import static java.lang.System.out;

import java.util.*;

import javastat.*;

import javastat.survival.*;

import static javastat.util.Argument.*;

import static javastat.util.Output.*;

import javastat.util.*;

/**

*

* <p>Example: class KaplanMeierEstimate.</p>

* <p>Data Source: Collett, D. (1994). Modelling Survival Data in Medical

* Research. New York: Chapman and Hall, pp. 290-291. </p>

*/

public class KaplanMeierEstimateExample

{

public static void main(String arg[])

{

double[] time1 = {156, 1040, 59, 421, 329, 769, 365, 770, 1227, 268,

475, 1129, 464, 1206, 638, 563, 1106, 431, 855, 803,

115, 744, 477, 448, 353, 377};

double[] censor1 = {1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0,

1, 0, 0, 1, 0, 0, 0, 1, 0};

double[][] covariate1 = { {1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1,

2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2},

{66, 38, 72, 53, 43, 59, 64, 57, 59, 74, 59,

53, 56, 44, 56, 55, 44, 50, 43, 39, 74, 50,

64, 56, 63, 58} };

DataManager dm = new DataManager();

KaplanMeierEstimate testclass1 =

new KaplanMeierEstimate(0.05, time1, censor1);

double[] estimate1 = testclass1.estimate;

double[] variance = testclass1.variance;

double[][] confidenceInterval = new double[time1.length][2];

for (int i = 0; i < confidenceInterval.length; i++)

{

confidenceInterval[i] = testclass1.confidenceInterval[i];

}

KaplanMeierEstimate testclass2 = new KaplanMeierEstimate();

confidenceInterval =

testclass2.confidenceInterval(0.05, time1, censor1);

estimate1 = testclass2.estimate(time1, censor1);

variance = testclass2.variance(time1, censor1);

double[] estimate2 = testclass2.estimate(time1, censor1, covariate1);

double[] mv = new BasicStatistics().meanVector(covariate1);

double power = 1.0;

for (int i = 0; i < covariate1.length; i++)

{

power *= Math.exp(mv[i] * testclass2.coefficients[i]);

}

double[] estimate2Prop = new double[estimate2.length];

for (int i = 0; i < estimate2.length; i++)

{

estimate2Prop[i] = Math.pow(estimate2[i], power);

}

confidenceInterval = testclass2.confidenceInterval(time1, censor1);

KaplanMeierEstimate testclass3 =

new KaplanMeierEstimate(time1, censor1, covariate1);

double[] estimate3 = testclass3.estimate;

power = 1.0;

for (int i = 0; i < covariate1.length; i++)

{

power *= Math.exp(covariate1[i][5] * testclass3.coefficients[i]);

}

double[] estimate3Prop = new double[testclass3.estimate.length];

for (int i = 0; i < estimate3Prop.length; i++)

{

estimate3Prop[i] = Math.pow(estimate3[i], power);

}

Hashtable argument1 = new Hashtable();

argument1.put(ALPHA, 0.05);

StatisticalAnalysis testclass4 = new KaplanMeierEstimate(argument1,

time1, censor1).statisticalAnalysis;

estimate1 = (double[]) testclass4.output.get(SURVIVAL_ESTIMATE);

variance = (double[]) testclass4.output.

get(SURVIVAL_ESTIMATE_VARIANCE);

confidenceInterval =

(double[][]) testclass4.output.get(CONFIDENCE_INTERVAL);

Hashtable argument2 = new Hashtable();

KaplanMeierEstimate testclass5 =

new KaplanMeierEstimate(argument2, null);

argument2.put(ALPHA, 0.05);

confidenceInterval =

testclass5.confidenceInterval(argument2, time1, censor1);

estimate1 = testclass5.estimate(argument2, time1, censor1);

variance = testclass5.variance(argument2, time1, censor1);

estimate2 = testclass5.estimate(argument2, time1, censor1, covariate1);

mv = new BasicStatistics().meanVector(covariate1);

power = 1.0;

for (int i = 0; i < covariate1.length; i++)

{

power *= Math.exp(mv[i] * testclass5.coefficients[i]);

}

estimate2Prop = new double[estimate2.length];

Hashtable argument3 = new Hashtable();

StatisticalAnalysis testclass6 = new KaplanMeierEstimate(argument3,

time1, censor1, covariate1).statisticalAnalysis;

estimate3 = (double[]) testclass6.output.get(SURVIVAL_ESTIMATE);

double[] coefficients =

(double[]) testclass6.output.get(COEFFICIENTS);

power = 1.0;

for (int i = 0; i < covariate1.length; i++)

{

power *= Math.exp(covariate1[i][5] * coefficients[i]);

}

estimate3Prop = new double[estimate3.length];

for (int i = 0; i < estimate3Prop.length; i++)

{

estimate3Prop[i] = Math.pow(estimate3[i], power);

}

}

}

Results:

The estimate for the 1'th observation based on non-null constructor = 0.885

The variance of the estimate of the 1'th observation based on non-null constructor

= 0.0040

The confidence interval corresponding to the 1'th observation based on non-null constructor

= [0.762 , 1.0]

The estimate for the 2'th observation based on non-null constructor = 0.497

The variance of the estimate of the 2'th observation based on non-null constructor

= 0.011

The confidence interval corresponding to the 2'th observation based on non-null constructor

= [0.291 , 0.703]

…

The estimate for the 26'th observation based on non-null constructor = 0.731

The variance of the estimate of the 26'th observation based on non-null constructor

= 0.0080

The confidence interval corresponding to the 26'th observation based on non-null constructor

= [0.56 , 0.901]

{CONFIDENCE_INTERVAL=[[D@157f0dc,

SURVIVAL_ESTIMATE=[D@863399,

SURVIVAL_ESTIMATE_VARIANCE=[D@a59698}

The estimate for the 1'th observation based on null constructor = 0.885

The variance of the estimate of the 1'th observation based on null constructor

= 0.0040

The confidence interval corresponding to the 1'th observation based on null constructor

= [0.762 , 1.0]

The estimate for the 2'th observation based on null constructor = 0.497

The variance of the estimate of the 2'th observation based on null constructor

= 0.011

The confidence interval corresponding to the 2'th observation based on null constructor

= [0.291 , 0.703]

…

The estimate for the 26'th observation based on null constructor = 0.731

The variance of the estimate of the 26'th observation based on null constructor

= 0.0080

The confidence interval corresponding to the 26'th observation based on null constructor

= [0.56 , 0.901]

The estimate for the 1'th observation based on null constructor = 0.956

The estimate for the 2'th observation based on null constructor = 0.476

…

The estimate for the 26'th observation based on null constructor = 0.808

{CONFIDENCE_INTERVAL=[[D@141d683,

SURVIVAL_ESTIMATE=[D@16a55fa, COEFFICIENTS=[D@32c41a,

SURVIVAL_ESTIMATE_VARIANCE=[D@e89b94}

The estimate for the 1'th observation under proportional hazards assumption

= 0.952

The estimate for the 2'th observation under proportional hazards assumption

= 0.447

…

The estimate for the 26'th observation under proportional hazards assumption

= 0.793

{SURVIVAL_ESTIMATE=[D@13e205f, COEFFICIENTS=[D@1bf73fa}