%include 'ice.sas'; title1 "Example 1 NR Original"; title2 " 0 2 1, 1 3 1, 2 10 4, 4 10 4"; %ice(data=ex1,time=(l r),freq=f); Example 1 NR Original 1 0 2 1, 1 3 1, 2 10 4, 4 10 4 Nonparametric Survival Curve for Interval Censoring Number of Observations: 4 Number of Parameters: 3 Optimization Technique: Newton Raphson Ridge Parameter Estimates Q P THETA 1 2 0.1999995 2 3 0.0000010 4 10 0.7999995 Survival Curve Estimates and 95% Confidence Intervals LEFT RIGHT ESTIMATE LOWER UPPER 0 1 1.0000 . . 2 2 0.8000 0.4494 1.0000 3 4 0.8000 0.4494 1.0000 10 10 0.0000 . . Example 1A NR Original 2 0 2.1 1, 1 3 1, 2 10 4, 4 10 4 Nonparametric Survival Curve for Interval Censoring Number of Observations: 4 Number of Parameters: 2 Optimization Technique: Newton Raphson Ridge Parameter Estimates Q P THETA 2 2.1 0.3333333 4 10 0.6666667 Survival Curve Estimates and 95% Confidence Intervals LEFT RIGHT ESTIMATE LOWER UPPER 0 2 1.0000 . . 2.1 4 0.6667 0.2895 1.0000 10 10 0.0000 . . Example 2 NR Original 3 1 1.1,2 3,3 4 Nonparametric Survival Curve for Interval Censoring Number of Observations: 3 Number of Parameters: 3 Optimization Technique: Newton Raphson Ridge Parameter Estimates Q P THETA 1 1.1 0.3333333 2 3 0.3333333 3 4 0.3333333 Survival Curve Estimates and 95% Confidence Intervals LEFT RIGHT ESTIMATE LOWER UPPER 0 1 1.0000 . . 1.1 2 0.6667 0.1332 1.0000 3 3 0.3333 0.0000 0.8668 4 4 0.0000 . . Example 2 EM Original 4 1 1.1,2 3,3 4 LL0 -3.295837 ITER LL DIFCRIT 1 -3.2958368660 0.0000000000 Nonparametric Survival Curve for Interval Censoring Number of Observations: 3 Number of Parameters: 3 Optimization Technique: Self-Consistency Algorithm Parameter Estimates Q P THETA 1 1.1 0.3333333 2 3 0.3333333 3 4 0.3333333 Survival Curve Estimates and 95% Confidence Intervals LEFT RIGHT ESTIMATE LOWER UPPER 0 1 1.0000 . . 1.1 2 0.6667 0.1332 1.0000 3 3 0.3333 0.0000 0.8668 4 4 0.0000 . . Example 3 NR Original 5 0 2,2 3,3 4 Nonparametric Survival Curve for Interval Censoring Number of Observations: 3 Number of Parameters: 3 Optimization Technique: Newton Raphson Ridge Parameter Estimates Q P THETA 0 2 0.3333333 2 3 0.3333333 3 4 0.3333333 Survival Curve Estimates and 95% Confidence Intervals LEFT RIGHT ESTIMATE LOWER UPPER 0 0 1.0000 . . 2 2 0.6667 0.1332 1.0000 3 3 0.3333 0.0000 0.8668 4 4 0.0000 . . Example 4 NR Original 6 2 2.1,3 3.1,5 5.1 Nonparametric Survival Curve for Interval Censoring Number of Observations: 3 Number of Parameters: 3 Optimization Technique: Newton Raphson Ridge Parameter Estimates Q P THETA 2 2.1 0.3333333 3 3.1 0.3333333 5 5.1 0.3333333 Survival Curve Estimates and 95% Confidence Intervals LEFT RIGHT ESTIMATE LOWER UPPER 0 2 1.0000 . . 2.1 3 0.6667 0.1332 1.0000 3.1 5 0.3333 0.0000 0.8668 5.1 5.1 0.0000 . . Example 5 NR Original 7 2 2.1,5 13,5 5.1,2 6,7 9,9 10,10 10.1,11 18,6 15,13 13.1 Nonparametric Survival Curve for Interval Censoring Number of Observations: 10 Number of Parameters: 7 Optimization Technique: Newton Raphson Ridge Parameter Estimates Q P THETA 2 2.1 0.1434748 5 5.1 0.1865431 7 9 0.1449643 9 10 0.1449643 10 10.1 0.1449643 11 13 0.0000010 13 13.1 0.2350882 Survival Curve Estimates and 95% Confidence Intervals LEFT RIGHT ESTIMATE LOWER UPPER 0 2 1.0000 . . 2.1 5 0.8565 0.6088 1.0000 5.1 7 0.6700 0.3530 0.9870 9 9 0.5250 0.1585 0.8916 10 10 0.3801 0.0000 0.7620 10.1 11 0.2351 0.0000 0.6026 13 13 0.2351 0.0000 0.6672 13.1 13.1 0.0000 . .