Business Context
A pharmaceutical research company is analyzing patient data from a clinical trial to predict patient recovery. The dataset is incomplete, with missing values for a key biomarker and patient-reported prior conditions. This scenario tests the model's robustness and the different strategies for handling missing data.
Data Preparation
Create a patient dataset where 'Biomarker_Level' and 'Prior_Conditions' have systematically introduced missing values. The target is 'Recovered' (1/0).
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| 1 | DATA clinical_trial_data; |
| 2 | call streaminit(42); |
| 3 | DO PatientID = 1 to 500; |
| 4 | Age = 30 + floor(rand('Uniform') * 45); |
| 5 | Biomarker_Level = rand('Normal', 100, 15); |
| 6 | Prior_Conditions = floor(rand('Uniform') * 4); |
| 7 | /* Introduce missing values */ |
| 8 | IF rand('Uniform') < 0.15 THEN call missing(Biomarker_Level); |
| 9 | IF rand('Uniform') < 0.20 THEN call missing(Prior_Conditions); |
| 10 | |
| 11 | logit_p = -3 + (Age/25) + (Biomarker_Level/50) - (Prior_Conditions * 0.4); |
| 12 | p = 1 / (1 + exp(-logit_p)); |
| 13 | Recovered = rand('Bernoulli', p); |
| 14 | OUTPUT; |
| 15 | END; |
| 16 | RUN; |
| 17 | |
| 18 | DATA casuser.clinical_trial_data; |
| 19 | SET clinical_trial_data; |
| 20 | RUN; |
Étapes de réalisation
1
Run bart.bartProbit using the default missing value handling ('SEPARATE').
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| 1 | PROC CAS; |
| 2 | bart.bartProbit TABLE={name='clinical_trial_data'}, |
| 3 | target='Recovered', |
| 4 | inputs={'Age', 'Biomarker_Level', 'Prior_Conditions'}, |
| 5 | missing='SEPARATE', /* Default, but explicit for clarity */ |
| 6 | minLeafSize=3, /* Test with a small leaf size */ |
| 7 | nTree=50, |
| 8 | nBI=100, |
| 9 | nMC=500, |
| 10 | seed=42, |
| 11 | outputTables={names={NObs='nobs_separate'}}; |
| 12 | RUN; |
| 13 | QUIT; |
2
Run the same model but change missing value handling to 'NONE' to exclude rows with missing data.
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| 1 | PROC CAS; |
| 2 | bart.bartProbit TABLE={name='clinical_trial_data'}, |
| 3 | target='Recovered', |
| 4 | inputs={'Age', 'Biomarker_Level', 'Prior_Conditions'}, |
| 5 | missing='NONE', |
| 6 | minLeafSize=3, |
| 7 | nTree=50, |
| 8 | nBI=100, |
| 9 | nMC=500, |
| 10 | seed=42, |
| 11 | outputTables={names={NObs='nobs_none'}}; |
| 12 | RUN; |
| 13 | QUIT; |
3
Compare the number of observations used in both models.
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| 1 | PROC CAS; |
| 2 | TABLE.fetch TABLE={name='nobs_separate'}; |
| 3 | TABLE.fetch TABLE={name='nobs_none'}; |
| 4 | RUN; |
| 5 | QUIT; |
Expected Result
Both actions should run successfully. The first run ('missing=SEPARATE') should use all 500 observations, treating missingness as a separate analytical category. The second run ('missing=NONE') should use a reduced number of observations, as it discards any row with a missing value in the predictors. Comparing the 'nobs_separate' and 'nobs_none' tables will confirm this difference, validating the behavior of the 'missing' parameter.