Mercurial > repos > fubar > lifelines_km_cph_tool
annotate lifelines_tool/run_log.txt @ 2:dd5e65893cb8 draft default tip
add survival and collapsed life table outputs suggested by Wolfgang
author | fubar |
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date | Thu, 10 Aug 2023 22:52:45 +0000 |
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1 ## Lifelines tool |
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2 Input data header = Index(['Unnamed: 0', 'week', 'arrest', 'fin', 'age', 'race', 'wexp', 'mar', |
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3 'paro', 'prio'], |
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4 dtype='object') time column = week status column = arrest |
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5 #### No grouping variable, so no log rank or other Kaplan-Meier statistical output is available |
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6 Survival table using time week and event arrest |
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7 removed observed censored entrance at_risk |
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8 event_at |
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9 0.0 0 0 0 432 432 |
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10 1.0 1 1 0 0 432 |
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11 2.0 1 1 0 0 431 |
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12 3.0 1 1 0 0 430 |
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13 4.0 1 1 0 0 429 |
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14 5.0 1 1 0 0 428 |
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15 6.0 1 1 0 0 427 |
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16 7.0 1 1 0 0 426 |
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17 8.0 5 5 0 0 425 |
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18 9.0 2 2 0 0 420 |
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19 10.0 1 1 0 0 418 |
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20 11.0 2 2 0 0 417 |
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21 12.0 2 2 0 0 415 |
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22 13.0 1 1 0 0 413 |
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23 14.0 3 3 0 0 412 |
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24 15.0 2 2 0 0 409 |
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25 16.0 2 2 0 0 407 |
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26 17.0 3 3 0 0 405 |
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27 18.0 3 3 0 0 402 |
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28 19.0 2 2 0 0 399 |
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29 20.0 5 5 0 0 397 |
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30 21.0 2 2 0 0 392 |
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31 22.0 1 1 0 0 390 |
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32 23.0 1 1 0 0 389 |
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33 24.0 4 4 0 0 388 |
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34 25.0 3 3 0 0 384 |
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35 26.0 3 3 0 0 381 |
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36 27.0 2 2 0 0 378 |
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37 28.0 2 2 0 0 376 |
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38 30.0 2 2 0 0 374 |
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39 31.0 1 1 0 0 372 |
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40 32.0 2 2 0 0 371 |
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41 33.0 2 2 0 0 369 |
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42 34.0 2 2 0 0 367 |
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43 35.0 4 4 0 0 365 |
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44 36.0 3 3 0 0 361 |
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45 37.0 4 4 0 0 358 |
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46 38.0 1 1 0 0 354 |
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47 39.0 2 2 0 0 353 |
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48 40.0 4 4 0 0 351 |
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49 42.0 2 2 0 0 347 |
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50 43.0 4 4 0 0 345 |
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51 44.0 2 2 0 0 341 |
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52 45.0 2 2 0 0 339 |
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53 46.0 4 4 0 0 337 |
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54 47.0 1 1 0 0 333 |
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55 48.0 2 2 0 0 332 |
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56 49.0 5 5 0 0 330 |
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57 50.0 3 3 0 0 325 |
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58 52.0 322 4 318 0 322 |
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59 Life table using time week and event arrest |
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60 removed observed censored at_risk |
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61 event_at |
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62 (-0.001, 13.844] 20 20 0 432 |
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63 (13.844, 27.687] 36 36 0 412 |
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64 (27.687, 41.531] 29 29 0 376 |
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65 (41.531, 55.374] 347 29 318 347 |
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66 ### Lifelines test of Proportional Hazards results with prio, age, race, paro, mar, fin as covariates on test |
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67 <lifelines.CoxPHFitter: fitted with 432 total observations, 318 right-censored observations> |
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68 duration col = 'week' |
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69 event col = 'arrest' |
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70 baseline estimation = breslow |
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71 number of observations = 432 |
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72 number of events observed = 114 |
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73 partial log-likelihood = -659.00 |
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74 time fit was run = 2023-08-10 11:57:10 UTC |
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75 |
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76 --- |
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77 coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95% |
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78 covariate |
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79 prio 0.10 1.10 0.03 0.04 0.15 1.04 1.16 |
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80 age -0.06 0.94 0.02 -0.10 -0.02 0.90 0.98 |
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81 race 0.32 1.38 0.31 -0.28 0.92 0.75 2.52 |
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82 paro -0.09 0.91 0.20 -0.47 0.29 0.62 1.34 |
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83 mar -0.48 0.62 0.38 -1.22 0.25 0.30 1.29 |
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84 fin -0.38 0.68 0.19 -0.75 -0.00 0.47 1.00 |
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85 |
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86 cmp to z p -log2(p) |
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87 covariate |
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88 prio 0.00 3.53 <0.005 11.26 |
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89 age 0.00 -2.95 <0.005 8.28 |
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90 race 0.00 1.04 0.30 1.75 |
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91 paro 0.00 -0.46 0.65 0.63 |
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92 mar 0.00 -1.28 0.20 2.32 |
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93 fin 0.00 -1.98 0.05 4.40 |
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94 --- |
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95 Concordance = 0.63 |
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96 Partial AIC = 1330.00 |
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97 log-likelihood ratio test = 32.77 on 6 df |
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98 -log2(p) of ll-ratio test = 16.39 |
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99 |
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100 |
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101 Bootstrapping lowess lines. May take a moment... |
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102 |
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103 |
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104 Bootstrapping lowess lines. May take a moment... |
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105 |
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106 The ``p_value_threshold`` is set at 0.01. Even under the null hypothesis of no violations, some |
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107 covariates will be below the threshold by chance. This is compounded when there are many covariates. |
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108 Similarly, when there are lots of observations, even minor deviances from the proportional hazard |
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109 assumption will be flagged. |
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110 |
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111 With that in mind, it's best to use a combination of statistical tests and visual tests to determine |
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112 the most serious violations. Produce visual plots using ``check_assumptions(..., show_plots=True)`` |
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113 and looking for non-constant lines. See link [A] below for a full example. |
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114 |
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115 <lifelines.StatisticalResult: proportional_hazard_test> |
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116 null_distribution = chi squared |
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117 degrees_of_freedom = 1 |
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118 model = <lifelines.CoxPHFitter: fitted with 432 total observations, 318 right-censored observations> |
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119 test_name = proportional_hazard_test |
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120 |
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121 --- |
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122 test_statistic p -log2(p) |
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123 age km 6.99 0.01 6.93 |
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124 rank 7.40 0.01 7.26 |
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125 fin km 0.02 0.90 0.15 |
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126 rank 0.01 0.91 0.13 |
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127 mar km 1.64 0.20 2.32 |
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128 rank 1.80 0.18 2.48 |
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129 paro km 0.06 0.81 0.31 |
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130 rank 0.07 0.79 0.34 |
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131 prio km 0.92 0.34 1.57 |
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132 rank 0.88 0.35 1.52 |
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133 race km 1.70 0.19 2.38 |
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134 rank 1.68 0.19 2.36 |
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135 |
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136 |
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137 1. Variable 'age' failed the non-proportional test: p-value is 0.0065. |
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138 |
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139 Advice 1: the functional form of the variable 'age' might be incorrect. That is, there may be |
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140 non-linear terms missing. The proportional hazard test used is very sensitive to incorrect |
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141 functional forms. See documentation in link [D] below on how to specify a functional form. |
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142 |
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143 Advice 2: try binning the variable 'age' using pd.cut, and then specify it in `strata=['age', |
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144 ...]` in the call in `.fit`. See documentation in link [B] below. |
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145 |
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146 Advice 3: try adding an interaction term with your time variable. See documentation in link [C] |
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147 below. |
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148 |
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149 |
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150 Bootstrapping lowess lines. May take a moment... |
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151 |
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152 |
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153 Bootstrapping lowess lines. May take a moment... |
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154 |
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155 |
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156 Bootstrapping lowess lines. May take a moment... |
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157 |
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158 |
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159 Bootstrapping lowess lines. May take a moment... |
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160 |
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161 |
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162 --- |
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163 [A] https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html |
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164 [B] https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Bin-variable-and-stratify-on-it |
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165 [C] https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Introduce-time-varying-covariates |
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166 [D] https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Modify-the-functional-form |
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167 [E] https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Stratification |
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168 |