/*
* This file is part of ADDIS (Aggregate Data Drug Information System).
* ADDIS is distributed from http://drugis.org/.
* Copyright © 2009 Gert van Valkenhoef, Tommi Tervonen.
* Copyright © 2010 Gert van Valkenhoef, Tommi Tervonen, Tijs Zwinkels,
* Maarten Jacobs, Hanno Koeslag, Florin Schimbinschi, Ahmad Kamal, Daniel
* Reid.
* Copyright © 2011 Gert van Valkenhoef, Ahmad Kamal, Daniel Reid, Florin
* Schimbinschi.
* Copyright © 2012 Gert van Valkenhoef, Daniel Reid, Joël Kuiper, Wouter
* Reckman.
* Copyright © 2013 Gert van Valkenhoef, Joël Kuiper.
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
package org.drugis.addis.util.JSMAAintegration;
import java.util.Collections;
import java.util.List;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.ArrayRealVector;
import org.drugis.addis.entities.ContinuousVariableType;
import org.drugis.addis.entities.OutcomeMeasure;
import org.drugis.addis.entities.RateVariableType;
import org.drugis.addis.entities.analysis.MetaBenefitRiskAnalysis;
import org.drugis.addis.entities.treatment.TreatmentDefinition;
import org.drugis.mtc.summary.MultivariateNormalSummary;
import fi.smaa.jsmaa.model.Alternative;
import fi.smaa.jsmaa.model.Criterion;
import fi.smaa.jsmaa.model.CriterionMeasurement;
import fi.smaa.jsmaa.model.GaussianMeasurement;
import fi.smaa.jsmaa.model.MultivariateGaussianCriterionMeasurement;
import fi.smaa.jsmaa.model.PerCriterionMeasurements;
import fi.smaa.jsmaa.model.RelativeGaussianCriterionMeasurement;
import fi.smaa.jsmaa.model.RelativeLogitGaussianCriterionMeasurement;
import fi.smaa.jsmaa.model.SMAAModel;
public class MetaBenefitRiskSMAAFactory extends AbstractBenefitRiskSMAAFactory<TreatmentDefinition> {
private final MetaBenefitRiskAnalysis d_brAnalysis;
public MetaBenefitRiskSMAAFactory(MetaBenefitRiskAnalysis brAnalysis) {
d_brAnalysis = brAnalysis;
}
public SMAAModel createSMAAModel() {
PerCriterionMeasurements measurements = new PerCriterionMeasurements(Collections.<Criterion> emptyList(), Collections.<Alternative> emptyList());
SMAAModel smaaModel = new SMAAModel(d_brAnalysis.getName(), measurements);
addCriteriaAndAlternatives(smaaModel, d_brAnalysis);
for(OutcomeMeasure om : d_brAnalysis.getCriteria()) {
CriterionMeasurement m = createMeasurement(smaaModel.getAlternatives(), om);
measurements.setCriterionMeasurement(getCriterion(om), m);
}
return smaaModel;
}
private CriterionMeasurement createMeasurement(List<Alternative> alts, OutcomeMeasure om) {
GaussianMeasurement baseline = new GaussianMeasurement(
d_brAnalysis.getBaselineDistribution(om).getMu(),
d_brAnalysis.getBaselineDistribution(om).getSigma());
MultivariateNormalSummary reSummary = d_brAnalysis.getRelativeEffectsSummary(om);
MultivariateGaussianCriterionMeasurement delta = new MultivariateGaussianCriterionMeasurement(alts);
double[] meanVector = createMeanVector(d_brAnalysis.getAlternatives(), d_brAnalysis.getBaseline(), reSummary.getMeanVector());
double[][] covMatrix = createCovarianceMatrix(d_brAnalysis.getAlternatives(), d_brAnalysis.getBaseline(), reSummary.getCovarianceMatrix());
delta.setMeanVector(new ArrayRealVector(meanVector));
delta.setCovarianceMatrix(new Array2DRowRealMatrix(covMatrix));
RelativeGaussianCriterionMeasurement relative = new RelativeGaussianCriterionMeasurement(delta, baseline);
CriterionMeasurement m = null;
if (om.getVariableType() instanceof RateVariableType) {
m = new RelativeLogitGaussianCriterionMeasurement(relative);
} else if (om.getVariableType() instanceof ContinuousVariableType) {
m = relative;
}
return m;
}
/**
* Add the baseline to a covariance matrix that excludes the baseline by inserting an all-zero row and column at the baseline's index.
* @param alternatives Ordered list of alternatives.
* @param baseline The baseline.
* @param covarianceMatrix A covariance matrix for all alternatives except the baseline.
* @return A covariance matrix for all alternatives including the baseline.
*/
static double[][] createCovarianceMatrix(List<TreatmentDefinition> alternatives, TreatmentDefinition baseline, double[][] covarianceMatrix) {
final int n = alternatives.size();
final int b = alternatives.indexOf(baseline);
double newCovMatrix[][] = new double[n][n];
for (int i = 0; i < n; ++i) {
for (int j = 0; j < n; ++j) {
if (i == b || j == b) {
newCovMatrix[i][j] = 0.0;
} else {
newCovMatrix[i][j] = covarianceMatrix[i < b ? i : i - 1][j < b ? j : j - 1];
}
}
}
return newCovMatrix;
}
/**
* Add the baseline to a mean vector that excludes the baseline by inserting a zero at the baseline's index.
* @param alternatives Ordered list of alternatives.
* @param baseline The baseline.
* @param covarianceMatrix A mean vector for all alternatives except the baseline.
* @return A mean vector for all alternatives including the baseline.
*/
static double[] createMeanVector(List<TreatmentDefinition> alternatives, TreatmentDefinition baseline, double[] meanVector) {
final int n = alternatives.size();
final int b = alternatives.indexOf(baseline);
double[] newMeanVector = new double[n];
for (int i = 0; i < n; ++i) {
if (i == b) {
newMeanVector[i] = 0.0;
} else {
newMeanVector[i] = meanVector[i < b ? i : i - 1];
}
}
return newMeanVector;
}
@Override
protected Alternative createAlternative(TreatmentDefinition a) {
return new Alternative(a.getLabel());
}
}