; this script is generated automatically

<DATAFILE>

[FILEINFO]
file = 'PKdata_ggPMX.csv'
delimiter = comma
header = {ID, AGE0, WT0, HT0, BMI0, SEX, RACE, DISE, TRT, TRT_NAME, CMT, EVID, DV, UNIT, MDV, BLOQ, AMT, ADM, TINF, NTIM, NTIM1, DAY, ND, TIMEUNIT, DESC, TIME, VARNAME, YTYPE, TRT_TYPE, ILOW, ISS, DOSE}

[CONTENT]
ID = {use=identifier}
AGE0 = {use=covariate, type=continuous}
WT0 = {use=covariate, type=continuous}
HT0 = {use=covariate, type=continuous}
SEX = {use=covariate, type=categorical}
RACE = {use=covariate, type=categorical}
DISE = {use=covariate, type=categorical}
TRT = {use=covariate, type=continuous}
EVID = {use=eventIdentifier}
DV = {use=observation, name=y1, ytype=1, type=continuous}
MDV = {use=missingDependentVariable}
AMT = {use=amount}
ADM = {use=administration}
TINF = {use=infusionTime}
TIME = {use=time}
YTYPE = {use=observationType}
ILOW = {use=covariate, type=categorical}
ISS = {use=occasion}

<MODEL>

[COVARIATE]
input = {AGE0, WT0, HT0, TRT, SEX, RACE, DISE, ILOW, OCC, sOCC}
SEX = {type=categorical, categories={1, 2}}
RACE = {type=categorical, categories={1, 2, 88}}
DISE = {type=categorical, categories={1, 2}}
ILOW = {type=categorical, categories={1, 2}}
OCC = {type=categorical, categories={1, 2}}
sOCC = {type=categorical, categories={1, 1_2}}

EQUATION:
t_AGE0 = log(AGE0/54)
t_WT0 = log(WT0/75.2)
t_HT0 = log(HT0/170)
t_TRT = log(TRT/3)

[INDIVIDUAL]
input = {F_pop, beta_F_ILOW_2, beta_F_OCC_2, ILOW, OCC, omega_F, gamma_F, CL_pop, omega_CL, Vc_pop, omega_Vc, Q1_pop, Vp1_pop, omega_Vp1, ka_input2_pop, Tlag_input2_pop}
ILOW = {type=categorical, categories={1, 2}}
OCC = {type=categorical, categories={1, 2}}

DEFINITION:
F = {distribution=logitnormal, typical=F_pop, covariate={ILOW, OCC}, coefficient={{0, beta_F_ILOW_2}, {0, beta_F_OCC_2}}, varlevel={id, id*occ}, sd={omega_F, gamma_F}}
CL = {distribution=lognormal, typical=CL_pop, sd=omega_CL}
Vc = {distribution=lognormal, typical=Vc_pop, sd=omega_Vc}
Q1 = {distribution=lognormal, typical=Q1_pop, no-variability}
Vp1 = {distribution=lognormal, typical=Vp1_pop, sd=omega_Vp1}
ka_input2 = {distribution=lognormal, typical=ka_input2_pop, no-variability}
Tlag_input2 = {distribution=lognormal, typical=Tlag_input2_pop, no-variability}

[LONGITUDINAL]
input = {a, b}

file = './popPKmodel_MLXTRAN.txt'

DEFINITION:
y1 = {distribution=normal, prediction=Cc, errorModel=combined1(a, b)}

<FIT>
data = y1
model = y1

<PARAMETER>
F_pop = {value=0.52, method=MLE}
beta_F_ILOW_2 = {value=0.1, method=MLE}
beta_F_OCC_2 = {value=0, method=MLE}
omega_F = {value=0.5, method=MLE}
gamma_F = {value=1, method=MLE}
CL_pop = {value=0.75, method=MLE}
omega_CL = {value=0.5, method=MLE}
Vc_pop = {value=3.5, method=MLE}
omega_Vc = {value=0.5, method=MLE}
Q1_pop = {value=0.79, method=MLE}
Vp1_pop = {value=3.3, method=MLE}
omega_Vp1 = {value=0.5, method=MLE}
ka_input2_pop = {value=0.2, method=MLE}
Tlag_input2_pop = {value=1e-10, method=FIXED}
a = {value=1, method=MLE}
b = {value=0.3, method=MLE}

<MONOLIX>

[TASKS]
populationParameters()
individualParameters(method = conditionalMode)
fim(method = Linearization)
logLikelihood(method = Linearization)
plotResult()

[SETTINGS]
GLOBAL:
exportpath = 'RESULTS'
autochains = no

POPULATION:
exploratoryautostop = no
smoothingautostop = no
mixturesinitbeta = 1

