Which is NOT a recommended step in validating a Cox proportional hazards model?

• A. Evaluate proportional hazards assumption.
• B. Evaluate independent censoring.
• C. Check for outliers.
• D. Ignore influential points.

Answer: (D)

In validating a Cox model, the focus is on making sure the model’s assumptions and the data support reliable conclusions. The proportional hazards assumption is key because the model relies on constant hazard ratios over time; you look for time trends or interactions that suggest nonproportional effects and address them with stratification or time-dependent covariates if needed. Non-informative censoring is another cornerstone—if the reason someone is censored is related to their prognosis, the hazard estimates become biased, so you assess whether censoring mechanisms are independent of the survival process. Checking for outliers and influential observations is important because unusual data points can disproportionately affect coefficient estimates and the precision of the results; residuals and influence diagnostics help identify these points and gauge their impact, guiding decisions about data quality, model fit, or alternative approaches.

Ignoring influential points is not advisable because it leaves the potential bias or distortion in the model unchecked; instead, investigate their influence, perform sensitivity analyses, and consider adjustments or alternative modeling strategies to ensure conclusions are robust.