using Pumas
using PharmaDatasets
Model Selection Methods - Mixed Routines
As stated in Introduction to Covariate Selection Methods, broadly, there are two main types of Stepwise Covariate Model (SCM):
- Forward Selection (FS)
- Backward Elimination (BE)
In this tutorial we’ll explore a mixed routine based on both Forward Selection (FS) and Backward Elimination (BE).
1 Mixed Routines
One common mixed routine is to first perform a FS, followed by a BE. In all of these SCM routines, we’ll use the following criterion:
\[\operatorname{criterion}(f_p, f_b) = \operatorname{LRT}(f_p, f_b, \alpha)\]
where:
- \(f_p\) is the “proposed” model fit at the current iteration
- \(f_b\) is the model fit at the previous iteration (i.e. the current “best model”)
- \(\operatorname{LRT}\) is the likelihood-ratio test between models \(f_p\) and \(f_m\) using the \(\alpha\) value as the significance threshold
We’ll use different \(\alpha\) values depending on stage of the analysis. Common values are:
- FS: \(\alpha = 0.01\)
- BE: \(\alpha = 0.001\)
The rationale is that a less strict criterion value is used when adding variables during FS, and a more stringent criterion is used for allowing covariates to remain in the model during BE.
2 Example in Pumas
Let’s showcase an example that builds from the model we used in Covariate Selection Methods - Introduction.
First, let’s import the following packages:
We are going to use the po_sad_1 dataset from PharmaDatasets:
df = dataset("po_sad_1")
first(df, 5)5×14 DataFrame
| Row | id | time | dv | amt | evid | cmt | rate | age | wt | doselevel | isPM | isfed | sex | route |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Int64 | Float64 | Float64? | Float64? | Int64 | Int64? | Float64 | Int64 | Int64 | Int64 | String3 | String3 | String7 | String3 | |
| 1 | 1 | 0.0 | missing | 30.0 | 1 | 1 | 0.0 | 51 | 74 | 30 | no | yes | male | ev |
| 2 | 1 | 0.25 | 35.7636 | missing | 0 | missing | 0.0 | 51 | 74 | 30 | no | yes | male | ev |
| 3 | 1 | 0.5 | 71.9551 | missing | 0 | missing | 0.0 | 51 | 74 | 30 | no | yes | male | ev |
| 4 | 1 | 0.75 | 97.3356 | missing | 0 | missing | 0.0 | 51 | 74 | 30 | no | yes | male | ev |
| 5 | 1 | 1.0 | 128.919 | missing | 0 | missing | 0.0 | 51 | 74 | 30 | no | yes | male | ev |
This is an oral dosing (route = "ev") NMTRAN-formatted dataset. It has 18 subjects, each with 1 dosing event (evid = 1) and 18 measurement events (evid = 0); and the following covariates:
age: age in years (continuous)wt: weight in kg (continuous)doselevel: dosing amount, either30,60or90milligrams (categorical)isPM: subject is a poor metabolizer (binary)isfed: subject is fed (binary)sex: subject sex (binary)
population =
read_pumas(df; observations = [:dv], covariates = [:wt, :isPM, :isfed], route = :route)Population
Subjects: 18
Covariates: wt, isPM, isfed
Observations: dvWe’ll use the same 2-compartment oral absorption model as in Covariate Selection Methods - Introduction, but now adding additional covariates and covariate effects:
model = @model begin
@metadata begin
desc = "full covariate model"
timeu = u"hr"
end
@param begin
"""
Clearance (L/hr)
"""
tvcl ∈ RealDomain(; lower = 0)
"""
Central Volume (L)
"""
tvvc ∈ RealDomain(; lower = 0)
"""
Peripheral Volume (L)
"""
tvvp ∈ RealDomain(; lower = 0)
"""
Distributional Clearance (L/hr)
"""
tvq ∈ RealDomain(; lower = 0)
"""
Fed - Absorption rate constant (h-1)
"""
tvkafed ∈ RealDomain(; lower = 0)
"""
Fasted - Absorption rate constant (h-1)
"""
tvkafasted ∈ RealDomain(; lower = 0)
"""
Power exponent on weight for Clearance
"""
dwtcl ∈ RealDomain()
"""
Proportional change in CL due to PM
"""
ispmoncl ∈ RealDomain(; lower = -1, upper = 1)
"""
- ΩCL
- ΩVc
- ΩKa
- ΩVp
- ΩQ
"""
Ω ∈ PDiagDomain(5)
"""
Proportional RUV (SD scale)
"""
σₚ ∈ RealDomain(; lower = 0)
end
@random begin
η ~ MvNormal(Ω)
end
@covariates begin
"""
Poor Metabolizer
"""
isPM
"""
Fed status
"""
isfed
"""
Weight (kg)
"""
wt
end
@pre begin
CL = tvcl * (wt / 70)^dwtcl * (1 + ispmoncl * (isPM == "yes")) * exp(η[1])
Vc = tvvc * (wt / 70) * exp(η[2])
Ka = (tvkafed + (isfed == "no") * (tvkafasted)) * exp(η[3])
Q = tvq * (wt / 70)^0.75 * exp(η[4])
Vp = tvvp * (wt / 70) * exp(η[5])
end
@dynamics Depots1Central1Periph1
@derived begin
cp := @. 1000 * (Central / Vc)
"""
DrugY Concentration (ng/mL)
"""
dv ~ @. Normal(cp, cp * σₚ)
end
endPumasModel
Parameters: tvcl, tvvc, tvvp, tvq, tvkafed, tvkafasted, dwtcl, ispmoncl, Ω, σₚ
Random effects: η
Covariates: isPM, isfed, wt
Dynamical variables: Depot, Central, Peripheral
Derived: dv
Observed: dvWith the model done, now we define our initial parameters:
iparams = (;
tvkafed = 0.4,
tvkafasted = 1.5,
tvcl = 4.0,
tvvc = 70.0,
tvq = 4.0,
tvvp = 50.0,
dwtcl = 0.75,
ispmoncl = -0.7,
Ω = Diagonal(fill(0.04, 5)),
σₚ = 0.1,
)(tvkafed = 0.4,
tvkafasted = 1.5,
tvcl = 4.0,
tvvc = 70.0,
tvq = 4.0,
tvvp = 50.0,
dwtcl = 0.75,
ispmoncl = -0.7,
Ω = [0.04 0.0 … 0.0 0.0; 0.0 0.04 … 0.0 0.0; … ; 0.0 0.0 … 0.04 0.0; 0.0 0.0 … 0.0 0.04],
σₚ = 0.1,)Now we create our criterion:
lrt_criterion(f; α = 0.01) = dof(f) * cquantile(Chisq(1), α) - (2 * loglikelihood(f))lrt_criterion (generic function with 1 method)lrt_criterion takes a model fit f and a desired \(\alpha\) value α. It calculates the LRT by first multiplying the model’s degree-of-freedom (dof) by the complementary quantile (cquantile) of the \(\alpha\) value from a \(\chi^2\) distribution with one degree of freedom. Then, it subtracts from this value the model fit -2LL (minus 2 times log-likelihood) with constant. We use one degree-of-freedom because our SCM methods as currently implemented for both FS and BE, always add or remove a single covariate effect in each model evaluation.
First, we’ll run covariate_select with lrt_criterion as a criterion and the FS method. Finally, we run covariate_select with lrt_criterion as a criterion and the BE method.
Note
Our α = 0.01, so we use the default value defined in lrt_criterion function.
covar_result_fs = covariate_select(
model,
population,
iparams,
FOCE();
control_param = (:dwtcl, :tvkafasted, :ispmoncl),
criterion = lrt_criterion,
method = CovariateSelection.Forward,
)[ Info: fitting model with zero restrictions on: dwtcl, tvkafasted, ispmoncl
[ Info: criterion: 2765.9438260321062
[ Info: fitting model with zero restrictions on: tvkafasted, ispmoncl
[ Info: criterion: 2768.8716816089895
[ Info: fitting model with zero restrictions on: dwtcl, ispmoncl
[ Info: criterion: 2735.6913640080456
[ Info: fitting model with zero restrictions on: dwtcl, tvkafasted
[ Info: criterion: 2758.2100172199457
[ Info: current best model has zero restrictions on dwtcl, ispmoncl
[ Info: criterion improved, continuing!
[ Info: fitting model with zero restrictions on: ispmoncl
[ Info: criterion: 2738.5517536234006
[ Info: fitting model with zero restrictions on: dwtcl
[ Info: criterion: 2727.974107213667
[ Info: current best model has zero restrictions on dwtcl
[ Info: criterion improved, continuing!
[ Info: fitting model with zero restrictions on:
[ Info: criterion: 2706.47449837789
[ Info: current best model has zero restrictions on
[ Info: criterion improved, continuing!
┌ Info: final best model summary
│ best_criterion = 2706.47449837789
└ best_zero_restrictions = ()Pumas.CovariateSelectionResults Criterion: lrt_criterion Method: Forward Best model: Unrestricted parameters: dwtcl, tvkafasted, ispmoncl Zero restricted parameters: Criterion value: 2706.47 Fitted model summary: 7×4 DataFrame Row │ free_parameters zero_parameters fit ⋯ │ Tuple… Tuple… Fit ⋯ ─────┼────────────────────────────────────────────────────────────────────────── 1 │ () (:dwtcl, :tvkafasted, :ispmoncl) Fit ⋯ 2 │ (:dwtcl,) (:tvkafasted, :ispmoncl) Fit 3 │ (:tvkafasted,) (:dwtcl, :ispmoncl) Fit 4 │ (:ispmoncl,) (:dwtcl, :tvkafasted) Fit 5 │ (:dwtcl, :tvkafasted) (:ispmoncl,) Fit ⋯ 6 │ (:tvkafasted, :ispmoncl) (:dwtcl,) Fit 7 │ (:dwtcl, :tvkafasted, :ispmoncl) () Fit 2 columns omitted
Now we extract the best model from the covar_result by accessing the best_model field. That gives us a model fit, which we can use to extract the best model:
best_model_fs = covar_result_fs.best_model.modelPumasModel
Parameters: tvcl, tvvc, tvvp, tvq, tvkafed, tvkafasted, dwtcl, ispmoncl, Ω, σₚ
Random effects: η
Covariates: isPM, isfed, wt
Dynamical variables: Depot, Central, Peripheral
Derived: dv
Observed: dv
Note
Our α = 0.001, so need to override the default value defined in lrt_criterion function.
covar_result_bs = covariate_select(
best_model_fs,
population,
iparams,
FOCE();
control_param = (:dwtcl, :tvkafasted, :ispmoncl),
criterion = x -> lrt_criterion(x; α = 0.001),
method = CovariateSelection.Backward,
)[ Info: fitting model with zero restrictions on:
[ Info: criterion: 2765.171872352871
[ Info: fitting model with zero restrictions on: dwtcl
[ Info: criterion: 2782.478811619007
[ Info: fitting model with zero restrictions on: tvkafasted
[ Info: criterion: 2792.0260533170317
[ Info: fitting model with zero restrictions on: ispmoncl
[ Info: criterion: 2793.0564580287405
[ Info: current best model has zero restrictions on dwtcl
[ Info: criterion did not improve, stopping!
┌ Info: final best model summary
│ best_criterion = 2765.171872352871
└ best_zero_restrictions = ()Pumas.CovariateSelectionResults Criterion: #16 Method: Backward Best model: Unrestricted parameters: dwtcl, tvkafasted, ispmoncl Zero restricted parameters: Criterion value: 2765.17 Fitted model summary: 4×4 DataFrame Row │ free_parameters zero_parameters fitted_model ⋯ │ Tuple… Tuple… FittedPumas… ⋯ ─────┼────────────────────────────────────────────────────────────────────────── 1 │ (:dwtcl, :tvkafasted, :ispmoncl) () FittedPumasModel(... ⋯ 2 │ (:tvkafasted, :ispmoncl) (:dwtcl,) FittedPumasModel(... 3 │ (:dwtcl, :ispmoncl) (:tvkafasted,) FittedPumasModel(... 4 │ (:dwtcl, :tvkafasted) (:ispmoncl,) FittedPumasModel(... 2 columns omitted
Note
x -> fun(x; ...) is an anonymous function. Since we need to override the default keyword argument α in lrt_criterion for the backward elimination pass, we cannot use:
criterion = lrt_criterionWe need to use:
criterion = x -> lrt_criterion(x; α = 0.001)As you can see, the results of this mixed routine: FS followed by BE process; results in the same final model as the FS and BE processes used in the Covariate Selection Methods - Forward Selection and Covariate Selection Methods - Backward Elimination.
covar_result_bs.best_modelFittedPumasModel
Successful minimization: true
Likelihood approximation: FOCE
Log-likelihood value: -1306.793
Number of subjects: 18
Number of parameters: Fixed Optimized
0 14
Observation records: Active Missing
dv: 270 0
Total: 270 0
-------------------------
Estimate
-------------------------
tvcl 3.6888
tvvc 70.043
tvvp 48.707
tvq 4.3511
tvkafed 0.41113
tvkafasted 1.0894
dwtcl 0.81858
ispmoncl -0.62382
Ω₁,₁ 0.022271
Ω₂,₂ 0.050184
Ω₃,₃ 0.031199
Ω₄,₄ 0.10595
Ω₅,₅ 0.035064
σₚ 0.098043
-------------------------