YPmodel: A main function of package of model of short-term and...
In YPmodel: The Short-Term and Long-Term Hazard Ratio Model for Survival Data
Description
Usage
Arguments
Value
Author(s)
References
See Also
Examples
Description
The main function to perform parameter estimation and hypothesis testing. The corresponding S4 functions, plot.Y Pmodel and print.Y Pmodel, are also included to demonstrate the results.
Usage
1
2
3
4
5
6
7
8
9
10
Arguments
...
For S4 method only.
data
A properly qualified filename where text data is to be saved, or a dataframe of input data set with three vectors: the event / censoring time (unite: year), the censoring indicator, and the group membership indicator. See the structure of sample data set gastric for instance.
startPoint
Start point for estimating \hat{β}.
nm
Parameter for parameter estimation, to define the upper boundary for the absolute value of \hat{β}.
maxIter1
Parameter of out-cycle iteration numbers.
maxIter2
Parameter of inner-cycle iteration numbers.
repNum
Number of iterations, to be used in the two lack-of-fit tests.
x
A dataframe of results from an YPmodel default process.
object
A dataframe of results from an YPmodel default process, equally to x (different symbol for S4 method only).
Value
An object of class YPmodel, basically a list including elements
Data
A dataframe of source data, generated from input data by YPmodel.inputData.
Estimate
A dataframe of estimation results, including 1) estimation of \hat{β}, 2) its confidential intervals and 3) the odds function of the control group \hat{R}(t,\hat{β}), generated by YPmodel.estimate.
IntervalBands
A dataframe of hazard ratios and related confidential intervals and bands,
generated by YPmodel.IntervalBands.
LackFitTest
A dataframe of the two lack-of-fit tests for the semi-parametric model, generated by YPmodel.lackfittest.
Adlgrk
A dataframe of the two lack-of-fit tests, to test the hypothesis of equal distribution function in the two groups, generated by YPmodel.adlgrk.
Author(s)
Junlong Sun and Song Yang
References
1) YANG, S. AND PRENTICE, R. L. (2010). Improved Logrank-Type Tests for Survival Data Using Adaptive Weights. Biometrics 66, 30-38.
2) YANG, S. AND PRENTICE, R. L. (2005). Semiparametric analysis of short-term and long-term hazard ratios with two-sample survival data. Biometrika 92, 1-17.
3) YANG, S. AND ZHAO, Y. (2012). Checking the Short-Term and Long-Term Hazard Ratio Model for Survival Data. Scandinavian Journal of Statistics.
See Also
YPmodel.estimate,
YPmodel.IntervalBands,
YPmodel.lackfittest,
YPmodel.adlgrk
Examples
1
2
3
4
5
Example output
Warning messages:
1: In ro * x :
Recycling array of length 1 in array-vector arithmetic is deprecated.
Use c() or as.vector() instead.
2: In (t - th2 - ro * x)/sqrt(2 * (1 - ro^2)) :
Recycling array of length 1 in vector-array arithmetic is deprecated.
Use c() or as.vector() instead.
3: In ro * x :
Recycling array of length 1 in array-vector arithmetic is deprecated.
Use c() or as.vector() instead.
4: In (-t - th2 - ro * x)/sqrt(2 * (1 - ro^2)) :
Recycling array of length 1 in vector-array arithmetic is deprecated.
Use c() or as.vector() instead.
Details of model of Yang and Prentice
-------------------------------------------------------------------------------------------------------------
Overall of short-term and long-term hazard ration model
Censoring rate:
0.0889
Total Number:
90
Group Number:
Control Group Treatment Group
Numbers 45 45
-------------------------------------------------------------------------------------------------------------
Parameters of short-term and long-term hazard ration model
Adaptive weight (Beta):
Beta_1 Beta_2
estimates 1.6 -0.906
Hazard ratio:
Theta_1 Theta_2
estimates 4.9541 0.4041
Confidence Interval (Beta):
Lower bound Upper bound
Beta_1 0.5461 2.6543
Beta_2 -1.3947 -0.4173
Confidence Interval (Theta)
Lower bound Upper bound
Theta_1 1.72651 14.2155
Theta_2 0.24791 0.6588
-------------------------------------------------------------------------------------------------------------
Point estimates, Pointwise confidence intervals, and confidence bands of short-term and long-term hazard ration model
Days HR_fun lower.cl upper.cl lower.95%band upper.95%band
[1,] 103.0000 3.1509 5.6625 1.7534 8.0527 1.2329
[2,] 105.0000 2.9014 5.0150 1.6786 6.9670 1.2083
[3,] 108.0000 2.8228 4.7816 1.6664 6.5627 1.2142
[4,] 122.0000 2.7457 4.5654 1.6513 6.1965 1.2166
[5,] 129.0000 2.5513 4.1293 1.5763 5.5144 1.1804
[6,] 144.0000 2.4811 3.9592 1.5548 5.2426 1.1742
[7,] 167.0000 2.4123 3.8014 1.5307 4.9958 1.1648
[8,] 170.0000 2.3448 3.6549 1.5044 4.7717 1.1523
[9,] 182.0000 2.1983 3.3719 1.4332 4.3600 1.1084
[10,] 183.0000 2.1365 3.2522 1.4035 4.1860 1.0904
[11,] 185.0000 2.0759 3.1403 1.3723 4.0268 1.0702
[12,] 193.0000 2.0167 3.0355 1.3398 3.8808 1.0480
[13,] 195.0000 1.9587 2.9371 1.3062 3.7465 1.0240
[14,] 197.0000 1.9020 2.8444 1.2718 3.6225 0.9986
[15,] 208.0000 1.8465 2.7569 1.2368 3.5075 0.9721
[16,] 216.0000 1.7505 2.5936 1.1815 3.2846 0.9329
[17,] 234.0000 1.6992 2.5166 1.1473 3.1863 0.9061
[18,] 235.0000 1.6490 2.4429 1.1131 3.0935 0.8790
[19,] 250.0000 1.5711 2.3148 1.0663 2.9217 0.8448
[20,] 254.0000 1.5245 2.2478 1.0339 2.8384 0.8188
[21,] 262.0000 1.4573 2.1402 0.9923 2.6960 0.7877
[22,] 301.0000 1.3957 2.0443 0.9529 2.5711 0.7577
[23,] 301.0000 1.3392 1.9583 0.9158 2.4605 0.7289
[24,] 307.0000 1.2993 1.9002 0.8884 2.3877 0.7070
[25,] 315.0000 1.2605 1.8437 0.8617 2.3169 0.6857
[26,] 342.0000 1.2138 1.7729 0.8310 2.2260 0.6619
[27,] 354.0000 1.1705 1.7084 0.8019 2.1442 0.6390
[28,] 356.0000 1.1302 1.6494 0.7744 2.0700 0.6171
[29,] 358.0000 1.0925 1.5951 0.7483 2.0023 0.5961
[30,] 380.0000 1.0573 1.5449 0.7236 1.9402 0.5762
[31,] 383.0000 1.0243 1.4983 0.7003 1.8829 0.5573
[32,] 383.0000 0.9933 1.4549 0.6782 1.8298 0.5393
[33,] 388.0000 0.9642 1.4143 0.6573 1.7804 0.5221
[34,] 394.0000 0.9367 1.3763 0.6375 1.7342 0.5059
[35,] 401.0000 0.9087 1.3296 0.6210 1.6712 0.4941
[36,] 408.0000 0.8843 1.2960 0.6033 1.6306 0.4795
[37,] 445.0000 0.8579 1.2515 0.5881 1.5702 0.4688
[38,] 460.0000 0.8362 1.2219 0.5723 1.5346 0.4556
[39,] 464.0000 0.8114 1.1796 0.5582 1.4769 0.4458
[40,] 484.0000 0.7875 1.1389 0.5445 1.4216 0.4363
[41,] 489.0000 0.7694 1.1147 0.5310 1.3928 0.4250
[42,] 499.0000 0.7521 1.0918 0.5181 1.3657 0.4141
[43,] 523.0000 0.7355 1.0700 0.5056 1.3403 0.4036
[44,] 524.0000 0.7197 1.0494 0.4936 1.3162 0.3935
[45,] 528.0000 0.6987 1.0123 0.4822 1.2649 0.3859
[46,] 535.0000 0.6845 0.9943 0.4713 1.2442 0.3766
[47,] 542.0000 0.6648 0.9594 0.4607 1.1960 0.3695
[48,] 562.0000 0.6522 0.9438 0.4507 1.1784 0.3609
[49,] 567.0000 0.6337 0.9112 0.4407 1.1334 0.3543
[50,] 569.0000 0.6224 0.8977 0.4315 1.1187 0.3463
[51,] 577.0000 0.6051 0.8674 0.4221 1.0770 0.3400
[52,] 580.0000 0.5886 0.8390 0.4129 1.0382 0.3337
[53,] 675.0000 0.5793 0.8288 0.4049 1.0278 0.3265
[54,] 676.0000 0.5702 0.8190 0.3970 1.0180 0.3194
[55,] 748.0000 0.5615 0.8097 0.3894 1.0088 0.3125
[56,] 778.0000 0.5530 0.8007 0.3819 1.0002 0.3058
[57,] 786.0000 0.5448 0.7921 0.3746 0.9919 0.2992
[58,] 795.0000 0.5304 0.7675 0.3665 0.9584 0.2935
[59,] 797.0000 0.5231 0.7605 0.3598 0.9522 0.2873
[60,] 855.0000 0.5099 0.7386 0.3520 0.9227 0.2817
[61,] 955.0000 0.5034 0.7330 0.3457 0.9186 0.2759
[62,] 968.0000 0.4971 0.7276 0.3396 0.9147 0.2702
[63,] 1000.0000 0.4910 0.7224 0.3337 0.9111 0.2646
[64,] 1245.0000 0.4850 0.7175 0.3278 0.9078 0.2591
[65,] 1271.0000 0.4791 0.7128 0.3221 0.9048 0.2537
[66,] 1366.0000 0.4677 0.6951 0.3147 0.8818 0.2481
[67,] 1420.0000 0.4627 0.6916 0.3095 0.8806 0.2431
[68,] 1551.0000 0.4577 0.6884 0.3044 0.8797 0.2382
[69,] 1577.0000 0.4477 0.6745 0.2972 0.8628 0.2324
[70,] 1694.0000 0.4436 0.6725 0.2926 0.8635 0.2278
[71,] 2060.0000 0.4349 0.6622 0.2857 0.8524 0.2219
[72,] 2363.0000 0.4315 0.6613 0.2815 0.8548 0.2178
[73,] 2412.0000 0.4315 0.6613 0.2815 0.8548 0.2178
[74,] 2486.0000 0.4315 0.6613 0.2815 0.8548 0.2178
[75,] 2754.0000 0.4315 0.6613 0.2815 0.8548 0.2178
[76,] 2796.0000 0.4315 0.6613 0.2815 0.8548 0.2178
[77,] 2802.0000 0.4315 0.6613 0.2815 0.8548 0.2178
[78,] 2934.0000 0.4315 0.6613 0.2815 0.8548 0.2178
[79,] 2950.0000 0.4315 0.6613 0.2815 0.8548 0.2178
[80,] 2988.0000 0.4315 0.6613 0.2815 0.8548 0.2178
lower.90%band upper.90%band
[1,] 7.2856 1.363
[2,] 6.3454 1.327
[3,] 5.9977 1.329
[4,] 5.6811 1.327
[5,] 5.0791 1.282
[6,] 4.8404 1.272
[7,] 4.6225 1.259
[8,] 4.4234 1.243
[9,] 4.0528 1.192
[10,] 3.8961 1.172
[11,] 3.7520 1.149
[12,] 3.6190 1.124
[13,] 3.4960 1.097
[14,] 3.3818 1.070
[15,] 3.2755 1.041
[16,] 3.0713 0.998
[17,] 2.9796 0.969
[18,] 2.8927 0.940
[19,] 2.7346 0.903
[20,] 2.6562 0.875
[21,] 2.5247 0.841
[22,] 2.4089 0.809
[23,] 2.3059 0.778
[24,] 2.2376 0.754
[25,] 2.1712 0.732
[26,] 2.0865 0.706
[27,] 2.0101 0.682
[28,] 1.9405 0.658
[29,] 1.8770 0.636
[30,] 1.8185 0.615
[31,] 1.7645 0.595
[32,] 1.7143 0.576
[33,] 1.6676 0.557
[34,] 1.6239 0.540
[35,] 1.5660 0.527
[36,] 1.5275 0.512
[37,] 1.4722 0.500
[38,] 1.4384 0.486
[39,] 1.3855 0.475
[40,] 1.3347 0.465
[41,] 1.3073 0.453
[42,] 1.2815 0.441
[43,] 1.2571 0.430
[44,] 1.2341 0.420
[45,] 1.1872 0.411
[46,] 1.1673 0.401
[47,] 1.1233 0.393
[48,] 1.1063 0.384
[49,] 1.0652 0.377
[50,] 1.0508 0.369
[51,] 1.0127 0.362
[52,] 0.9772 0.355
[53,] 0.9668 0.347
[54,] 0.9570 0.340
[55,] 0.9477 0.333
[56,] 0.9389 0.326
[57,] 0.9305 0.319
[58,] 0.8997 0.313
[59,] 0.8933 0.306
[60,] 0.8661 0.300
[61,] 0.8615 0.294
[62,] 0.8571 0.288
[63,] 0.8529 0.283
[64,] 0.8491 0.277
[65,] 0.8455 0.272
[66,] 0.8241 0.265
[67,] 0.8222 0.260
[68,] 0.8204 0.255
[69,] 0.8044 0.249
[70,] 0.8043 0.245
[71,] 0.7934 0.238
[72,] 0.7947 0.234
[73,] 0.7947 0.234
[74,] 0.7947 0.234
[75,] 0.7947 0.234
[76,] 0.7947 0.234
[77,] 0.7947 0.234
[78,] 0.7947 0.234
[79,] 0.7947 0.234
[80,] 0.7947 0.234
-------------------------------------------------------------------------------------------------------------
Lack-of-fit tests for checking short-term and long-term hazard ration model
Adaptive weight (Beta, sample odds function estimator using only the control group data):
Beta_1 Beta_2
estimates 1.712 -0.949
Residual, the martingale residual-based test (p-value):
0.12
Contrast, the contrast-based test (p-value):
0.58
-------------------------------------------------------------------------------------------------------------
Improved Logrank-Type Tests (p-value):
0.0304
-------------------------------------------------------------------------------------------------------------
YPmodel documentation built on Oct. 23, 2020, 5:15 p.m.
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Description Usage Arguments Value Author(s) References See Also Examples
Description
The main function to perform parameter estimation and hypothesis testing. The corresponding S4 functions, plot.Y Pmodel and print.Y Pmodel, are also included to demonstrate the results.
Usage
1 2 3 4 5 6 7 8 9 10 |
Arguments
... |
For S4 method only. |
data |
A properly qualified filename where text data is to be saved, or a dataframe of input data set with three vectors: the event / censoring time (unite: year), the censoring indicator, and the group membership indicator. See the structure of sample data set |
startPoint |
Start point for estimating \hat{β}. |
nm |
Parameter for parameter estimation, to define the upper boundary for the absolute value of \hat{β}. |
maxIter1 |
Parameter of out-cycle iteration numbers. |
maxIter2 |
Parameter of inner-cycle iteration numbers. |
repNum |
Number of iterations, to be used in the two lack-of-fit tests. |
x |
A dataframe of results from an YPmodel default process. |
object |
A dataframe of results from an YPmodel default process, equally to x (different symbol for S4 method only). |
Value
An object of class YPmodel, basically a list including elements
Data |
A dataframe of source data, generated from input data by |
Estimate |
A dataframe of estimation results, including 1) estimation of \hat{β}, 2) its confidential intervals and 3) the odds function of the control group \hat{R}(t,\hat{β}), generated by |
IntervalBands |
A dataframe of hazard ratios and related confidential intervals and bands,
generated by |
LackFitTest |
A dataframe of the two lack-of-fit tests for the semi-parametric model, generated by |
Adlgrk |
A dataframe of the two lack-of-fit tests, to test the hypothesis of equal distribution function in the two groups, generated by |
Author(s)
Junlong Sun and Song Yang
References
1) YANG, S. AND PRENTICE, R. L. (2010). Improved Logrank-Type Tests for Survival Data Using Adaptive Weights. Biometrics 66, 30-38. 2) YANG, S. AND PRENTICE, R. L. (2005). Semiparametric analysis of short-term and long-term hazard ratios with two-sample survival data. Biometrika 92, 1-17. 3) YANG, S. AND ZHAO, Y. (2012). Checking the Short-Term and Long-Term Hazard Ratio Model for Survival Data. Scandinavian Journal of Statistics.
See Also
YPmodel.estimate,
YPmodel.IntervalBands,
YPmodel.lackfittest,
YPmodel.adlgrk
Examples
1 2 3 4 5 |
Example output
Warning messages:
1: In ro * x :
Recycling array of length 1 in array-vector arithmetic is deprecated.
Use c() or as.vector() instead.
2: In (t - th2 - ro * x)/sqrt(2 * (1 - ro^2)) :
Recycling array of length 1 in vector-array arithmetic is deprecated.
Use c() or as.vector() instead.
3: In ro * x :
Recycling array of length 1 in array-vector arithmetic is deprecated.
Use c() or as.vector() instead.
4: In (-t - th2 - ro * x)/sqrt(2 * (1 - ro^2)) :
Recycling array of length 1 in vector-array arithmetic is deprecated.
Use c() or as.vector() instead.
Details of model of Yang and Prentice
-------------------------------------------------------------------------------------------------------------
Overall of short-term and long-term hazard ration model
Censoring rate:
0.0889
Total Number:
90
Group Number:
Control Group Treatment Group
Numbers 45 45
-------------------------------------------------------------------------------------------------------------
Parameters of short-term and long-term hazard ration model
Adaptive weight (Beta):
Beta_1 Beta_2
estimates 1.6 -0.906
Hazard ratio:
Theta_1 Theta_2
estimates 4.9541 0.4041
Confidence Interval (Beta):
Lower bound Upper bound
Beta_1 0.5461 2.6543
Beta_2 -1.3947 -0.4173
Confidence Interval (Theta)
Lower bound Upper bound
Theta_1 1.72651 14.2155
Theta_2 0.24791 0.6588
-------------------------------------------------------------------------------------------------------------
Point estimates, Pointwise confidence intervals, and confidence bands of short-term and long-term hazard ration model
Days HR_fun lower.cl upper.cl lower.95%band upper.95%band
[1,] 103.0000 3.1509 5.6625 1.7534 8.0527 1.2329
[2,] 105.0000 2.9014 5.0150 1.6786 6.9670 1.2083
[3,] 108.0000 2.8228 4.7816 1.6664 6.5627 1.2142
[4,] 122.0000 2.7457 4.5654 1.6513 6.1965 1.2166
[5,] 129.0000 2.5513 4.1293 1.5763 5.5144 1.1804
[6,] 144.0000 2.4811 3.9592 1.5548 5.2426 1.1742
[7,] 167.0000 2.4123 3.8014 1.5307 4.9958 1.1648
[8,] 170.0000 2.3448 3.6549 1.5044 4.7717 1.1523
[9,] 182.0000 2.1983 3.3719 1.4332 4.3600 1.1084
[10,] 183.0000 2.1365 3.2522 1.4035 4.1860 1.0904
[11,] 185.0000 2.0759 3.1403 1.3723 4.0268 1.0702
[12,] 193.0000 2.0167 3.0355 1.3398 3.8808 1.0480
[13,] 195.0000 1.9587 2.9371 1.3062 3.7465 1.0240
[14,] 197.0000 1.9020 2.8444 1.2718 3.6225 0.9986
[15,] 208.0000 1.8465 2.7569 1.2368 3.5075 0.9721
[16,] 216.0000 1.7505 2.5936 1.1815 3.2846 0.9329
[17,] 234.0000 1.6992 2.5166 1.1473 3.1863 0.9061
[18,] 235.0000 1.6490 2.4429 1.1131 3.0935 0.8790
[19,] 250.0000 1.5711 2.3148 1.0663 2.9217 0.8448
[20,] 254.0000 1.5245 2.2478 1.0339 2.8384 0.8188
[21,] 262.0000 1.4573 2.1402 0.9923 2.6960 0.7877
[22,] 301.0000 1.3957 2.0443 0.9529 2.5711 0.7577
[23,] 301.0000 1.3392 1.9583 0.9158 2.4605 0.7289
[24,] 307.0000 1.2993 1.9002 0.8884 2.3877 0.7070
[25,] 315.0000 1.2605 1.8437 0.8617 2.3169 0.6857
[26,] 342.0000 1.2138 1.7729 0.8310 2.2260 0.6619
[27,] 354.0000 1.1705 1.7084 0.8019 2.1442 0.6390
[28,] 356.0000 1.1302 1.6494 0.7744 2.0700 0.6171
[29,] 358.0000 1.0925 1.5951 0.7483 2.0023 0.5961
[30,] 380.0000 1.0573 1.5449 0.7236 1.9402 0.5762
[31,] 383.0000 1.0243 1.4983 0.7003 1.8829 0.5573
[32,] 383.0000 0.9933 1.4549 0.6782 1.8298 0.5393
[33,] 388.0000 0.9642 1.4143 0.6573 1.7804 0.5221
[34,] 394.0000 0.9367 1.3763 0.6375 1.7342 0.5059
[35,] 401.0000 0.9087 1.3296 0.6210 1.6712 0.4941
[36,] 408.0000 0.8843 1.2960 0.6033 1.6306 0.4795
[37,] 445.0000 0.8579 1.2515 0.5881 1.5702 0.4688
[38,] 460.0000 0.8362 1.2219 0.5723 1.5346 0.4556
[39,] 464.0000 0.8114 1.1796 0.5582 1.4769 0.4458
[40,] 484.0000 0.7875 1.1389 0.5445 1.4216 0.4363
[41,] 489.0000 0.7694 1.1147 0.5310 1.3928 0.4250
[42,] 499.0000 0.7521 1.0918 0.5181 1.3657 0.4141
[43,] 523.0000 0.7355 1.0700 0.5056 1.3403 0.4036
[44,] 524.0000 0.7197 1.0494 0.4936 1.3162 0.3935
[45,] 528.0000 0.6987 1.0123 0.4822 1.2649 0.3859
[46,] 535.0000 0.6845 0.9943 0.4713 1.2442 0.3766
[47,] 542.0000 0.6648 0.9594 0.4607 1.1960 0.3695
[48,] 562.0000 0.6522 0.9438 0.4507 1.1784 0.3609
[49,] 567.0000 0.6337 0.9112 0.4407 1.1334 0.3543
[50,] 569.0000 0.6224 0.8977 0.4315 1.1187 0.3463
[51,] 577.0000 0.6051 0.8674 0.4221 1.0770 0.3400
[52,] 580.0000 0.5886 0.8390 0.4129 1.0382 0.3337
[53,] 675.0000 0.5793 0.8288 0.4049 1.0278 0.3265
[54,] 676.0000 0.5702 0.8190 0.3970 1.0180 0.3194
[55,] 748.0000 0.5615 0.8097 0.3894 1.0088 0.3125
[56,] 778.0000 0.5530 0.8007 0.3819 1.0002 0.3058
[57,] 786.0000 0.5448 0.7921 0.3746 0.9919 0.2992
[58,] 795.0000 0.5304 0.7675 0.3665 0.9584 0.2935
[59,] 797.0000 0.5231 0.7605 0.3598 0.9522 0.2873
[60,] 855.0000 0.5099 0.7386 0.3520 0.9227 0.2817
[61,] 955.0000 0.5034 0.7330 0.3457 0.9186 0.2759
[62,] 968.0000 0.4971 0.7276 0.3396 0.9147 0.2702
[63,] 1000.0000 0.4910 0.7224 0.3337 0.9111 0.2646
[64,] 1245.0000 0.4850 0.7175 0.3278 0.9078 0.2591
[65,] 1271.0000 0.4791 0.7128 0.3221 0.9048 0.2537
[66,] 1366.0000 0.4677 0.6951 0.3147 0.8818 0.2481
[67,] 1420.0000 0.4627 0.6916 0.3095 0.8806 0.2431
[68,] 1551.0000 0.4577 0.6884 0.3044 0.8797 0.2382
[69,] 1577.0000 0.4477 0.6745 0.2972 0.8628 0.2324
[70,] 1694.0000 0.4436 0.6725 0.2926 0.8635 0.2278
[71,] 2060.0000 0.4349 0.6622 0.2857 0.8524 0.2219
[72,] 2363.0000 0.4315 0.6613 0.2815 0.8548 0.2178
[73,] 2412.0000 0.4315 0.6613 0.2815 0.8548 0.2178
[74,] 2486.0000 0.4315 0.6613 0.2815 0.8548 0.2178
[75,] 2754.0000 0.4315 0.6613 0.2815 0.8548 0.2178
[76,] 2796.0000 0.4315 0.6613 0.2815 0.8548 0.2178
[77,] 2802.0000 0.4315 0.6613 0.2815 0.8548 0.2178
[78,] 2934.0000 0.4315 0.6613 0.2815 0.8548 0.2178
[79,] 2950.0000 0.4315 0.6613 0.2815 0.8548 0.2178
[80,] 2988.0000 0.4315 0.6613 0.2815 0.8548 0.2178
lower.90%band upper.90%band
[1,] 7.2856 1.363
[2,] 6.3454 1.327
[3,] 5.9977 1.329
[4,] 5.6811 1.327
[5,] 5.0791 1.282
[6,] 4.8404 1.272
[7,] 4.6225 1.259
[8,] 4.4234 1.243
[9,] 4.0528 1.192
[10,] 3.8961 1.172
[11,] 3.7520 1.149
[12,] 3.6190 1.124
[13,] 3.4960 1.097
[14,] 3.3818 1.070
[15,] 3.2755 1.041
[16,] 3.0713 0.998
[17,] 2.9796 0.969
[18,] 2.8927 0.940
[19,] 2.7346 0.903
[20,] 2.6562 0.875
[21,] 2.5247 0.841
[22,] 2.4089 0.809
[23,] 2.3059 0.778
[24,] 2.2376 0.754
[25,] 2.1712 0.732
[26,] 2.0865 0.706
[27,] 2.0101 0.682
[28,] 1.9405 0.658
[29,] 1.8770 0.636
[30,] 1.8185 0.615
[31,] 1.7645 0.595
[32,] 1.7143 0.576
[33,] 1.6676 0.557
[34,] 1.6239 0.540
[35,] 1.5660 0.527
[36,] 1.5275 0.512
[37,] 1.4722 0.500
[38,] 1.4384 0.486
[39,] 1.3855 0.475
[40,] 1.3347 0.465
[41,] 1.3073 0.453
[42,] 1.2815 0.441
[43,] 1.2571 0.430
[44,] 1.2341 0.420
[45,] 1.1872 0.411
[46,] 1.1673 0.401
[47,] 1.1233 0.393
[48,] 1.1063 0.384
[49,] 1.0652 0.377
[50,] 1.0508 0.369
[51,] 1.0127 0.362
[52,] 0.9772 0.355
[53,] 0.9668 0.347
[54,] 0.9570 0.340
[55,] 0.9477 0.333
[56,] 0.9389 0.326
[57,] 0.9305 0.319
[58,] 0.8997 0.313
[59,] 0.8933 0.306
[60,] 0.8661 0.300
[61,] 0.8615 0.294
[62,] 0.8571 0.288
[63,] 0.8529 0.283
[64,] 0.8491 0.277
[65,] 0.8455 0.272
[66,] 0.8241 0.265
[67,] 0.8222 0.260
[68,] 0.8204 0.255
[69,] 0.8044 0.249
[70,] 0.8043 0.245
[71,] 0.7934 0.238
[72,] 0.7947 0.234
[73,] 0.7947 0.234
[74,] 0.7947 0.234
[75,] 0.7947 0.234
[76,] 0.7947 0.234
[77,] 0.7947 0.234
[78,] 0.7947 0.234
[79,] 0.7947 0.234
[80,] 0.7947 0.234
-------------------------------------------------------------------------------------------------------------
Lack-of-fit tests for checking short-term and long-term hazard ration model
Adaptive weight (Beta, sample odds function estimator using only the control group data):
Beta_1 Beta_2
estimates 1.712 -0.949
Residual, the martingale residual-based test (p-value):
0.12
Contrast, the contrast-based test (p-value):
0.58
-------------------------------------------------------------------------------------------------------------
Improved Logrank-Type Tests (p-value):
0.0304
-------------------------------------------------------------------------------------------------------------
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