levels =c(
"1st tertile of Albumin: [31.0,44.0)"
, "2nd tertile of Albumin: [44.0,46.0)"
, "3rd tertile of Albumin: [46.0,54.0]"
, "Male"
, "Female"
, "All Subjects"
, "1st quartile of Weight: [40.6,71.3)"
, "2nd quartile of Weight: [71.3,85.0)"
, "3rd quartile of Weight: [85.0,98.2)"
,"4th quartile of Weight: [98.2,222]"
))

nca.summaries.long$covvalue2 <- factor(nca.summaries.long$covvalue,
labels =c(
"T1: [31.0,44.0)"
, "T2: [44.0,46.0)"
, "T3: [46.0,54.0]"
, "Male"
, "Female"
, "All Subjects"
, "Q1: [40.6,71.3)"
, "Q2: [71.3,85.0)"
, "Q3: [85.0,98.2)"
, "Q4: [98.2,222]"
))

nca.summaries.long$covname<- as.factor(nca.summaries.long$covname)
nca.summaries.long$covname<- factor(nca.summaries.long$covname,
levels =c("WTCAT4","SEXCAT","ALBCAT3","REF"),
labels = c("Weight","Sex","Albumin","REF"))
func <- function(bob) c(min(bob), median(bob), max(bob))
boxplotMV<- ggplot(nca.summaries.long
, aes(x=covvalue2  , y=paramvalue ))+
facet_grid (  paramname ~covname, scales="free", labeller=label_parsed,
switch="both",space="free_x") +
geom_boxplot(outlier.shape = NA) +
theme_bw(base_size = 12)+
theme(axis.title=element_blank(),
strip.placement = "outside",
axis.text.x = element_text(angle=20,vjust = 1, hjust = 1, face = "bold"),
strip.text.y.left = element_text(angle= 0,vjust = 1, hjust = 1,face = "bold"))+
scale_y_continuous(breaks = scales::pretty_breaks(n=4)  )
boxplotMV

## Alternative View of the Data: Distributions and Intervals

Here we provide an alternative visual summary of the standardized PK parameters. It shows intervals of interest split by covariate quantiles (e.g.Â below/above median, tertiles, quartiles).It is exactly the same data as the boxplots. We need to keep in mind here that although we split by one covariate quantiles we can split jointly by more than one covariate. Also, the presented effects are joint effects of all covariates viewed from a specific covariate angle.

ggridgesplot<- ggplot(nca.summaries.long,
aes(x=paramvaluestd,y=covvalue,
fill=factor(..quantile..),
height=..ndensity..))+
facet_grid(covname~paramname,scales="free_y")+
annotate( "rect",
xmin = 0.8,
xmax = 1.25,
ymin = -Inf,
ymax = Inf,
fill = "gray",alpha=0.4
)+
stat_density_ridges(
geom = "density_ridges_gradient", calc_ecdf = TRUE,
quantile_lines = TRUE, rel_min_height = 0.01,scale=0.9,
quantiles = c(0.05,0.5, 0.95))+
geom_vline( aes(xintercept = 1),size = 1)+
scale_fill_manual(
name = "Probability", values = c("white","#0000FFA0", "#0000FFA0", "white"),
labels = c("(0, 0.05]", "(0.05, 0.5]","(0.5, 0.95]", "(0.95, 1]")
)+
geom_vline(data=data.frame (xintercept=1),  aes(xintercept =xintercept  ),size = 1)+
theme_bw()+
theme(legend.position = "none")+
labs(x="Effects Of Covariates on PK Parameter",y="")+
scale_x_continuous(breaks=c(0.5,0.8,1/0.8,1/0.5,1/0.25),trans ="log" )+
coord_cartesian(xlim=c(0.25,3))
ggridgesplot

## Putting it all Together Using forest_plot

Here we have the joint effects of correlated covariates, BSV and uncertainty concisely summarized and presented using intervals.

coveffectsdatacovrep <- nca.summaries.long %>%
dplyr::group_by(paramname,covname,covvalue) %>%
dplyr::summarize(
mid= median(paramvaluestd),
lower= quantile(paramvaluestd,0.05),
upper = quantile(paramvaluestd,0.95)) %>%
dplyr::filter(!is.na(mid))

coveffectsdatacovrepbsv <- coveffectsdatacovrep[coveffectsdatacovrep$covname=="REF",] coveffectsdatacovrepbsv$covname <- "BSV"
coveffectsdatacovrepbsv$covvalue <- "90% of patients" coveffectsdatacovrepbsv$label <-    "90% of patients"
coveffectsdatacovrepbsv$lower <- bsvranges$P05
coveffectsdatacovrepbsv$upper <- bsvranges$P95
coveffectsdatacovrepbsv2 <- coveffectsdatacovrep[coveffectsdatacovrep$covname=="REF",] coveffectsdatacovrepbsv2$covname <- "BSV"
coveffectsdatacovrepbsv2$covvalue <- "50% of patients" coveffectsdatacovrepbsv2$label <-    "50% of patients"
coveffectsdatacovrepbsv2$lower <- bsvranges$P25
coveffectsdatacovrepbsv2$upper <- bsvranges$P75

coveffectsdatacovrepbsv<- rbind(coveffectsdatacovrep,coveffectsdatacovrepbsv2,
coveffectsdatacovrepbsv)
coveffectsdatacovrepbsv <- coveffectsdatacovrepbsv %>%
mutate(
label= covvalue,
LABEL = paste0(format(round(mid,2), nsmall = 2),
" [", format(round(lower,2), nsmall = 2), "-",
format(round(upper,2), nsmall = 2), "]"))
coveffectsdatacovrepbsv<- as.data.frame(coveffectsdatacovrepbsv)

coveffectsdatacovrepbsv$label <- gsub(": ", ":\n", coveffectsdatacovrepbsv$label)

coveffectsdatacovrepbsv$covname <-factor(as.factor(coveffectsdatacovrepbsv$covname ),
levels = c("Weight","Sex","Albumin","REF","BSV"))
coveffectsdatacovrepbsv$label <- factor(coveffectsdatacovrepbsv$label,
levels =c( "1st tertile of Albumin:\n[31.0,44.0)"
, "2nd tertile of Albumin:\n[44.0,46.0)"
, "3rd tertile of Albumin:\n[46.0,54.0]"
, "Male", "Female"
, "All Subjects","90% of patients","50% of patients"
, "1st quartile of Weight:\n[40.6,71.3)"
, "2nd quartile of Weight:\n[71.3,85.0)"
, "3rd quartile of Weight:\n[85.0,98.2)"
,"4th quartile of Weight:\n[98.2,222]"
))
coveffectsdatacovrepbsv$label <- factor(coveffectsdatacovrepbsv$label,
labels =c("T1:\n[31.0,44.0)"
, "T2:\n[44.0,46.0)"
, "T3:\n[46.0,54.0]"
, "Male", "Female"
, "All Subjects","90% of patients","50% of patients"
, "Q1:\n[40.6,71.3)"
, "Q2:\n[71.3,85.0)"
, "Q3:\n[85.0,98.2)"
, "Q4:\n[98.2,222]"
))

interval_legend_text <- "Median (points)\n90% intervals (horizontal lines) of joint effects:
covariate distributions, uncertainty
and between subject variability"
interval_bsv_text    <- "BSV (points)\nPrediction Intervals (horizontal lines)"
ref_legend_text      <- "Reference (vertical line)\nClinically relevant limits\n(gray area)"
area_legend_text     <- "Reference (vertical line)\nClinically relevant limits\n(gray area)"

#emf("Figure_PKdist_forest.emf",width= 15, height = 7.5)
png("./coveffectsplot_full.png",width =9.5 ,height = 8,units = "in",res=72)

forest_plot(coveffectsdatacovrepbsv[coveffectsdatacovrepbsv$covname!="REF"& coveffectsdatacovrepbsv$covname!="BSV",
],
ref_area = c(0.8, 1/0.8),x_range = c(0.4,3),
strip_placement = "outside",base_size = 18,
y_label_text_size = 9,x_label_text_size = 10,
xlabel = "Fold Change Relative to Reference",
ref_legend_text =ref_legend_text,
area_legend_text =ref_legend_text ,
interval_legend_text = interval_legend_text,
plot_title = "",
interval_bsv_text = interval_bsv_text,
facet_formula = "covname~paramname",
facet_switch = "y",
table_facet_switch = "both",
reserve_table_xaxis_label_space = FALSE,
facet_scales = "free_y", facet_space = "free",
paramname_shape = FALSE,
table_position = "below",
show_table_yaxis_tick_label = TRUE,
table_text_size= 4,
plot_table_ratio = 1,
show_table_facet_strip = "both",
logxscale = TRUE,
major_x_ticks = c(0.5,0.8,1/0.8,1/0.5),
return_list = FALSE)
dev.off()
#> png
#>   2
Covariate Effects Plot With BSV.