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2025-07-30 17:09:11 +03:00
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Group,T,Status
ALL,2081,0
ALL,1602,0
ALL,1496,0
ALL,1462,0
ALL,1433,0
ALL,1377,0
ALL,1330,0
ALL,996,0
ALL,226,0
ALL,1199,0
ALL,1111,0
ALL,530,0
ALL,1182,0
ALL,1167,0
ALL,418,1
ALL,383,1
ALL,276,1
ALL,104,1
ALL,609,1
ALL,172,1
ALL,487,1
ALL,662,1
ALL,194,1
ALL,230,1
ALL,526,1
ALL,122,1
ALL,129,1
ALL,74,1
ALL,122,1
ALL,86,1
ALL,466,1
ALL,192,1
ALL,109,1
ALL,55,1
ALL,1,1
ALL,107,1
ALL,110,1
ALL,332,1
AML-Low Risk,2569,0
AML-Low Risk,2506,0
AML-Low Risk,2409,0
AML-Low Risk,2218,0
AML-Low Risk,1857,0
AML-Low Risk,1829,0
AML-Low Risk,1562,0
AML-Low Risk,1470,0
AML-Low Risk,1363,0
AML-Low Risk,1030,0
AML-Low Risk,860,0
AML-Low Risk,1258,0
AML-Low Risk,2246,0
AML-Low Risk,1870,0
AML-Low Risk,1799,0
AML-Low Risk,1709,0
AML-Low Risk,1674,0
AML-Low Risk,1568,0
AML-Low Risk,1527,0
AML-Low Risk,1324,0
AML-Low Risk,957,0
AML-Low Risk,932,0
AML-Low Risk,847,0
AML-Low Risk,848,0
AML-Low Risk,1850,0
AML-Low Risk,1843,0
AML-Low Risk,1535,0
AML-Low Risk,1447,0
AML-Low Risk,1384,0
AML-Low Risk,414,1
AML-Low Risk,2204,1
AML-Low Risk,1063,1
AML-Low Risk,481,1
AML-Low Risk,105,1
AML-Low Risk,641,1
AML-Low Risk,390,1
AML-Low Risk,288,1
AML-Low Risk,421,1
AML-Low Risk,79,1
AML-Low Risk,748,1
AML-Low Risk,486,1
AML-Low Risk,48,1
AML-Low Risk,272,1
AML-Low Risk,1074,1
AML-Low Risk,381,1
AML-Low Risk,10,1
AML-Low Risk,53,1
AML-Low Risk,80,1
AML-Low Risk,35,1
AML-Low Risk,248,1
AML-Low Risk,704,1
AML-Low Risk,211,1
AML-Low Risk,219,1
AML-Low Risk,606,1
AML-High Risk,2640,0
AML-High Risk,2430,0
AML-High Risk,2252,0
AML-High Risk,2140,0
AML-High Risk,2133,0
AML-High Risk,1238,0
AML-High Risk,1631,0
AML-High Risk,2024,0
AML-High Risk,1345,0
AML-High Risk,1136,0
AML-High Risk,845,0
AML-High Risk,422,1
AML-High Risk,162,1
AML-High Risk,84,1
AML-High Risk,100,1
AML-High Risk,2,1
AML-High Risk,47,1
AML-High Risk,242,1
AML-High Risk,456,1
AML-High Risk,268,1
AML-High Risk,318,1
AML-High Risk,32,1
AML-High Risk,467,1
AML-High Risk,47,1
AML-High Risk,390,1
AML-High Risk,183,1
AML-High Risk,105,1
AML-High Risk,115,1
AML-High Risk,164,1
AML-High Risk,93,1
AML-High Risk,120,1
AML-High Risk,80,1
AML-High Risk,677,1
AML-High Risk,64,1
AML-High Risk,168,1
AML-High Risk,74,1
AML-High Risk,16,1
AML-High Risk,157,1
AML-High Risk,625,1
AML-High Risk,48,1
AML-High Risk,273,1
AML-High Risk,63,1
AML-High Risk,76,1
AML-High Risk,113,1
AML-High Risk,363,1
1 Group T Status
2 ALL 2081 0
3 ALL 1602 0
4 ALL 1496 0
5 ALL 1462 0
6 ALL 1433 0
7 ALL 1377 0
8 ALL 1330 0
9 ALL 996 0
10 ALL 226 0
11 ALL 1199 0
12 ALL 1111 0
13 ALL 530 0
14 ALL 1182 0
15 ALL 1167 0
16 ALL 418 1
17 ALL 383 1
18 ALL 276 1
19 ALL 104 1
20 ALL 609 1
21 ALL 172 1
22 ALL 487 1
23 ALL 662 1
24 ALL 194 1
25 ALL 230 1
26 ALL 526 1
27 ALL 122 1
28 ALL 129 1
29 ALL 74 1
30 ALL 122 1
31 ALL 86 1
32 ALL 466 1
33 ALL 192 1
34 ALL 109 1
35 ALL 55 1
36 ALL 1 1
37 ALL 107 1
38 ALL 110 1
39 ALL 332 1
40 AML-Low Risk 2569 0
41 AML-Low Risk 2506 0
42 AML-Low Risk 2409 0
43 AML-Low Risk 2218 0
44 AML-Low Risk 1857 0
45 AML-Low Risk 1829 0
46 AML-Low Risk 1562 0
47 AML-Low Risk 1470 0
48 AML-Low Risk 1363 0
49 AML-Low Risk 1030 0
50 AML-Low Risk 860 0
51 AML-Low Risk 1258 0
52 AML-Low Risk 2246 0
53 AML-Low Risk 1870 0
54 AML-Low Risk 1799 0
55 AML-Low Risk 1709 0
56 AML-Low Risk 1674 0
57 AML-Low Risk 1568 0
58 AML-Low Risk 1527 0
59 AML-Low Risk 1324 0
60 AML-Low Risk 957 0
61 AML-Low Risk 932 0
62 AML-Low Risk 847 0
63 AML-Low Risk 848 0
64 AML-Low Risk 1850 0
65 AML-Low Risk 1843 0
66 AML-Low Risk 1535 0
67 AML-Low Risk 1447 0
68 AML-Low Risk 1384 0
69 AML-Low Risk 414 1
70 AML-Low Risk 2204 1
71 AML-Low Risk 1063 1
72 AML-Low Risk 481 1
73 AML-Low Risk 105 1
74 AML-Low Risk 641 1
75 AML-Low Risk 390 1
76 AML-Low Risk 288 1
77 AML-Low Risk 421 1
78 AML-Low Risk 79 1
79 AML-Low Risk 748 1
80 AML-Low Risk 486 1
81 AML-Low Risk 48 1
82 AML-Low Risk 272 1
83 AML-Low Risk 1074 1
84 AML-Low Risk 381 1
85 AML-Low Risk 10 1
86 AML-Low Risk 53 1
87 AML-Low Risk 80 1
88 AML-Low Risk 35 1
89 AML-Low Risk 248 1
90 AML-Low Risk 704 1
91 AML-Low Risk 211 1
92 AML-Low Risk 219 1
93 AML-Low Risk 606 1
94 AML-High Risk 2640 0
95 AML-High Risk 2430 0
96 AML-High Risk 2252 0
97 AML-High Risk 2140 0
98 AML-High Risk 2133 0
99 AML-High Risk 1238 0
100 AML-High Risk 1631 0
101 AML-High Risk 2024 0
102 AML-High Risk 1345 0
103 AML-High Risk 1136 0
104 AML-High Risk 845 0
105 AML-High Risk 422 1
106 AML-High Risk 162 1
107 AML-High Risk 84 1
108 AML-High Risk 100 1
109 AML-High Risk 2 1
110 AML-High Risk 47 1
111 AML-High Risk 242 1
112 AML-High Risk 456 1
113 AML-High Risk 268 1
114 AML-High Risk 318 1
115 AML-High Risk 32 1
116 AML-High Risk 467 1
117 AML-High Risk 47 1
118 AML-High Risk 390 1
119 AML-High Risk 183 1
120 AML-High Risk 105 1
121 AML-High Risk 115 1
122 AML-High Risk 164 1
123 AML-High Risk 93 1
124 AML-High Risk 120 1
125 AML-High Risk 80 1
126 AML-High Risk 677 1
127 AML-High Risk 64 1
128 AML-High Risk 168 1
129 AML-High Risk 74 1
130 AML-High Risk 16 1
131 AML-High Risk 157 1
132 AML-High Risk 625 1
133 AML-High Risk 48 1
134 AML-High Risk 273 1
135 AML-High Risk 63 1
136 AML-High Risk 76 1
137 AML-High Risk 113 1
138 AML-High Risk 363 1

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t,s,se,linear,loglog,log,asinsqrt,logit
1,0.97368,0.025967,8.6141,2.37831,9.7871,4.44648,2.47903
55,0.94737,0.036224,5.4486,2.36375,6.1098,3.60151,2.46635
74,0.92105,0.043744,3.9103,2.16833,4.3257,2.94398,2.25757
86,0.89474,0.049784,2.9073,1.89961,3.1713,2.38164,1.97023
104,0.86842,0.054836,2.1595,1.59196,2.3217,1.87884,1.64297
107,0.84211,0.059153,1.5571,1.26050,1.6490,1.41733,1.29331
109,0.81579,0.062886,1.0462,0.91307,1.0908,0.98624,0.93069
110,0.78947,0.066135,0.5969,0.55415,0.6123,0.57846,0.56079
122,0.73684,0.071434,–0.1842,–0.18808,–0.1826,–0.18573,–0.18728
129,0.71053,0.073570,–0.5365,–0.56842,–0.5222,–0.54859,–0.56101
172,0.68421,0.075405,–0.8725,–0.95372,–0.8330,–0.90178,–0.93247
192,0.65789,0.076960,–1.1968,–1.34341,–1.1201,–1.24712,–1.30048
194,0.63158,0.078252,–1.5133,–1.73709,–1.3870,–1.58613,–1.66406
230,0.60412,0.079522,–1.8345,–2.14672,–1.6432,–1.92995,–2.03291
276,0.57666,0.080509,–2.1531,–2.55898,–1.8825,–2.26871,–2.39408
332,0.54920,0.081223,–2.4722,–2.97389,–2.1070,–2.60380,–2.74691
383,0.52174,0.081672,–2.7948,–3.39146,–2.3183,–2.93646,–3.09068
418,0.49428,0.081860,–3.1239,–3.81166,–2.5177,–3.26782,–3.42460
466,0.46682,0.081788,–3.4624,–4.23445,–2.7062,–3.59898,–3.74781
487,0.43936,0.081457,–3.8136,–4.65971,–2.8844,–3.93103,–4.05931
526,0.41190,0.080862,–4.1812,–5.08726,–3.0527,–4.26507,–4.35795
609,0.38248,0.080260,–4.5791,–5.52446,–3.2091,–4.60719,–4.64271
662,0.35306,0.079296,–5.0059,–5.96222,–3.3546,–4.95358,–4.90900
1 t s se linear loglog log asinsqrt logit
2 1 0.97368 0.025967 8.6141 2.37831 9.7871 4.44648 2.47903
3 55 0.94737 0.036224 5.4486 2.36375 6.1098 3.60151 2.46635
4 74 0.92105 0.043744 3.9103 2.16833 4.3257 2.94398 2.25757
5 86 0.89474 0.049784 2.9073 1.89961 3.1713 2.38164 1.97023
6 104 0.86842 0.054836 2.1595 1.59196 2.3217 1.87884 1.64297
7 107 0.84211 0.059153 1.5571 1.26050 1.6490 1.41733 1.29331
8 109 0.81579 0.062886 1.0462 0.91307 1.0908 0.98624 0.93069
9 110 0.78947 0.066135 0.5969 0.55415 0.6123 0.57846 0.56079
10 122 0.73684 0.071434 –0.1842 –0.18808 –0.1826 –0.18573 –0.18728
11 129 0.71053 0.073570 –0.5365 –0.56842 –0.5222 –0.54859 –0.56101
12 172 0.68421 0.075405 –0.8725 –0.95372 –0.8330 –0.90178 –0.93247
13 192 0.65789 0.076960 –1.1968 –1.34341 –1.1201 –1.24712 –1.30048
14 194 0.63158 0.078252 –1.5133 –1.73709 –1.3870 –1.58613 –1.66406
15 230 0.60412 0.079522 –1.8345 –2.14672 –1.6432 –1.92995 –2.03291
16 276 0.57666 0.080509 –2.1531 –2.55898 –1.8825 –2.26871 –2.39408
17 332 0.54920 0.081223 –2.4722 –2.97389 –2.1070 –2.60380 –2.74691
18 383 0.52174 0.081672 –2.7948 –3.39146 –2.3183 –2.93646 –3.09068
19 418 0.49428 0.081860 –3.1239 –3.81166 –2.5177 –3.26782 –3.42460
20 466 0.46682 0.081788 –3.4624 –4.23445 –2.7062 –3.59898 –3.74781
21 487 0.43936 0.081457 –3.8136 –4.65971 –2.8844 –3.93103 –4.05931
22 526 0.41190 0.080862 –4.1812 –5.08726 –3.0527 –4.26507 –4.35795
23 609 0.38248 0.080260 –4.5791 –5.52446 –3.2091 –4.60719 –4.64271
24 662 0.35306 0.079296 –5.0059 –5.96222 –3.3546 –4.95358 –4.90900

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import numpy as np
"""
Generate data sets for testing Cox proportional hazards regression
models.
After updating the test data sets, use R to run the survival.R script
to update the R results.
"""
# The current data may not reflect this seed
np.random.seed(5234)
# Loop over pairs containing (sample size, number of variables).
for (n, p) in (20, 1), (50, 1), (50, 2), (100, 5), (1000, 10):
exog = np.random.normal(size=(5*n, p))
coef = np.linspace(-0.5, 0.5, p)
lpred = np.dot(exog, coef)
expected_survival_time = np.exp(-lpred)
# Survival times are exponential
survival_time = -np.log(np.random.uniform(size=5*n))
survival_time *= expected_survival_time
# Set this to get a reasonable amount of censoring
expected_censoring_time = np.mean(expected_survival_time)
# Theses are the observation times.
censoring_time = -np.log(np.random.uniform(size=5*n))
censoring_time *= expected_censoring_time
# Entry times
entry_time = -np.log(np.random.uniform(size=5*n))
entry_time *= 0.5*expected_censoring_time
# 1=failure (death), 0=no failure (no death)
status = 1*(survival_time <= censoring_time)
# The censoring time of the failure time, whichever comes first
time = np.where(status == 1, survival_time, censoring_time)
# Round time so that we have ties
time = np.around(time, decimals=1)
# Only take cases where the entry time is before the failure or
# censoring time. Take exactly n such cases.
ii = np.flatnonzero(entry_time < time)
ii = ii[np.random.permutation(len(ii))[0:n]]
status = status[ii]
time = time[ii]
exog = exog[ii, :]
entry_time = entry_time[ii]
data = np.concatenate((time[:, None], status[:, None],
entry_time[:, None], exog),
axis=1)
fname = "results/survival_data_%d_%d.csv" % (n, p)
np.savetxt(fname, data, fmt="%.5f")

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2.00000 0.00000 0.04088 1.65888 0.43887 2.16009 1.53852 -0.32477
0.80000 0.00000 0.64317 -0.01543 1.27200 0.06238 -1.39983 0.28912
0.70000 0.00000 0.62035 0.01568 1.10189 1.08241 0.28838 0.03060
0.20000 0.00000 0.00396 1.92122 -0.74627 -0.20202 -0.49432 0.18627
1.30000 0.00000 0.48061 0.03445 0.62648 -0.05107 0.21468 -1.33564
0.10000 1.00000 0.00311 -0.42507 -0.02765 0.92646 -1.79002 0.46062
0.10000 1.00000 0.00360 -1.92760 -0.97652 0.33572 1.09292 0.99413
0.20000 1.00000 0.16825 -0.92224 -1.46157 -0.83315 1.27701 0.89145
1.90000 0.00000 0.31681 0.47668 -2.21992 -1.23187 0.17330 -0.71609
0.20000 1.00000 0.11154 -0.78852 -0.22280 -0.39515 0.49144 0.73336
0.20000 0.00000 0.08597 0.63231 0.55002 0.32947 0.04749 -0.85148
0.60000 1.00000 0.07035 -0.98309 0.24301 -0.72338 0.81746 -1.26767
0.90000 1.00000 0.12016 -0.95699 -0.79824 -0.33108 1.09238 -1.56246
0.90000 1.00000 0.02773 0.71522 0.92762 -0.58902 1.14907 0.04783
1.10000 0.00000 0.69469 0.23457 0.83722 -0.50415 0.55788 -0.06246
1.20000 0.00000 0.75846 -0.48568 0.26540 -0.65537 1.36581 -0.12905
0.90000 1.00000 0.18518 -0.64696 1.20687 -1.18730 1.23675 -0.30962
0.40000 1.00000 0.05883 -0.81157 0.13249 -1.84991 0.19780 0.79789
0.30000 1.00000 0.29091 0.50970 1.45016 0.02051 1.09439 0.66784
0.90000 0.00000 0.21925 1.25407 -0.55512 0.06966 -0.22258 -1.92762
0.80000 0.00000 0.52030 -1.82573 -0.96723 3.06176 -0.78653 -1.03201
0.50000 0.00000 0.31396 -1.50465 0.34928 -0.28756 -0.75276 -1.60910
2.80000 0.00000 0.50161 -0.51879 1.85085 1.62061 1.62330 0.31456
0.20000 0.00000 0.17478 0.51892 -2.01221 0.24535 0.33276 -0.00219
0.70000 1.00000 0.02345 1.01616 -2.08282 0.85616 0.20281 0.85377
0.50000 0.00000 0.02822 -1.22080 0.43769 -0.94754 -1.30187 0.90808
0.60000 0.00000 0.35722 1.42375 -0.38970 -0.64537 -1.08120 -0.66272
0.10000 0.00000 0.01318 -2.32399 -0.32153 -0.64911 2.44607 -0.67781
0.70000 1.00000 0.33234 -0.86239 2.74390 -0.26652 -1.38002 -1.72868
0.30000 1.00000 0.01818 -0.00276 -0.73023 -0.07908 -0.36846 0.24726
2.50000 1.00000 0.12683 -0.20405 1.38441 0.11380 -1.55265 1.61875
1.90000 1.00000 1.05276 0.46781 0.14070 -2.79994 0.69604 -0.71800
0.30000 0.00000 0.12424 0.34365 -1.30879 0.06613 -1.80332 0.50442
2.00000 1.00000 0.16247 -0.31284 -1.08730 -0.08539 0.00160 -1.68699
2.10000 1.00000 0.19426 -0.31993 0.21405 -0.52766 -1.15955 -1.66554
0.20000 1.00000 0.01721 0.25259 0.37725 1.05776 0.20531 0.00615
0.20000 0.00000 0.08898 0.25225 -0.59453 -1.68651 0.01607 -0.13487
3.30000 1.00000 0.36441 1.58715 0.10576 0.90020 -0.64476 0.16278
1.60000 1.00000 0.25111 0.25002 0.29993 0.17598 -0.01910 0.14305
1.00000 1.00000 0.91850 0.51040 -0.37021 -1.11632 0.21391 -0.13709
1.50000 1.00000 0.85495 1.01267 0.53073 1.60362 0.91206 -0.06950
1.70000 0.00000 0.98528 -0.03470 0.46678 0.51241 1.81681 -0.87271
1.50000 0.00000 0.45072 1.22177 -0.33281 -0.21419 0.30984 0.25901
0.30000 1.00000 0.01095 0.08628 0.06831 -0.55069 -0.98570 -1.47021
0.70000 1.00000 0.20957 0.09985 0.55591 -0.14907 1.44737 -0.19020
1.80000 0.00000 0.48048 -0.15396 2.06115 0.73874 -0.63995 0.83598
1.20000 1.00000 0.05958 1.34778 1.18792 -0.37932 2.49795 0.34569
1.60000 0.00000 0.37681 0.42462 0.39925 0.31818 -1.24178 -2.10098
1.60000 0.00000 0.27681 0.43797 0.42857 0.71614 0.47339 -0.69239
1.50000 0.00000 0.16083 -0.68583 -1.09449 0.14085 0.15615 -2.17219
0.30000 0.00000 0.11799 0.84516 0.60886 -0.02455 -0.88110 -0.78903
1.30000 0.00000 0.47439 1.14278 0.22765 0.00355 1.24747 -0.44769
1.00000 0.00000 0.34329 1.13115 -0.71649 0.20680 -0.62674 0.11690
0.90000 0.00000 0.07241 0.87043 -0.52193 -1.82556 -0.96574 -0.46303
1.50000 1.00000 0.40548 0.17182 1.23767 -0.22511 0.92575 -0.99200
0.70000 0.00000 0.13319 0.64169 0.50024 -1.40025 -0.22809 0.37239
0.40000 1.00000 0.30444 -1.48351 -0.71597 1.13583 -0.31381 0.17228
0.40000 0.00000 0.00518 0.78247 0.27451 0.90321 0.88329 0.87831
0.10000 1.00000 0.06982 -0.40623 -0.59915 0.16943 0.65481 0.74213
0.20000 1.00000 0.02326 0.98320 -0.33050 -1.45162 1.03546 0.05095
1.70000 1.00000 0.26671 0.39948 -0.01170 -1.99802 1.09214 0.38202
0.50000 1.00000 0.20631 1.85586 1.26267 1.09781 0.26681 0.86920
0.30000 1.00000 0.03166 -1.12012 -2.17322 -0.81189 0.47966 0.66974
0.40000 0.00000 0.17714 -0.62389 0.11727 0.30922 0.45983 -1.18198
0.90000 0.00000 0.39204 -1.45848 0.60149 0.40569 -0.14587 -0.41534
0.30000 0.00000 0.03714 0.83848 0.53119 -0.58428 1.31723 -1.24635
1.10000 0.00000 0.46379 0.21576 -0.81655 0.31364 -0.27610 -1.03055
1.40000 0.00000 0.71581 0.88179 2.50473 -1.20963 0.22792 -0.68145
0.50000 1.00000 0.04560 -0.13862 1.38279 -0.96235 1.86021 0.77598
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1.00000 0.00000 0.53120 0.10882 -1.29716
1.40000 0.00000 0.39915 1.78210 -0.98747
0.30000 1.00000 0.24249 -0.52481 -0.00631
1.50000 0.00000 0.90297 -0.15558 -0.41401
0.60000 0.00000 0.11513 1.91513 0.84025
2.90000 0.00000 1.22897 0.83216 0.05158
0.20000 1.00000 0.14026 -0.15508 2.57765
0.20000 1.00000 0.02342 -0.43017 -0.19378
0.10000 1.00000 0.01043 -2.09826 1.82889
1.00000 0.00000 0.12414 -0.15255 -0.78868
0.10000 0.00000 0.08182 2.05096 -0.00273
0.70000 0.00000 0.50106 -1.11553 -0.37599
0.70000 0.00000 0.04540 0.20011 -1.63279
0.70000 1.00000 0.41003 1.04118 0.07873
0.40000 1.00000 0.33584 -1.70869 -0.94676
1.20000 0.00000 0.59698 0.21795 -2.65967
1.10000 0.00000 0.90747 1.10303 2.21828
0.20000 0.00000 0.15655 1.50321 0.88795
1 1.00000 1.00000 0.03528 -0.38315 -1.11646
2 0.30000 1.00000 0.01149 -2.16263 0.67790
3 1.50000 1.00000 1.19729 1.33121 -0.43045
4 0.70000 1.00000 0.53896 0.84117 -0.63001
5 0.20000 1.00000 0.16054 -0.95780 -0.82060
6 0.90000 1.00000 0.30738 -1.08477 -0.06120
7 0.40000 0.00000 0.12942 -1.76866 -0.04241
8 0.50000 1.00000 0.13266 -0.26914 -1.53422
9 0.90000 0.00000 0.15526 -0.81154 -0.36088
10 0.60000 0.00000 0.12826 0.33186 0.65154
11 0.70000 0.00000 0.22373 0.92204 0.25643
12 1.20000 1.00000 0.48185 -0.43982 0.31456
13 0.90000 0.00000 0.63365 -1.04665 -2.21337
14 0.60000 0.00000 0.07299 -2.42939 0.18910
15 1.20000 1.00000 0.69923 -0.29619 0.26199
16 0.80000 0.00000 0.10030 0.85841 0.88421
17 0.80000 1.00000 0.63120 -0.36353 0.26827
18 0.70000 1.00000 0.55604 -1.95990 -0.44759
19 1.10000 0.00000 0.40564 0.88427 -1.96625
20 0.20000 1.00000 0.03342 0.64777 -0.36391
21 0.10000 1.00000 0.06233 -0.64523 0.51863
22 1.10000 1.00000 0.04864 2.43561 0.44143
23 1.00000 0.00000 0.14794 1.51211 -1.71867
24 1.30000 1.00000 0.16023 -0.49040 -0.02378
25 0.40000 1.00000 0.24862 0.08828 -0.70830
26 0.50000 0.00000 0.12510 1.64139 -0.07560
27 1.90000 1.00000 0.57775 0.22910 -0.06035
28 0.60000 0.00000 0.21988 -0.61760 -0.84216
29 1.50000 0.00000 0.73572 -0.31742 -0.50461
30 0.20000 0.00000 0.09880 0.01778 -0.61300
31 2.70000 1.00000 0.68267 0.46799 -1.20130
32 0.20000 0.00000 0.00640 -0.57452 0.07641
33 1.00000 0.00000 0.53120 0.10882 -1.29716
34 1.40000 0.00000 0.39915 1.78210 -0.98747
35 0.30000 1.00000 0.24249 -0.52481 -0.00631
36 1.50000 0.00000 0.90297 -0.15558 -0.41401
37 0.60000 0.00000 0.11513 1.91513 0.84025
38 2.90000 0.00000 1.22897 0.83216 0.05158
39 0.20000 1.00000 0.14026 -0.15508 2.57765
40 0.20000 1.00000 0.02342 -0.43017 -0.19378
41 0.10000 1.00000 0.01043 -2.09826 1.82889
42 1.00000 0.00000 0.12414 -0.15255 -0.78868
43 0.10000 0.00000 0.08182 2.05096 -0.00273
44 0.70000 0.00000 0.50106 -1.11553 -0.37599
45 0.70000 0.00000 0.04540 0.20011 -1.63279
46 0.70000 1.00000 0.41003 1.04118 0.07873
47 0.40000 1.00000 0.33584 -1.70869 -0.94676
48 1.20000 0.00000 0.59698 0.21795 -2.65967
49 1.10000 0.00000 0.90747 1.10303 2.21828
50 0.20000 0.00000 0.15655 1.50321 0.88795

10
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import numpy as np
coef_50_2_0 = np.array([-0.6748149, 0.5219471])
coef_50_2_1 = np.array([-0.3464841, 0.211115])
coef_100_5_0 = np.array([
-0.4839566, -0.3130558, -0.1239565, 0.3466049, 0.5827503])
coef_100_5_1 = np.array([-0.1314948, 0, 0, 0.0324285, 0.2364489])

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import numpy as np
coef_20_1_bre = np.array([-0.9185611])
se_20_1_bre = np.array([0.4706831])
time_20_1_bre = np.array([
0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 1.1, 1.2, 1.3, 1.4, 1.5])
hazard_20_1_bre = np.array([
0, 0, 0.04139181, 0.1755379, 0.3121216, 0.3121216, 0.4263121,
0.6196358, 0.6196358, 0.6196358, 0.909556, 1.31083, 1.31083])
coef_20_1_et_bre = np.array([-0.8907007])
se_20_1_et_bre = np.array([0.4683384])
time_20_1_et_bre = np.array([0])
hazard_20_1_et_bre = np.array([0])
coef_20_1_st_bre = np.array([-0.5766809])
se_20_1_st_bre = np.array([0.4418918])
time_20_1_st_bre = np.array([0])
hazard_20_1_st_bre = np.array([0])
coef_20_1_et_st_bre = np.array([-0.5785683])
se_20_1_et_st_bre = np.array([0.4388437])
time_20_1_et_st_bre = np.array([0])
hazard_20_1_et_st_bre = np.array([0])
coef_20_1_efr = np.array([-0.9975319])
se_20_1_efr = np.array([0.4792421])
time_20_1_efr = np.array([
0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 1.1, 1.2, 1.3, 1.4, 1.5])
hazard_20_1_efr = np.array([
0, 0, 0.03934634, 0.1663316, 0.2986427, 0.2986427, 0.4119189,
0.6077373, 0.6077373, 0.6077373, 0.8933041, 1.285732, 1.285732])
coef_20_1_et_efr = np.array([-0.9679541])
se_20_1_et_efr = np.array([0.4766406])
time_20_1_et_efr = np.array([0])
hazard_20_1_et_efr = np.array([0])
coef_20_1_st_efr = np.array([-0.6345294])
se_20_1_st_efr = np.array([0.4455952])
time_20_1_st_efr = np.array([0])
hazard_20_1_st_efr = np.array([0])
coef_20_1_et_st_efr = np.array([-0.6355622])
se_20_1_et_st_efr = np.array([0.4423104])
time_20_1_et_st_efr = np.array([0])
hazard_20_1_et_st_efr = np.array([0])
coef_50_1_bre = np.array([-0.6761247])
se_50_1_bre = np.array([0.25133])
time_50_1_bre = np.array([
0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2,
1.5, 1.6, 1.7, 1.8, 1.9, 2.4, 2.8])
hazard_50_1_bre = np.array([
0, 0.04895521, 0.08457461, 0.2073863, 0.2382473, 0.2793018,
0.3271622, 0.3842953, 0.3842953, 0.5310807, 0.6360276,
0.7648251, 0.7648251, 0.9294298, 0.9294298, 0.9294298,
1.206438, 1.555569, 1.555569])
coef_50_1_et_bre = np.array([-0.6492871])
se_50_1_et_bre = np.array([0.2542493])
time_50_1_et_bre = np.array([0])
hazard_50_1_et_bre = np.array([0])
coef_50_1_st_bre = np.array([-0.7051135])
se_50_1_st_bre = np.array([0.2852093])
time_50_1_st_bre = np.array([0])
hazard_50_1_st_bre = np.array([0])
coef_50_1_et_st_bre = np.array([-0.8672546])
se_50_1_et_st_bre = np.array([0.3443235])
time_50_1_et_st_bre = np.array([0])
hazard_50_1_et_st_bre = np.array([0])
coef_50_1_efr = np.array([-0.7119322])
se_50_1_efr = np.array([0.2533563])
time_50_1_efr = np.array([
0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2,
1.5, 1.6, 1.7, 1.8, 1.9, 2.4, 2.8])
hazard_50_1_efr = np.array([
0, 0.04773902, 0.08238731, 0.2022993, 0.2327053, 0.2736316,
0.3215519, 0.3787123, 0.3787123, 0.526184, 0.6323073,
0.7627338, 0.7627338, 0.9288858, 0.9288858, 0.9288858,
1.206835, 1.556054, 1.556054])
coef_50_1_et_efr = np.array([-0.7103063])
se_50_1_et_efr = np.array([0.2598129])
time_50_1_et_efr = np.array([0])
hazard_50_1_et_efr = np.array([0])
coef_50_1_st_efr = np.array([-0.7417904])
se_50_1_st_efr = np.array([0.2846437])
time_50_1_st_efr = np.array([0])
hazard_50_1_st_efr = np.array([0])
coef_50_1_et_st_efr = np.array([-0.9276112])
se_50_1_et_st_efr = np.array([0.3462638])
time_50_1_et_st_efr = np.array([0])
hazard_50_1_et_st_efr = np.array([0])
coef_50_2_bre = np.array([-0.5935189, 0.5035724])
se_50_2_bre = np.array([0.2172841, 0.2399933])
time_50_2_bre = np.array([
0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2,
1.3, 1.4, 1.5, 1.9, 2.7, 2.9])
hazard_50_2_bre = np.array([
0.02695812, 0.09162381, 0.1309537, 0.1768423, 0.2033353,
0.2033353, 0.3083449, 0.3547287, 0.4076453, 0.4761318,
0.5579718, 0.7610905, 0.918962, 0.918962, 1.136173,
1.605757, 2.457676, 2.457676])
coef_50_2_et_bre = np.array([-0.4001465, 0.4415933])
se_50_2_et_bre = np.array([0.1992302, 0.2525949])
time_50_2_et_bre = np.array([0])
hazard_50_2_et_bre = np.array([0])
coef_50_2_st_bre = np.array([-0.6574891, 0.4416079])
se_50_2_st_bre = np.array([0.2753398, 0.269458])
time_50_2_st_bre = np.array([0])
hazard_50_2_st_bre = np.array([0])
coef_50_2_et_st_bre = np.array([-0.3607069, 0.2731982])
se_50_2_et_st_bre = np.array([0.255415, 0.306942])
time_50_2_et_st_bre = np.array([0])
hazard_50_2_et_st_bre = np.array([0])
coef_50_2_efr = np.array([-0.6107485, 0.5309737])
se_50_2_efr = np.array([0.2177713, 0.2440535])
time_50_2_efr = np.array([
0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2,
1.3, 1.4, 1.5, 1.9, 2.7, 2.9])
hazard_50_2_efr = np.array([
0.02610571, 0.08933637, 0.1279094, 0.1731699, 0.19933,
0.19933, 0.303598, 0.3497025, 0.4023939, 0.4706978,
0.5519237, 0.7545023, 0.9129989, 0.9129989, 1.13186,
1.60574, 2.472615, 2.472615])
coef_50_2_et_efr = np.array([-0.4092002, 0.4871344])
se_50_2_et_efr = np.array([0.1968905, 0.2608527])
time_50_2_et_efr = np.array([0])
hazard_50_2_et_efr = np.array([0])
coef_50_2_st_efr = np.array([-0.6631286, 0.4663285])
se_50_2_st_efr = np.array([0.2748224, 0.273603])
time_50_2_st_efr = np.array([0])
hazard_50_2_st_efr = np.array([0])
coef_50_2_et_st_efr = np.array([-0.3656059, 0.2943912])
se_50_2_et_st_efr = np.array([0.2540752, 0.3124632])
time_50_2_et_st_efr = np.array([0])
hazard_50_2_et_st_efr = np.array([0])
coef_100_5_bre = np.array([
-0.529776, -0.2916374, -0.1205425, 0.3493476, 0.6034305])
se_100_5_bre = np.array([
0.1789305, 0.1482505, 0.1347422, 0.1528205, 0.1647927])
time_100_5_bre = np.array([
0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2,
1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.1, 2.5, 2.8, 3.2, 3.3])
hazard_100_5_bre = np.array([
0.02558588, 0.05608812, 0.1087773, 0.1451098, 0.1896703,
0.2235791, 0.3127521, 0.3355107, 0.439452, 0.504983,
0.5431706, 0.5841462, 0.5841462, 0.5841462, 0.6916466,
0.7540191, 0.8298704, 1.027876, 1.170335, 1.379306,
1.648758, 1.943177, 1.943177, 1.943177, 4.727101])
coef_100_5_et_bre = np.array([
-0.4000784, -0.1790941, -0.1378969, 0.3288529, 0.533246])
se_100_5_et_bre = np.array([
0.1745655, 0.1513545, 0.1393968, 0.1487803, 0.1686992])
time_100_5_et_bre = np.array([0])
hazard_100_5_et_bre = np.array([0])
coef_100_5_st_bre = np.array([
-0.53019, -0.3225739, -0.1241568, 0.3246598, 0.6196859])
se_100_5_st_bre = np.array([
0.1954581, 0.1602811, 0.1470644, 0.17121, 0.1784115])
time_100_5_st_bre = np.array([0])
hazard_100_5_st_bre = np.array([0])
coef_100_5_et_st_bre = np.array([
-0.3977171, -0.2166136, -0.1387623, 0.3251726, 0.5664705])
se_100_5_et_st_bre = np.array([
0.1951054, 0.1707925, 0.1501968, 0.1699932, 0.1843428])
time_100_5_et_st_bre = np.array([0])
hazard_100_5_et_st_bre = np.array([0])
coef_100_5_efr = np.array([
-0.5641909, -0.3233021, -0.1234858, 0.3712328, 0.6421963])
se_100_5_efr = np.array([
0.1804027, 0.1496253, 0.1338531, 0.1529832, 0.1670848])
time_100_5_efr = np.array([
0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2,
1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.1, 2.5, 2.8, 3.2, 3.3])
hazard_100_5_efr = np.array([
0.02393412, 0.05276399, 0.1028432, 0.1383859, 0.1823461,
0.2158107, 0.3037825, 0.3264864, 0.4306648, 0.4964367,
0.5348595, 0.5760305, 0.5760305, 0.5760305, 0.6842238,
0.7468135, 0.8228841, 1.023195, 1.166635, 1.379361,
1.652898, 1.950119, 1.950119, 1.950119, 4.910635])
coef_100_5_et_efr = np.array([
-0.4338666, -0.2140139, -0.1397387, 0.3535993, 0.5768645])
se_100_5_et_efr = np.array([
0.1756485, 0.1527244, 0.138298, 0.1488427, 0.1716654])
time_100_5_et_efr = np.array([0])
hazard_100_5_et_efr = np.array([0])
coef_100_5_st_efr = np.array([
-0.5530876, -0.3331652, -0.128381, 0.3503472, 0.6397813])
se_100_5_st_efr = np.array([
0.1969338, 0.1614976, 0.1464088, 0.171299, 0.1800787])
time_100_5_st_efr = np.array([0])
hazard_100_5_st_efr = np.array([0])
coef_100_5_et_st_efr = np.array([
-0.421153, -0.2350069, -0.1433638, 0.3538863, 0.5934568])
se_100_5_et_st_efr = np.array([
0.1961729, 0.1724719, 0.1492979, 0.170464, 0.1861849])
time_100_5_et_st_efr = np.array([0])
hazard_100_5_et_st_efr = np.array([0])
coef_1000_10_bre = np.array([
-0.4699279, -0.464557, -0.308411, -0.2158298, -0.09048563,
0.09359662, 0.112588, 0.3343705, 0.3480601, 0.5634985])
se_1000_10_bre = np.array([
0.04722914, 0.04785291, 0.04503528, 0.04586872, 0.04429793,
0.0446141, 0.04139944, 0.04464292, 0.04559903, 0.04864393])
time_1000_10_bre = np.array([
0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2,
1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.1, 2.2, 2.3, 2.4,
2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6,
3.7, 3.8, 3.9, 4, 4.1, 4.2, 4.3, 4.4, 4.6, 4.8, 4.9, 5, 5.1,
5.2, 5.7, 5.8, 5.9, 6.9])
hazard_1000_10_bre = np.array([
0.01610374, 0.04853538, 0.08984849, 0.1311329, 0.168397,
0.2230488, 0.2755388, 0.3312606, 0.3668702, 0.4146558,
0.477935, 0.5290705, 0.5831775, 0.6503129, 0.7113068,
0.7830385, 0.8361717, 0.8910061, 0.9615944, 1.024011,
1.113399, 1.165349, 1.239827, 1.352902, 1.409548, 1.53197,
1.601843, 1.682158, 1.714907, 1.751564, 1.790898, 1.790898,
1.83393, 1.83393, 1.936055, 1.992303, 2.050778, 2.118776,
2.263056, 2.504999, 2.739343, 2.895514, 3.090349, 3.090349,
3.391772, 3.728142, 4.152769, 4.152769, 4.152769, 4.725957,
4.725957, 5.69653, 5.69653, 5.69653])
coef_1000_10_et_bre = np.array([
-0.410889, -0.3929442, -0.2975845, -0.1851533, -0.0918359,
0.1011997, 0.106735, 0.2899179, 0.3220672, 0.5069589])
se_1000_10_et_bre = np.array([
0.04696754, 0.04732169, 0.04537707, 0.04605371, 0.04365232,
0.04450021, 0.04252475, 0.04482007, 0.04562374, 0.04859727])
time_1000_10_et_bre = np.array([0])
hazard_1000_10_et_bre = np.array([0])
coef_1000_10_st_bre = np.array([
-0.471015, -0.4766859, -0.3070839, -0.2091938, -0.09190845,
0.0964942, 0.1138269, 0.3307131, 0.3543551, 0.562492])
se_1000_10_st_bre = np.array([
0.04814778, 0.04841938, 0.04572291, 0.04641227, 0.04502525,
0.04517603, 0.04203737, 0.04524356, 0.04635037, 0.04920866])
time_1000_10_st_bre = np.array([0])
hazard_1000_10_st_bre = np.array([0])
coef_1000_10_et_st_bre = np.array([
-0.4165849, -0.4073504, -0.2980959, -0.1765194, -0.09152798,
0.1013213, 0.1009838, 0.2859668, 0.3247608, 0.5044448])
se_1000_10_et_st_bre = np.array([
0.04809818, 0.04809499, 0.0460829, 0.04679922, 0.0445294,
0.04514045, 0.04339298, 0.04580591, 0.04652447, 0.04920744])
time_1000_10_et_st_bre = np.array([0])
hazard_1000_10_et_st_bre = np.array([0])
coef_1000_10_efr = np.array([
-0.4894399, -0.4839746, -0.3227769, -0.2261293, -0.09318482,
0.09767154, 0.1173205, 0.3493732, 0.3640146, 0.5879749])
se_1000_10_efr = np.array([
0.0474181, 0.04811855, 0.04507655, 0.04603044, 0.04440409,
0.04478202, 0.04136728, 0.04473343, 0.045768, 0.04891375])
time_1000_10_efr = np.array([
0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2,
1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.1, 2.2, 2.3, 2.4,
2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6,
3.7, 3.8, 3.9, 4, 4.1, 4.2, 4.3, 4.4, 4.6, 4.8, 4.9, 5, 5.1,
5.2, 5.7, 5.8, 5.9, 6.9])
hazard_1000_10_efr = np.array([
0.01549698, 0.04680035, 0.08682564, 0.1269429, 0.1632388,
0.2167291, 0.2682311, 0.3231316, 0.3582936, 0.4054892, 0.4681098,
0.5188697, 0.5727059, 0.639571, 0.7003012, 0.7718979, 0.825053,
0.880063, 0.950935, 1.013828, 1.103903, 1.156314, 1.231707,
1.346235, 1.40359, 1.527475, 1.598231, 1.6795, 1.712779,
1.750227, 1.790455, 1.790455, 1.834455, 1.834455, 1.938997,
1.996804, 2.056859, 2.126816, 2.275217, 2.524027, 2.76669,
2.929268, 3.13247, 3.13247, 3.448515, 3.80143, 4.249649,
4.249649, 4.249649, 4.851365, 4.851365, 5.877307, 5.877307, 5.877307])
coef_1000_10_et_efr = np.array([
-0.4373066, -0.4131901, -0.3177637, -0.1978493, -0.09679451,
0.1092037, 0.1136069, 0.3088907, 0.3442007, 0.5394121])
se_1000_10_et_efr = np.array([
0.04716041, 0.04755342, 0.04546713, 0.04627802, 0.04376583,
0.04474868, 0.04259991, 0.04491564, 0.04589027, 0.04890847])
time_1000_10_et_efr = np.array([0])
hazard_1000_10_et_efr = np.array([0])
coef_1000_10_st_efr = np.array([
-0.4911117, -0.4960756, -0.3226152, -0.220949, -0.09478141,
0.1015735, 0.1195524, 0.3446977, 0.3695904, 0.5878576])
se_1000_10_st_efr = np.array([
0.04833676, 0.04868554, 0.04578407, 0.04661755, 0.04518267,
0.04537135, 0.04202183, 0.04531266, 0.0464931, 0.04949831])
time_1000_10_st_efr = np.array([0])
hazard_1000_10_st_efr = np.array([0])
coef_1000_10_et_st_efr = np.array([
-0.444355, -0.4283278, -0.3198815, -0.1901781, -0.09727039,
0.1106191, 0.1092104, 0.3034778, 0.3451699, 0.5382381])
se_1000_10_et_st_efr = np.array([
0.04830664, 0.04833619, 0.04617371, 0.04706401, 0.04472699,
0.0454208, 0.04350539, 0.04588588, 0.04675675, 0.04950987])
time_1000_10_et_st_efr = np.array([0])
hazard_1000_10_et_st_efr = np.array([0])