Plot Confidence Intervals of PK Model Predictions
Contents
Load Data
Load the sample data to fit.
clear all load data10_32R.mat gData = groupedData(data); gData.Properties.VariableUnits = {'','hour','milligram/liter','milligram/liter'}; sbiotrellis(gData,'ID','Time',{'CentralConc','PeripheralConc'},'Marker','+',... 'LineStyle','none');
Create Model
Create a two-compartment model.
pkmd = PKModelDesign; pkc1 = addCompartment(pkmd,'Central'); pkc1.DosingType = 'Infusion'; pkc1.EliminationType = 'linear-clearance'; pkc1.HasResponseVariable = true; pkc2 = addCompartment(pkmd,'Peripheral'); model = construct(pkmd); configset = getconfigset(model); configset.CompileOptions.UnitConversion = true;
Define Dosing
Define the infusion dose.
dose = sbiodose('dose','TargetName','Drug_Central'); dose.StartTime = 0; dose.Amount = 100; dose.Rate = 50; dose.AmountUnits = 'milligram'; dose.TimeUnits = 'hour'; dose.RateUnits = 'milligram/hour';
Define Parameters
Define parameters to estimate.
responseMap = {'Drug_Central = CentralConc','Drug_Peripheral = PeripheralConc'};
paramsToEstimate = {'log(Central)','log(Peripheral)','Q12','Cl_Central'};
estimatedParam = estimatedInfo(paramsToEstimate,...
'InitialValue',[1 1 1 1],...
'Bounds',[0.1 3;0.1 10;0 10;0.1 2]);
Fit Model
Perform an unpooled fit, that is, one set of estimated parameters for each patient.
unpooledFit = sbiofit(model,gData,responseMap,estimatedParam,dose,'Pooled',false);
Compute Confidence Intervals for Model Predictions
Compute 95% confidence intervals for predicted model responses in the unpooled fit using the Gaussian approximation.
ciPredUnpooled = sbiopredictionci(unpooledFit);
Plot the confidence intervals. If the estimation status of a confidence interval is constrained or not estimable, the function uses the second default color (red). Otherwise, the function uses the first default color (blue). To see the color order, type get(groot,'defaultAxesColorOrder').
Each group is displayed in each column, from left to right, in the same order that they appear in the GroupNames property of the object, which is used to label each column.
plot(ciPredUnpooled)
Plot using a single color (black).
plot(ciPredUnpooled,'Color',[0 0 0])
