Estimate the posterior distribution of individual parameters by SIR
Source:R/param_estim.R
poso_estim_sir.RdEstimates the posterior distribution of individual parameters by Sequential Importance Resampling (SIR)
Usage
poso_estim_sir(
dat = NULL,
prior_model = NULL,
n_sample = 10000,
n_resample = 1000,
return_model = TRUE,
nocb = FALSE
)Arguments
- dat
Dataframe. An individual subject dataset following the structure of NONMEM/rxode2 event records.
- prior_model
A
posologyrprior population pharmacokinetics model, a list of six objects.- n_sample
Number of samples from the S-step
- n_resample
Number of samples from the R-step
- return_model
A boolean. Returns a rxode2 model using the estimated ETAs if set to
TRUE.- nocb
A boolean. for time-varying covariates: the next observation carried backward (nocb) interpolation style, similar to NONMEM. If
FALSE, the last observation carried forward (locf) style will be used. Defaults toFALSE.
Value
If return_model is set to FALSE, a list of one element: a
dataframe $eta of ETAs from the posterior distribution, estimated by
Sequential Importance Resampling.
If return_model is set to TRUE, a list of the dataframe of the posterior
distribution of ETA, and a rxode2 model using the estimated distributions of
ETAs.
Examples
# model
mod_run001 <- function() {
ini({
THETA_Cl <- 4.0
THETA_Vc <- 70.0
THETA_Ka <- 1.0
ETA_Cl ~ 0.2
ETA_Vc ~ 0.2
ETA_Ka ~ 0.2
prop.sd <- sqrt(0.05)
})
model({
TVCl <- THETA_Cl
TVVc <- THETA_Vc
TVKa <- THETA_Ka
Cl <- TVCl*exp(ETA_Cl)
Vc <- TVVc*exp(ETA_Vc)
Ka <- TVKa*exp(ETA_Ka)
K20 <- Cl/Vc
Cc <- centr/Vc
d/dt(depot) = -Ka*depot
d/dt(centr) = Ka*depot - K20*centr
Cc ~ prop(prop.sd)
})
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
TIME=c(0.0,1.0,14.0),
DV=c(NA,25.0,5.5),
AMT=c(2000,0,0),
EVID=c(1,0,0),
DUR=c(0.5,NA,NA))
# estimate the posterior distribution of population parameters
poso_estim_sir(dat=df_patient01,prior_model=mod_run001,
n_sample=1e3,n_resample=1e2)
#>
#>
#>
#>
#> $eta
#> ETA_Cl ETA_Vc ETA_Ka
#> 1 0.6504014 -0.46513521 0.43653693
#> 2 0.7998906 -0.03341945 0.16662057
#> 3 0.5739246 -0.05601440 0.71549851
#> 4 0.7089199 -0.17563653 0.37654151
#> 5 0.6224641 -0.44103680 0.11062341
#> 6 0.5756166 -0.54744862 -0.11639086
#> 7 0.2097388 -0.80535811 -0.39535473
#> 8 0.4410245 -0.51368224 0.18837049
#> 9 0.8041649 -0.20072306 0.23829165
#> 10 0.3270809 -0.20504934 0.30219971
#> 11 0.6224641 -0.44103680 0.11062341
#> 12 0.6901039 -0.30108197 -0.14402225
#> 13 0.5756166 -0.54744862 -0.11639086
#> 14 0.6224641 -0.44103680 0.11062341
#> 15 0.7089199 -0.17563653 0.37654151
#> 16 0.7740971 -0.30891172 0.57370521
#> 17 0.6504014 -0.46513521 0.43653693
#> 18 0.6280538 -0.23392662 0.44682453
#> 19 0.7089199 -0.17563653 0.37654151
#> 20 0.3976482 -0.64529135 0.23007851
#> 21 0.3425794 -0.70136613 0.12968425
#> 22 0.5716248 -0.36818884 0.36830570
#> 23 0.2097388 -0.80535811 -0.39535473
#> 24 0.8530497 -0.23492316 0.25581125
#> 25 0.6354166 -0.31357126 0.73866202
#> 26 0.6280538 -0.23392662 0.44682453
#> 27 0.5756166 -0.54744862 -0.11639086
#> 28 0.8530497 -0.23492316 0.25581125
#> 29 0.6354166 -0.31357126 0.73866202
#> 30 0.5480420 -0.78238693 -0.54016365
#> 31 0.6504014 -0.46513521 0.43653693
#> 32 0.6956159 -0.25114851 0.92671472
#> 33 0.3653215 -0.46129773 0.18425818
#> 34 0.4621170 -0.71068616 0.26608879
#> 35 0.2319779 -0.86032500 -0.48170475
#> 36 0.6956159 -0.25114851 0.92671472
#> 37 0.3313779 -0.54665431 0.48542548
#> 38 0.7089199 -0.17563653 0.37654151
#> 39 0.6947877 -0.46349506 0.19509695
#> 40 0.4179024 -0.58931306 0.22519506
#> 41 0.6224641 -0.44103680 0.11062341
#> 42 0.5480420 -0.78238693 -0.54016365
#> 43 0.3653215 -0.46129773 0.18425818
#> 44 0.4796500 -0.65544436 -0.11063743
#> 45 0.4737102 -0.05879126 0.68993753
#> 46 0.6224641 -0.44103680 0.11062341
#> 47 0.8041649 -0.20072306 0.23829165
#> 48 0.6441458 -0.17385456 0.07400396
#> 49 0.5480420 -0.78238693 -0.54016365
#> 50 0.5480420 -0.78238693 -0.54016365
#> 51 0.7089199 -0.17563653 0.37654151
#> 52 0.3769539 -0.82826593 0.23448758
#> 53 0.6834596 -0.07578277 0.01909532
#> 54 0.2527094 -0.21569803 0.25635943
#> 55 0.6431052 -0.37166373 -0.12400846
#> 56 0.4410245 -0.51368224 0.18837049
#> 57 0.7740971 -0.30891172 0.57370521
#> 58 0.2319779 -0.86032500 -0.48170475
#> 59 0.5716248 -0.36818884 0.36830570
#> 60 0.6280538 -0.23392662 0.44682453
#> 61 0.5756166 -0.54744862 -0.11639086
#> 62 0.5716248 -0.36818884 0.36830570
#> 63 0.6014266 -0.46330347 0.48532615
#> 64 0.4621170 -0.71068616 0.26608879
#> 65 0.2708922 -0.52232311 -0.08780402
#> 66 0.5739246 -0.05601440 0.71549851
#> 67 0.5716248 -0.36818884 0.36830570
#> 68 0.3946202 -0.37801414 0.10956928
#> 69 0.6631807 0.37417320 1.32753128
#> 70 0.6224641 -0.44103680 0.11062341
#> 71 0.6177660 0.34344899 0.79709830
#> 72 0.4796500 -0.65544436 -0.11063743
#> 73 0.6280538 -0.23392662 0.44682453
#> 74 0.2324490 -0.80580463 -0.28471752
#> 75 0.3270809 -0.20504934 0.30219971
#> 76 0.6224641 -0.44103680 0.11062341
#> 77 0.7089199 -0.17563653 0.37654151
#> 78 0.6224641 -0.44103680 0.11062341
#> 79 0.5716248 -0.36818884 0.36830570
#> 80 0.5716248 -0.36818884 0.36830570
#> 81 0.5756166 -0.54744862 -0.11639086
#> 82 0.3769539 -0.82826593 0.23448758
#> 83 0.6947877 -0.46349506 0.19509695
#> 84 0.6504014 -0.46513521 0.43653693
#> 85 0.5756166 -0.54744862 -0.11639086
#> 86 0.6224641 -0.44103680 0.11062341
#> 87 0.2944519 -0.31777262 0.32041506
#> 88 0.5065188 -0.43510689 0.59017798
#> 89 0.6354166 -0.31357126 0.73866202
#> 90 0.5065188 -0.43510689 0.59017798
#> 91 0.3951683 -0.42558280 0.21811956
#> 92 0.2486862 -0.80758491 -0.05621151
#> 93 0.7089199 -0.17563653 0.37654151
#> 94 0.4796500 -0.65544436 -0.11063743
#> 95 0.5065188 -0.43510689 0.59017798
#> 96 0.3946202 -0.37801414 0.10956928
#> 97 0.2196114 -0.43600196 0.46038729
#> 98 0.1867108 -0.72833532 0.32243635
#> 99 0.2496268 -0.29416330 0.40590506
#> 100 0.6014266 -0.46330347 0.48532615
#>
#> $model
#> ── Solved rxode2 object ──
#> ── Parameters ($params): ──
#> # A tibble: 100 × 8
#> sim.id THETA_Cl THETA_Vc THETA_Ka prop.sd ETA_Cl ETA_Vc ETA_Ka
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 4 70 1 0.224 0.650 -0.465 0.437
#> 2 2 4 70 1 0.224 0.800 -0.0334 0.167
#> 3 3 4 70 1 0.224 0.574 -0.0560 0.715
#> 4 4 4 70 1 0.224 0.709 -0.176 0.377
#> 5 5 4 70 1 0.224 0.622 -0.441 0.111
#> 6 6 4 70 1 0.224 0.576 -0.547 -0.116
#> 7 7 4 70 1 0.224 0.210 -0.805 -0.395
#> 8 8 4 70 1 0.224 0.441 -0.514 0.188
#> 9 9 4 70 1 0.224 0.804 -0.201 0.238
#> 10 10 4 70 1 0.224 0.327 -0.205 0.302
#> # ℹ 90 more rows
#> ── Initial Conditions ($inits): ──
#> depot centr AUC
#> 0 0 0
#>
#> Simulation without uncertainty in parameters, omega, or sigma matricies
#>
#> ── First part of data (object): ──
#> # A tibble: 200 × 14
#> sim.id time TVCl TVVc TVKa Cl Vc Ka K20 rxCc Cc depot
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 4 70 1 7.67 44.0 1.55 0.174 28.5 28.5 6.42e+2
#> 2 1 14 4 70 1 7.67 44.0 1.55 0.174 4.66 4.66 1.18e-6
#> 3 2 1 4 70 1 8.90 67.7 1.18 0.131 16.2 16.2 8.37e+2
#> 4 2 14 4 70 1 8.90 67.7 1.18 0.131 5.45 5.45 1.79e-4
#> 5 3 1 4 70 1 7.10 66.2 2.05 0.107 22.2 22.2 4.50e+2
#> 6 3 14 4 70 1 7.10 66.2 2.05 0.107 7.30 7.30 1.20e-9
#> # ℹ 194 more rows
#> # ℹ 2 more variables: centr <dbl>, AUC <dbl>
#>