Difference between revisions of "Visualization"

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How we graphically represent data depends on the type of data. Often for continuous data we use "spaghetti plots", where all of the observations are given on the same plot, and those for each individual are joined up using line segments. Time-to-event data are usually represented using Kaplan-Meyer plots, i.e., an estimate of the survival function for the first event. In the case of repeated events, we can instead represent the average cumulative number of events per individual.
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How we graphically represent data depends on the type of data. Often for continuous data we use "spaghetti plots", where all of the observations are given on the same plot, and those for each individual are joined up using line segments. Time-to-event data are usually represented using [https://en.wikipedia.org/wiki/Kaplan-Meier_survival_curve Kaplan-Meier plots], i.e., an estimate of the survival function for the first event. In the case of repeated events, we can instead represent the average cumulative number of events per individual.
  
  

Revision as of 10:37, 21 June 2013

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Introduction

Before deciding to model data, it is very important to be able to visualize it. This is especially the case for longitudinal data when we want to see how an outcome varies with time or as a function of another outcome. We may also want to visualize how the individual covariates are distributed, visually detect if there are relationships between variables, visually compare data from different groups, etc. Development of such visual exploration tools poses no methodological problems. It is simple to write a Matlab or R code for one's own needs. To illustrate the data visualization part of this chapter, we have created a little Matlab toolbox called $\popixplore$ (https://wiki.inria.fr/wikis/popix/images/7/71/Popixplore_1.1.zip) which can be freely downloaded and used.

It may also be useful to be able to visualize the model itself by undertaking a sensitivity analysis to look at how the structural model changes when we vary one or several parameters. This is important for truly understanding the structural model, i.e., what is behind the given mathematical equations. In the modeling context, we may also want to visually calibrate parameters in order to obtain predictions as close as possible to the observations. Developing such a tool is a difficult task because the tool needs to be able to easily input a model using some coding language, perform complex calculations, and provide a decent graphical interface (e.g., one that lets you easily modify the model parameters).

Various model visualization tools exist, such as Berkeley Madonna, specialized in the analysis of dynamical systems and the resolution of ordinary differential equations. Here, we use [$\mlxplore$] for some different reasons:


    • $\mlxplore$ uses the $\mlxtran$ language which is extremely flexible and well-adapted to implementing complex mixed-effects models. Indeed, with $\mlxtran$ we can implement pharmacokinetic models with complex administration schedules, include inter-individual variability in parameters, define a statistical model for the covariates, etc. Another extremely important aspect of $\mlxtran$ is that it rigorously adopts the model representation formalisms proposed in $\wikipopix$. In other words, model implementation is completely in sync with its mathematical representation.

    • $\mlxplore$ provides a clear graphical interface that of course allows us to visualize the structural model, but also the statistical model, which is of fundamental importance in the population approach. We can thus visualize the impact of covariates and inter-individual variability of model parameters on predictions.



Data exploration

The following example involves 80 individuals that receive a unique dose of an anticoagulant at time $t=0$. For each patient we then measure the plasmatic concentration of the drug at various times. This drug can cause undesirable side effects such as nose bleeds. If this happens, we also record the times at which this happens. The data is recorded in columns of a single text file pkrtte_data.csv. In this example, the columns are:


    id the ID number of the patient

    time dose administration and observation times

    amt the amount of drug administered

    y the observations (concentrations and events)

    ytype the type of observation: 1=concentration, 2=event

    weight a continuous individual covariate

    gender a categorical individual covariate (F or M)

    group four different groups receive different doses: A=40mg, B=60mg, C=80mg, D=100mg.


{{{2}}}
The datafile pkrtte_data.csv


We can read this datafile with the function readdatapx and add additional information about the data:


Ml.gif
MATLAB 

datafile.name='pkrtte_data.csv';
datafile.format='csv';  % can be "csv", "space", "tab" or ";"

info.header = {'ID','TIME','AMT','Y','YTYPE','COV','CAT','CAT'};
info.observation.name={'concentration','hemorrhaging'};
info.observation.type={'continuous','event'};
info.observation.unit={'mg/l',''};
info.covariate.unit={'kg',''};
info.time.unit='h';

data=readdatapx(datafile,info);


How we graphically represent data depends on the type of data. Often for continuous data we use "spaghetti plots", where all of the observations are given on the same plot, and those for each individual are joined up using line segments. Time-to-event data are usually represented using Kaplan-Meier plots, i.e., an estimate of the survival function for the first event. In the case of repeated events, we can instead represent the average cumulative number of events per individual.


Ml.gif
MATLAB 

>>exploredatapx(data)


{{{2}}}
Graphical representation of the data. Left: concentrations, right: average cumulative number of events per individual


When different groups receive different treatments, it can be useful to separately visualize the data from each group. Here for instance we can separate the patients into groups depending on the initial dose given.


{{{2}}}
Concentration profiles per dose group


Exploredata3a.png Exploredata3b.png
Distribution of weight and gender per dose group


Remark

The data file pkrtte_data.csv and the matlab script pkrtte_demo.m are available in the folder demos of $\popixplore$: https://wiki.inria.fr/wikis/popix/images/7/71/Popixplore_1.1.zip.



Model exploration

Exploring the structural model

Suppose that we now want to visualize the following joint model which is one that can be used for simultaneously modeling PK and time-to-event data:

\(\begin{eqnarray} k&=&Cl/V \\ \deriv{A_d} &=& - k_a \, A_d(t) \\ \deriv{A_c} &=& k_a \, A_d(t) - k \, A_c(t) \\ Cc(t) &=& {Ac(t)}/{V} \\ h(t) &=& h_0 \, \exp(\gamma\, Cc(t)) . \end{eqnarray} \)

Here, $A_d$ and $A_c$ are the amounts of drug in the depot and central compartments, $Cc$ the concentration in the central compartment and $h$ the hazard function for the event of interest (hemorrhaging for instance). The parameters of the model are the absorption rate constant $ka$, the volume of distribution $V$, the clearance $Cl$, the baseline hazard $h_0$ and the coefficient $\gamma$. We assume that the drug can be administered both intravenously and orally, meaning that the drug can be administered to both the depot and the central compartment.

We first need to implement this model using $\mlxtran$:


Monolix icon2.png
MLXTran joint1_model.txt
[PREDICTION]
input={ka, V, Cl, h0, gamma}

PK:
depot(type=1,target=Ad)
depot(type=2,target=Ac)

EQUATION:
k = Cl/V
ddt_Ad = -ka*Ad
ddt_Ac =  ka*Ad - k*Ac
Cc = Ac/V
h = h0*exp(gamma*Cc)


Here, an administration of type 1 (resp. 2) is an oral (resp. iv) administration.

The tasks, i.e., how the model is to be used, are then coded as an $\mlxplore$ project:


Mlxplore.jpg
MLXPlore joint1_project.txt

<MODEL>
file='joint1_model.txt'

<DESIGN>
[ADMINISTRATION]
adm1={time=0, amount=50,type=1}

<PARAMETER>
ka = 0.5
V = 10
Cl = 0.5
h0 = 0.01
gamma = 0.5

<OUTPUT>
list={Cc, h}
grid=0:0.1:100


In this example, a single dose of 50 mg is administered orally (target=Ad when type=1) at time 0. We have asked $\mlxplore$ to display the predicted concentration $Cc$ and the hazard function $h$ between $t=0$ and $t=100$ every $0.1\,h$ for a given set of parameters. We can then change the values of these parameters with the sliders to see what the impact on the two functions is.


{{{2}}}
Exploring the model using $\mlxplore$


We can easily modify the dose regimen without changing anything in the model itself. Suppose for instance that we want now to compare a treatment with repeated doses of 50mg every 24 hours and a treatment with repeated doses of 25mg every 12 hours. Only the section <DESIGN> needs to be modified:



Mlxplore.jpg
MLXPlore joint2_project.txt


<DESIGN>
[ADMINISTRATION]
adm1={time=0:24:144, amount=50,type=1}
adm2={time=0:12:144, amount=25,type=1}

Exploremodel2.png


We can combine different administrations (oral and intravenous for instance) into one global treatment:



Mlxplore.jpg
MLXPlore joint3_project.txt

<DESIGN>
[ADMINISTRATION]
adm1={time=0:24:144, amount=50,type=1}
adm2={time=6:48:150, amount=25,type=2}

[TREATMENT]
trt1={adm1, adm2}

Exploremodel3.png


Exploring the statistical model

One of the main advantages of $\mlxplore$ is its ability to graphically display the predicted distribution of the functions of interest $Cc$ and $h$ when certain parameters of the model are assumed to be random variables. Assume for instance that $V$, $Cl$ and $h_0$ are log-normally distributed. To take this into account, we simply need to insert a section [INDIVIDUAL] into the project file:


Monolix icon2.png
MLXTran joint2_model.txt
[INDIVIDUAL]
input={V_pop,Cl_pop,h0_pop,omega_V,omega_Cl,omega_h0}

DEFINITION:
V =  {distribution=lognormal, reference=V_pop,  sd=omega_V}
Cl = {distribution=lognormal, reference=Cl_pop, sd=omega_Cl}
h0 = {distribution=lognormal, reference=h0_pop, sd=omega_h0}

[PREDICTION]
input={ka, V, Cl, h0, gamma}
.
.
.


The parameters of the model are now the population parameters $V_{\rm pop}$, $Cl_{\rm pop}$, $h0_{\rm pop}$, $\omega_V$, $\omega_{Cl}$ and $\omega_{h_0}$ and the parameters $k_a$ and $\gamma$ which have no inter-individual variability.


Monolix icon2.png
MLXTran joint4_project.txt
<MODEL>
file='joint2_model.txt'

<DESIGN>
[ADMINISTRATION]
adm1={time=0, amount=50,type=1}

<PARAMETER>
V_pop = 10
Cl_pop = 0.5
h0_pop=0.01
omega_V = 0.2
omega_Cl = 0.3
omega_h0 = 0.2
ka = 0.5
gamma = 0.5

<OUTPUT>
list={Cc, h}
grid=0:0.1:100


When some parameters of the model are random variables, $\mlxplore$ displays the median of the predicted distribution and several prediction intervals (the default is to use different shaded areas for the 10%, 20%, ..., 90% quantiles).


{{{2}}}
Exploring the statistical model using $\mlxplore$


It is possible to introduce covariates into the statistical model by considering for example that the volume depends on the weight, and considering that these covariates are themselves random variables. This may be important if we are for example looking to visualize the amount of variation in concentration due to variation in weight, and the variation in concentration which remains unaccounted for, caused by random effects.


{{{2}}}
Exploring the statistical model using $\mlxplore$


The $\mlxtran$ model files and the $\mlxplore$ scripts can be downloaded here: https://wiki.inria.fr/wikis/popix/images/c/c6/Pk_mlxplore.zip.



Bibliography

POPIX Inria team - Popixplore 1.0

https://wiki.inria.fr/wikis/popix/images/7/71/Popixplore_1.1.zip
Bibtex
Author : POPIX Inria team
Title : Popixplore 1.0
In : -
Address :
Date :

Lixoft - MLXPlore 1.0

http://www.lixoft.eu/products/mlxplore/mlxplore-overview
Bibtex
Author : Lixoft
Title : MLXPlore 1.0
In : -
Address :
Date :

Macey, R., Oster, G., Zahnley, T. - Berkeley Madonna user’s guide

Berkeley (CA): University of California ,2000
Bibtex
Author : Macey, R., Oster, G., Zahnley, T.
Title : Berkeley Madonna user’s guide
In : Berkeley (CA): University of California -
Address :
Date : 2000

Chatterjee, S., Hadi, A. S. - Sensitivity analysis in linear regression

Vol. 327, Wiley,2009
Bibtex
Author : Chatterjee, S., Hadi, A. S.
Title : Sensitivity analysis in linear regression
In : -
Address :
Date : 2009

Faivre R., Looss B., Mahévas, S., Makowski, D., Monod, H. - Analyse de sensibilité et exploration de modèles

Editions Quae,2013
Bibtex
Author : Faivre R., Looss B., Mahévas, S., Makowski, D., Monod, H.
Title : Analyse de sensibilité et exploration de modèles
In : -
Address :
Date : 2013

Saltelli, A., Chan, K., Scott, E. M., others - Sensitivity analysis

Vol. 134, Wiley New York,2000
Bibtex
Author : Saltelli, A., Chan, K., Scott, E. M., others
Title : Sensitivity analysis
In : -
Address :
Date : 2000

Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S. - Global sensitivity analysis: the primer

Wiley-Interscience,2008
Bibtex
Author : Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S.
Title : Global sensitivity analysis: the primer
In : -
Address :
Date : 2008

Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. - Sensitivity analysis in practice: a guide to assessing scientific models

Wiley,2004
Bibtex
Author : Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.
Title : Sensitivity analysis in practice: a guide to assessing scientific models
In : -
Address :
Date : 2004


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