mrgsolve
library(mrgsim)
library(mrgsolve)
library(dplyr)
mod <- mread("popex", modlib())
param(mod)
.
. Model parameters (N=4):
. name value . name value
. TVCL 1 | TVV 24
. TVKA 0.5 | WT 70
run <- load_run("inst/yaml/foo.yaml")
out <- sim_run(mod,run)
runtime
. user system elapsed
. 0.499 0.014 0.515
dim(out)
. [1] 109850 5
out %>%
group_by(arm) %>%
filter(time==4032) %>%
summarise(Mean = mean(DV), Min = min(DV), Max = max(DV), N=n())
. # A tibble: 3 x 5
. arm Mean Min Max N
. <chr> <dbl> <dbl> <dbl> <int>
. 1 arm1 7.014079 0.11433253 78.11614 250
. 2 arm2 3.492472 0.09303598 22.02542 250
. 3 arm3 17.942659 0.65358606 112.13541 150
name: Simulation 1
endpoints: [DV,CP,RESP]
output_file: saved.RDS
model: irm1
project: /Users/kyleb/Rlibs/mrgsolve/models
covariate:
WT: {distribution: rlnorm, meanlog: 4.3 , sdlog: 0.5, by: ID, lb: 40, ub: 140}
SEX: {distribution: rbinomial, p: 0.5}
AGE1: {distribution: runif, min: 21, max: 42, as: AGE}
AGE2: {distribution: runif, min: 12, max: 27, as: AGE}
covset:
cov1: [WT,SEX,AGE1]
cov2: [WT,SEX,AGE2]
period:
a: {amt: 100, ii: 24, addl: 83}
b: {amt: 200, ii: 24, addl: 83}
c: {amt: 500, ii: 24, addl: 168}
sequence:
s1: [a, b]
s2: [a, a]
s3: [c]
sample:
des1: {end: 4032, delta: 24, add: [0]}
arm:
arm1: {nid: 250, sequence: s1, covset: cov1}
arm2: {nid: 250, sequence: s2, covset: cov2}
arm3: {nid: 150, sequence: c, covset: cov1}
objects:
- "Sigma <- diag(c(1,1))"
- "mu <- log(c(2,20))"
name: Pediatrics and adults, same dose
endpoints: [DV]
output_file: saved.RDS
model: popex
covariate:
WT1: {distribution: rlnorm, meanlog: 4.3 , sdlog: 0.5, by: ID, lb: 50, ub: 120, as: WT}
WT2: {distribution: rlnorm, meanlog: 3.6 , sdlog: 0.5, by: ID, lb: 25, ub: 80, as: WT}
covset:
cov1: [WT1]
cov2: [WT2]
period:
dose100: {amt: 100, ii: 24, addl: 168}
sample:
des1: {end: 4032, delta: 24, add: [0]}
arm:
adult: {nid: 250, sequence: dose100, covset: cov1}
pediatric: {nid: 250, sequence: dose100, covset: cov2}
The run has arms
arms(run)
. # A tibble: 3 x 9
. nid sequence covset sample arm covsetn sequencen samplen armn
. <int> <chr> <chr> <chr> <chr> <int> <int> <int> <int>
. 1 250 s1 cov1 des1 arm1 1 1 1 1
. 2 250 s2 cov2 des1 arm2 2 2 1 2
. 3 150 c cov1 des1 arm3 1 6 1 3
treatment sequences
sequences(run)
. # A tibble: 6 x 2
. sequence periods
. <chr> <chr>
. 1 s1 a,b
. 2 s2 a,a
. 3 s3 c
. 4 a a
. 5 b b
. 6 c c
treatment periods
run$periods
. $s1
. Events:
. time cmt amt ii addl evid
. 1 0 1 100 24 83 1
. 2 2016 1 200 24 83 1
.
. $s2
. Events:
. time cmt amt ii addl evid
. 1 0 1 100 24 83 1
. 2 2016 1 100 24 83 1
.
. $s3
. Events:
. time cmt amt ii addl evid
. 1 0 1 500 24 168 1
.
. $a
. Events:
. time cmt amt ii addl evid
. 1 0 1 100 24 83 1
.
. $b
. Events:
. time cmt amt ii addl evid
. 1 0 1 200 24 83 1
.
. $c
. Events:
. time cmt amt ii addl evid
. 1 0 1 500 24 168 1
covariate sets
covsets(run)
. $cov1
. Formulae
. WT~rlnorm(meanlog=4.3,sdlog=0.5)|ID
. SEX~rbinomial(p=0.5)
. AGE~runif(min=21,max=42)
.
. $cov2
. Formulae
. WT~rlnorm(meanlog=4.3,sdlog=0.5)|ID
. SEX~rbinomial(p=0.5)
. AGE~runif(min=12,max=27)
sampling times
run$designs
. $des1
. start: 0 end: 4032 delta: 24 offset: 0 min: 0 max: 4032
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