using Pumas
using PharmaDatasets
Covariate Selection Methods - Introduction
In pharmacometric workflows, we often have competing models to select from. In this tutorial we will review selection criteria and automated procedures to select the best model out of a set of competing candidate models.
First, we’ll review how to measure model fit, then we’ll cover model selection algorithms.
1 Model Fit Measures
Traditionally in Statistics, model comparison has been done based on a theoretical divergence metric that originates from information theory’s entropy:
\[H = - \operatorname{E}\log(p) = -\sum_i p_i \log(p_i)\]
where \(p_i\) is the probability of occurrence of the \(i\)-th possible value.
Note
We use the \(\log\) scale because it transforms a product of probabilities into a sum, which is both numerically faster and numerically more stable due to the robustness against floating point underflow.
Entropy was the inspiration behind Akaike’s Information Criterion (AIC) (Akaike, 1973):
\[\operatorname{AIC} = -2\log{\hat{\mathcal{L}}} + 2k\]
where \(\hat{\mathcal{L}}\) is the estimated value of the likelihood for a given model and data, and \(k\) is the number of parameters in the model. Generally the likelihood is estimated by maximizing the likelihood function, thus the name maximum likelihood estimation (MLE). The likelihood describes how well the model fits the data, and in certain conditions, can be treated similarly to a probability: higher values means higher plausibility. Hence, models with higher likelihood values demonstrate better fits to the data. Since we are multiplying by a negative value, this means that lower values are preferred.
Note
The \(-2\) was proposed in Akaike’s 1973 original paper to simplify some calculations involving \(\chi^2\) distributions and was kept around since then.
AIC was devised to “punish” model complexity, i.e models that have more parameters to fit to the data. This is why we add \(2\) to the loglikelihood value for every parameter that the model has. Due to the preference of lower AIC values this penalizes models by the number of parameters, while also making it possible to compare models with different complexities.
Building from the AIC, the Bayesian Information Criterion (BIC) (Schwarz, 1978) uses the same idea, but the penalty term is different:
\[\operatorname{BIC} = -2\log{\hat{\mathcal{L}}} + k\log(n)\]
where \(\hat{\mathcal{L}}\) is the estimated value of the likelihood for a given model and data, \(k\) is the model’s number of parameters, and \(n\) is the number of observations. It is called Bayesian because it uses a “Bayesian” argument to derive the punishment term \(k\log(n)\) in the original 1975 paper.
1.1 Example in Pumas
Let’s go over an example of model fit measures in Pumas.
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)
Let’s parse df into a Population with read_pumas:
population =
read_pumas(df; observations = [:dv], covariates = [:wt, :isPM, :isfed], route = :route)Population
Subjects: 18
Covariates: wt, isPM, isfed
Observations: dvLet’s create a 2-compartment oral absorption base model with no covariate effects:
base_model = @model begin
@metadata begin
desc = "base 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)
"""
Absorption rate constant (1/h)
"""
tvka ∈ RealDomain(; lower = 0)
"""
- ΩCL
- ΩVc
- ΩKa
- ΩVp
- ΩQ
"""
Ω ∈ PDiagDomain(5)
"""
Proportional RUV (SD scale)
"""
σₚ ∈ RealDomain(; lower = 0)
end
@random begin
η ~ MvNormal(Ω)
end
@pre begin
CL = tvcl * exp(η[1])
Vc = tvvc * exp(η[2])
Ka = tvka * exp(η[3])
Q = tvq * exp(η[4])
Vp = tvvp * exp(η[5])
end
@dynamics Depots1Central1Periph1
@derived begin
cp := @. 1000 * (Central / Vc)
"""
Drug Concentration (ng/mL)
"""
dv ~ @. Normal(cp, cp * σₚ)
end
endPumasModel
Parameters: tvcl, tvvc, tvvp, tvq, tvka, Ω, σₚ
Random effects: η
Covariates:
Dynamical variables: Depot, Central, Peripheral
Derived: dv
Observed: dvLet’s go over the model.
In the @metadata block we are adding a model description and adding information regarding the time units (hours).
Next, we define the model’s parameters in @param while also prepending them with a string that serves as an annotation for the parameter description. This is helpful for post-processing, since Pumas can use the description instead of the parameter name in tables and figures.
Our ηs are defined in the @random block and are sampled from a multivariate normal distribution with mean 0 and a positive-diagonal covariance matrix Ω. We have 5 ηs, one for each PK typical value (also known as θs).
We proceed by defining the individual PK parameters in the @pre block. Each typical value is incremented by the subject’s ηs in a non-linear exponential transformation. This is done to enforce that all individual PK parameters are constrained to being positive. This also has a side effect that the individual PK parameters will be log-normally distributed.
We use the aliased short notation Depots1Central1Periph1 for the ODE system in the @dynamics. This is equivalent to having the following equations:
Depot' = -Ka * Depot
Central' = Ka * Depot - (CL + Q) / Vc * Central + Q / Vp * Peripheral
Peripheral' = Q / Vc * Central - Q / Vp * PeripheralNote that, in order for Depots1Central1Periph1 work correctly, we need to define Ka, CL, Q, Vc, and Vp in the @pre block.
Finally, in the @derived block we define our error model (or likelihood for the statistically-minded). Here we are using a proportional error model with the Gaussian/normal likelihood. Note that Normal is parameterized with mean and standard deviation, not with variance. That’s why we name our proportional error parameter as σₚ and not σ²ₚ.
Let’s now define a initial set of parameter estimates to fit our model:
iparams = (;
tvka = 0.4,
tvcl = 4.0,
tvvc = 70.0,
tvq = 4.0,
tvvp = 50.0,
Ω = Diagonal(fill(0.04, 5)),
σₚ = 0.1,
)(tvka = 0.4,
tvcl = 4.0,
tvvc = 70.0,
tvq = 4.0,
tvvp = 50.0,
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,)We call the fit function to estimate the parameters of the model:
base_fit = fit(base_model, population, iparams, FOCE())[ Info: Checking the initial parameter values.
[ Info: The initial negative log likelihood and its gradient are finite. Check passed.
Iter Function value Gradient norm
0 1.630402e+03 2.604358e+02
* time: 0.019394874572753906
1 1.499510e+03 9.365700e+01
* time: 0.380612850189209
2 1.447619e+03 4.714464e+01
* time: 0.42054200172424316
3 1.427906e+03 4.439232e+01
* time: 0.43823885917663574
4 1.414326e+03 2.726109e+01
* time: 0.46880388259887695
5 1.387798e+03 1.159019e+01
* time: 0.48673486709594727
6 1.382364e+03 7.060796e+00
* time: 0.5099639892578125
7 1.380839e+03 4.839103e+00
* time: 0.5274930000305176
8 1.380281e+03 4.075615e+00
* time: 0.5505988597869873
9 1.379767e+03 3.303901e+00
* time: 0.568763017654419
10 1.379390e+03 2.856359e+00
* time: 0.5912840366363525
11 1.379193e+03 2.650736e+00
* time: 0.613879919052124
12 1.379036e+03 2.523349e+00
* time: 0.630483865737915
13 1.378830e+03 2.638648e+00
* time: 0.6530818939208984
14 1.378593e+03 3.463990e+00
* time: 0.6699469089508057
15 1.378335e+03 3.471127e+00
* time: 0.6928088665008545
16 1.378143e+03 2.756670e+00
* time: 0.7100610733032227
17 1.378019e+03 2.541343e+00
* time: 0.7333199977874756
18 1.377888e+03 2.163251e+00
* time: 0.7508289813995361
19 1.377754e+03 2.571076e+00
* time: 0.7742688655853271
20 1.377620e+03 3.370764e+00
* time: 0.7979660034179688
21 1.377413e+03 3.938291e+00
* time: 0.815558910369873
22 1.377094e+03 4.458016e+00
* time: 0.8390910625457764
23 1.376674e+03 5.713348e+00
* time: 0.8569378852844238
24 1.375946e+03 5.417530e+00
* time: 0.881166934967041
25 1.375343e+03 5.862876e+00
* time: 0.8997139930725098
26 1.374689e+03 5.717165e+00
* time: 0.9242448806762695
27 1.374056e+03 4.400490e+00
* time: 0.9492928981781006
28 1.373510e+03 2.191437e+00
* time: 0.9688599109649658
29 1.373277e+03 1.203587e+00
* time: 0.9962899684906006
30 1.373233e+03 1.157761e+00
* time: 1.0153729915618896
31 1.373218e+03 8.770728e-01
* time: 1.0397059917449951
32 1.373204e+03 8.021952e-01
* time: 1.0646450519561768
33 1.373190e+03 6.613857e-01
* time: 1.083695888519287
34 1.373183e+03 7.602394e-01
* time: 1.1082329750061035
35 1.373173e+03 8.552154e-01
* time: 1.1264798641204834
36 1.373162e+03 6.961928e-01
* time: 1.1508989334106445
37 1.373152e+03 3.162546e-01
* time: 1.170367956161499
38 1.373148e+03 1.747381e-01
* time: 1.1941709518432617
39 1.373147e+03 1.258699e-01
* time: 1.2173020839691162
40 1.373147e+03 1.074908e-01
* time: 1.2350139617919922
41 1.373147e+03 6.799619e-02
* time: 1.2585840225219727
42 1.373147e+03 1.819329e-02
* time: 1.276196002960205
43 1.373147e+03 1.338880e-02
* time: 1.298825979232788
44 1.373147e+03 1.370144e-02
* time: 1.3152110576629639
45 1.373147e+03 1.315666e-02
* time: 1.3375298976898193
46 1.373147e+03 1.065953e-02
* time: 1.3544590473175049
47 1.373147e+03 1.069775e-02
* time: 1.3759288787841797
48 1.373147e+03 6.234846e-03
* time: 1.393326997756958
49 1.373147e+03 6.234846e-03
* time: 1.423698902130127
50 1.373147e+03 6.234846e-03
* time: 1.4579648971557617FittedPumasModel
Successful minimization: true
Likelihood approximation: FOCE
Log-likelihood value: -1373.1468
Number of subjects: 18
Number of parameters: Fixed Optimized
0 11
Observation records: Active Missing
dv: 270 0
Total: 270 0
-------------------
Estimate
-------------------
tvcl 2.8344
tvvc 77.801
tvvp 48.754
tvq 3.9789
tvka 1.028
Ω₁,₁ 0.2638
Ω₂,₂ 0.2288
Ω₃,₃ 0.40047
Ω₄,₄ 0.37968
Ω₅,₅ 0.21495
σₚ 0.097805
-------------------Now we are ready to showcase model fit measures. All of these functions should take a result from fit and output a real number.
Let’s start with aic and bic which are included in Pumas:
aic(base_fit)2768.2935804173985bic(base_fit)2807.876221966381We are also free to create our own functions if we want to use something different than aic or bic.
Here’s an example of a function that takes a fitted Pumas model, m, and outputs the -2LL (minus 2 times log-likelihood) without the constant. This is a model fit measure commonly used by NONMEM users and is is known as OFV: Objective Function Value. Hence, we will name the function ofv:
ofv(m) = (-2 * loglikelihood(m)) - (nobs(m) * log(2π))ofv (generic function with 1 method)We can use it on our base_fit model fit result:
ofv(base_fit)2250.06677248687542 Likelihood Ratio Tests
A likelihood-ratio test (LRT) is a statistical hypothesis test used in the field of statistics and probability theory to compare two statistical models and determine which one provides a better fit to a given set of observed data. It is particularly useful in the context of maximum likelihood estimation (MLE) and is commonly used for hypothesis testing in parametric statistical modeling.
The basic idea behind the likelihood ratio test is to compare the likelihoods of two competing models:
Null Hypothesis (\(H_0\)): This is the model that you want to test against. It represents a specific set of parameter values or restrictions on the model.
Alternative Hypothesis (\(H_a\)): This is the alternative model, often a more complex one or the one you want to support.
The test statistic is calculated as the ratio of the likelihood under the alternative model (\(H_a\)) to the likelihood under the null model (\(H_0\)). Mathematically, it can be expressed as:
\[\operatorname{LRT} = - 2 \log \left( \frac{\mathcal{L}(H_0)}{\mathcal{L}(H_a)} \right)\]
where:
- \(\operatorname{LRT}\): likelihood ratio test statistic
- \(\mathcal{L}(H_0)\): likelihood under \(H_0\), the likelihood of the data under the null hypothesis
- \(\mathcal{L}(H_a)\): likelihood under \(H_a\), the likelihood of the data under the alternative hypothesis
The LRT statistic follows a \(\chi^2\) (chi-squared) distribution with degrees of freedom equal to the difference in the number of parameters between the two models (i.e., the degrees of freedom is the number of additional parameters in the alternative model). In practice, you compare the LRT statistic to \(\chi^2\) distribution to determine whether the alternative model is a significantly better fit to the data than the null model.
The key idea is that if the p-value derived from the LRT statistic is lower than your desired \(\alpha\) (the type-1 error rate, commonly set to \(0.05\)), you would reject the null hypothesis in favor of the alternative hypothesis, indicating that the alternative model provides a better fit to the data.
Note
The likelihood-ratio test requires that the models be nested, i.e. the more complex model can be transformed into the simpler model by imposing constraints on the former’s parameters.
This is generally the case when performing LRT in a covariate selection context. However, be mindful of not violating this assumption when performing LRT.
2.1 Example in Pumas
Pumas provides us with the lrtest function to perform LRT. It takes 2 positional arguments as competing models:
- Model under \(H_0\) (i.e. the model with less parameters)
- Model under \(H_a\) (i.e. the model with more parameters)
Let’s define a covariate model that takes wt into consideration for all the clearance and volume PK parameters:
covariate_model = @model begin
@metadata begin
desc = "covariate model that uses weight covariate information"
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)
"""
Absorption rate constant (h-1)
"""
tvka ∈ RealDomain(; lower = 0)
"""
Power exponent on weight for Clearance # new
"""
dwtcl ∈ RealDomain() # new
"""
Power exponent on weight for Distributional Clearance # new
"""
dwtq ∈ RealDomain() # new
"""
- 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
"""
Weight (kg) # new
"""
wt # new
end
@pre begin
CL = tvcl * exp(η[1]) * (wt / 70)^dwtcl # new
Vc = tvvc * exp(η[2]) * (wt / 70) # new
Ka = tvka * exp(η[3])
Q = tvq * exp(η[4]) * (wt / 70)^dwtq # new
Vp = tvvp * exp(η[5]) * (wt / 70) # new
end
@dynamics Depots1Central1Periph1
@derived begin
cp := @. 1000 * (Central / Vc)
"""
Drug Concentration (ng/mL)
"""
dv ~ @. Normal(cp, cp * σₚ)
end
endPumasModel
Parameters: tvcl, tvvc, tvvp, tvq, tvka, dwtcl, dwtq, Ω, σₚ
Random effects: η
Covariates: wt
Dynamical variables: Depot, Central, Peripheral
Derived: dv
Observed: dvThis is almost the same model as before. However, we are adding a few tweaks (commented with # new):
wtin the new@covariatesblock- allometric scaling based on
wtfor the individual PK parametersCL,Q,VcandVp - new parameters in
@paramfor the exponent of the power function ofwton both individual clearance PK parametersCLandQ
Since covariate_model has two new parameters in the @param block, we need to add them to the initial set of parameter estimates. We can do this by creating a new NamedTuple that builts upon the last one iparams, while also adding initial values for dwtcl and dwtq:
iparams_covariate = (; iparams..., dwtcl = 0.75, dwtq = 0.75)(tvka = 0.4,
tvcl = 4.0,
tvvc = 70.0,
tvq = 4.0,
tvvp = 50.0,
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,
dwtcl = 0.75,
dwtq = 0.75,)
Tip
We are using Julia’s splatting ... operator to expand inline the iparams NamedTuple.
Now we fit our covariate_model:
covariate_fit = fit(covariate_model, population, iparams_covariate, FOCE())[ Info: Checking the initial parameter values.
[ Info: The initial negative log likelihood and its gradient are finite. Check passed.
Iter Function value Gradient norm
0 1.555051e+03 2.584685e+02
* time: 6.985664367675781e-5
1 1.436886e+03 9.959639e+01
* time: 0.019348859786987305
2 1.383250e+03 3.318037e+01
* time: 0.054045915603637695
3 1.372961e+03 2.525341e+01
* time: 0.07117486000061035
4 1.365242e+03 2.081002e+01
* time: 0.10243391990661621
5 1.350200e+03 1.667386e+01
* time: 0.11963605880737305
6 1.346374e+03 9.195785e+00
* time: 0.14302396774291992
7 1.344738e+03 8.614309e+00
* time: 0.1599259376525879
8 1.343902e+03 4.950745e+00
* time: 0.1831200122833252
9 1.343662e+03 1.478699e+00
* time: 0.2004098892211914
10 1.343626e+03 9.575005e-01
* time: 0.22291088104248047
11 1.343609e+03 8.509968e-01
* time: 0.24035906791687012
12 1.343589e+03 7.964671e-01
* time: 0.2626309394836426
13 1.343567e+03 8.202459e-01
* time: 0.28008604049682617
14 1.343550e+03 8.133359e-01
* time: 0.30224084854125977
15 1.343542e+03 6.865506e-01
* time: 0.3251688480377197
16 1.343538e+03 3.869567e-01
* time: 0.3415069580078125
17 1.343534e+03 2.805019e-01
* time: 0.36441898345947266
18 1.343531e+03 3.271442e-01
* time: 0.3807718753814697
19 1.343529e+03 4.584302e-01
* time: 0.40361905097961426
20 1.343527e+03 3.951940e-01
* time: 0.41976404190063477
21 1.343525e+03 1.928385e-01
* time: 0.4457979202270508
22 1.343524e+03 1.958575e-01
* time: 0.4620640277862549
23 1.343523e+03 2.008844e-01
* time: 0.48570895195007324
24 1.343522e+03 1.636364e-01
* time: 0.5017058849334717
25 1.343522e+03 1.041929e-01
* time: 0.5246598720550537
26 1.343521e+03 7.417497e-02
* time: 0.5407638549804688
27 1.343521e+03 7.297961e-02
* time: 0.5633718967437744
28 1.343521e+03 8.109591e-02
* time: 0.5788788795471191
29 1.343520e+03 7.067080e-02
* time: 0.6012158393859863
30 1.343520e+03 5.088025e-02
* time: 0.6167559623718262
31 1.343520e+03 4.980085e-02
* time: 0.6391358375549316
32 1.343520e+03 4.778940e-02
* time: 0.6546218395233154
33 1.343520e+03 5.667067e-02
* time: 0.6768980026245117
34 1.343520e+03 5.825591e-02
* time: 0.6921639442443848
35 1.343519e+03 5.354660e-02
* time: 0.7144498825073242
36 1.343519e+03 5.300792e-02
* time: 0.7302219867706299
37 1.343519e+03 4.011720e-02
* time: 0.7531249523162842
38 1.343519e+03 3.606197e-02
* time: 0.7694070339202881
39 1.343519e+03 3.546034e-02
* time: 0.7921538352966309
40 1.343519e+03 3.525307e-02
* time: 0.8076279163360596
41 1.343519e+03 3.468091e-02
* time: 0.8306269645690918
42 1.343519e+03 3.313732e-02
* time: 0.8461630344390869
43 1.343518e+03 4.524162e-02
* time: 0.8689420223236084
44 1.343518e+03 5.769309e-02
* time: 0.8846189975738525
45 1.343518e+03 5.716613e-02
* time: 0.9070489406585693
46 1.343517e+03 4.600797e-02
* time: 0.9227390289306641
47 1.343517e+03 3.221948e-02
* time: 0.9452810287475586
48 1.343517e+03 2.610758e-02
* time: 0.9605450630187988
49 1.343517e+03 2.120270e-02
* time: 0.9828090667724609
50 1.343517e+03 1.887916e-02
* time: 0.998215913772583
51 1.343517e+03 1.229271e-02
* time: 1.0205769538879395
52 1.343517e+03 4.778802e-03
* time: 1.0360078811645508
53 1.343517e+03 2.158460e-03
* time: 1.0581209659576416
54 1.343517e+03 2.158460e-03
* time: 1.080096960067749
55 1.343517e+03 2.158460e-03
* time: 1.111011028289795
56 1.343517e+03 2.158460e-03
* time: 1.1457829475402832FittedPumasModel
Successful minimization: true
Likelihood approximation: FOCE
Log-likelihood value: -1343.5173
Number of subjects: 18
Number of parameters: Fixed Optimized
0 13
Observation records: Active Missing
dv: 270 0
Total: 270 0
--------------------
Estimate
--------------------
tvcl 2.7287
tvvc 70.681
tvvp 47.396
tvq 4.0573
tvka 0.98725
dwtcl 0.58351
dwtq 1.176
Ω₁,₁ 0.21435
Ω₂,₂ 0.050415
Ω₃,₃ 0.42468
Ω₄,₄ 0.040356
Ω₅,₅ 0.045987
σₚ 0.097904
--------------------Now we are ready to perform LRT with lrtest:
mytest = lrtest(base_fit, covariate_fit)Statistic: 59.3
Degrees of freedom: 2
P-value: 0.0The degrees of freedom of the underlying \(\chi^2\) distribution is \(2\), i.e. we have two additional parameters in the model under \(H_a\); and the test statistic is \(59.3\).
The \(p\)-value corresponding for the test statistic and degree of freedom is very close to \(0\). It prints as 0.0, but we can access the value with the pvalue function:
pvalue(mytest)1.3554737256701043e-13This indicates strong evidence against the base_model (i.e. model under \(H_0\)) and in favor of the covariate_model (i.e. model under \(H_a\)).
3 Model Selection Algorithms
There are several model selection techniques that take into account covariate selection. In the statistical literature, the reader can check Thayer (1990), and for the pharmacometric context, the reader can check Hutmacher & Kowalski (2015) and Jonsson & Karlsson (1998).
Pumas currently only implements the Stepwise Covariate Model (SCM). SCM, also known as stepwise procedures, is a model building strategy that is used to identify the best covariate model for a given dataset by a series of iterations (Hutmacher & Kowalski, 2015). Broadly, there are two main types of SCM:
- Forward Selection (FS)
- Backward Elimination (BE)
We will be covering these in detail in a new set of tutorials, please check tutorials.pumas.ai.
4 References
Akaike, H. (1973). Information theory and the extension of the maximum likelihood principle. Proceedings of the Second International Symposium on Information Theory.
Hutmacher, M. M., & Kowalski, K. G. (2015). Covariate selection in pharmacometric analyses: a review of methods. British journal of clinical pharmacology, 79(1), 132–147. https://doi.org/10.1111/bcp.12451
Jonsson, E. N., & Karlsson, M. O. (1998). Automated covariate model building within NONMEM. Pharmaceutical research, 15(9), 1463–1468. https://doi.org/10.1023/a:1011970125687
Schwarz, Gideon E. (1978). Estimating the dimension of a model. Annals of Statistics, 6 (2): 461–464, doi:10.1214/aos/1176344136.
Thayer, J. D. (1990). Implementing Variable Selection Techniques in Regression. ERIC.