Oncology RWD: Data Exploration¶
This notebook explores synthetic oncology patient data, simulating EHR and biomarker records.
In [13]:
# Import libraries
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
In [14]:
# Load synthetic data
df = pd.read_csv('../data/synthetic_oncology_patients.csv')
df.head()
Out[14]:
| patient_id | age | gender | cancer_type | stage | diagnosis_date | biomarker_status | treatment | adverse_event | survival_months | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | P001 | 67 | F | Breast | II | 2021-03-15 | HER2+ | Trastuzumab | NaN | 28 |
| 1 | P002 | 59 | M | Lung | III | 2020-11-02 | EGFR+ | Osimertinib | Rash | 18 |
| 2 | P003 | 72 | F | Colorectal | IV | 2019-07-21 | BRAF- | FOLFOX | Neuropathy | 12 |
| 3 | P004 | 50 | M | Prostate | II | 2022-01-10 | AR+ | Abiraterone | NaN | 30 |
| 4 | P005 | 64 | F | Ovarian | III | 2021-06-18 | BRCA1+ | Carboplatin | Neutropenia | 22 |
Patient Demographics¶
In [15]:
# Demographic summary
df[['age', 'gender', 'cancer_type', 'stage']].describe(include='all')
Out[15]:
| age | gender | cancer_type | stage | |
|---|---|---|---|---|
| count | 10.000000 | 10 | 10 | 10 |
| unique | NaN | 2 | 5 | 3 |
| top | NaN | F | Breast | II |
| freq | NaN | 5 | 2 | 4 |
| mean | 62.200000 | NaN | NaN | NaN |
| std | 9.186947 | NaN | NaN | NaN |
| min | 48.000000 | NaN | NaN | NaN |
| 25% | 56.000000 | NaN | NaN | NaN |
| 50% | 63.000000 | NaN | NaN | NaN |
| 75% | 69.250000 | NaN | NaN | NaN |
| max | 75.000000 | NaN | NaN | NaN |
Cancer Type Distribution¶
In [16]:
sns.countplot(data=df, x='cancer_type', hue='gender')
plt.title('Cancer Type by Gender')
plt.show()
Biomarker Status vs. Treatment¶
In [17]:
pd.crosstab(df['biomarker_status'], df['treatment'])
Out[17]:
| treatment | Abiraterone | Alectinib | Carboplatin | Docetaxel | FOLFIRI | FOLFOX | Osimertinib | Paclitaxel | Trastuzumab |
|---|---|---|---|---|---|---|---|---|---|
| biomarker_status | |||||||||
| ALK+ | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| AR+ | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| AR- | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| BRAF+ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| BRAF- | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| BRCA1+ | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| BRCA2- | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| EGFR+ | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| HER2+ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| HER2- | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Survival Analysis (Simple)¶
In [18]:
sns.histplot(df['survival_months'], bins=8, kde=True)
plt.xlabel('Survival (months)')
plt.title('Distribution of Survival Time')
plt.show()
