Purpose

This section documents the data preprocessing pipeline for a survival analysis model with 500 observations. Perfect retention (100%) indicates no rows were removed during cleaning, suggesting either exceptionally clean source data or minimal data quality validation. Understanding preprocessing decisions is critical for assessing whether the model's strong performance (concordance: 0.654, all 6 predictors significant) reflects genuine predictive power or potential data quality issues masked by incomplete validation.

Key Findings

Retention Rate: 100% (500/500 rows retained) - No observations were excluded during preprocessing
Rows Removed: 0 - Complete absence of filtering, outlier removal, or missing value handling
Train/Test Split: Not documented - No explicit validation strategy is recorded in the preprocessing stage
Data Transformations: No transformations explicitly noted despite survival analysis typically requiring time-to-event and event indicator preparation

Interpretation

The perfect retention rate is unusual for real-world data and raises questions about preprocessing rigor. With 93.4% event rate and 467 events across 500 observations, the data appears complete but potentially unvalidated. The absence of documented train/test splits means model performance metrics (concordance, AIC) may reflect training set performance rather than generalization capability. This is particularly concerning given all 6 predictors achieved statistical significance—a pattern that could indicate ov