This example compares each of the four new patients in the table lc_new_predictors with each of the attribute reference values provided in the table lc_new_reference, partitioning by id and calculating the hazard ratio only when the patient id matches the reference id.
Input
- cox_coef_model: lungcancer_coef, output by CoxPH Example
- predicts: lc_new_predictors, as in CoxHazardRatio Example 1: No Reference Values
- refs: lc_new_reference, as in CoxHazardRatio Example 3: Reference Values
SQL Call
SELECT * FROM CoxHazardRatio ( ON lungcancer_coef AS cox_coef_model DIMENSION ON lc_new_predictors AS predicts PARTITION BY id ON lc_new_reference AS refs PARTITION BY id USING PredictFeatureNames ('trt','celltype','karno','diagtime','age','prior') PredictFeatureColumns ('trt','celltype','karno','diagtime','age','prior') RefFeatureColumns ('trt','celltype','karno','diagtime','age','prior') Accumulate ('id','name') ) AS dt ORDER BY 1, 2, 3, 4, 5, 6, 7, 8;
Output
Because the input is partitioned, the output table has only 4 rows. The attributes of patient Steffi are similar to those of reference id 4, so her hazard ratio is very close to 1.0 (0.97).
id | name | trt | celltype | karno | diagtime | age | prior | trt_ref | celltype_ref | karno_ref | diagtime_ref | age_ref | prior_ref | hazardratio |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | John | standard | squamous | 30 | 4 | 63 | yes | standard | squamous | 58 | 12 | 60 | 2.44014910130533 | |
2 | James | standard | large | 80 | 12 | 41 | no | standard | smallcell | 54 | 8 | 58 | 0.311881511740527 | |
3 | Stella | test | smallcell | 70 | 3 | 72 | no | test | smallcell | 52 | 12 | 61 | 0.502993022637894 | |
4 | Steffi | test | adeno | 60 | 5 | 63 | yes | test | adeno | 60 | 5 | 60 | 0.974218736747828 |