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Updated: Jun 25, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Risk-adjusted monitoring of survival times
Landon H Sego1, Marion R Reynolds, William H Woodall
1Statistics and Sensor Analytics Group, Pacific Northwest National Laboratory, Richland, WA, U.S.A. Landon.Sego@pnl.gov
Abstract:
We consider the monitoring of surgical outcomes, where each patient has a different risk of post-operative mortality due to risk factors that exist prior to the surgery. We propose a risk-adjusted (RA) survival time CUSUM chart (RAST CUSUM) for monitoring a continuous, time-to-event variable that may be right-censored. Risk adjustment is accomplished using accelerated failure time regression models. We compare the average run length performance of the RAST CUSUM chart with the RA Bernoulli CUSUM chart using data from cardiac surgeries to motivate the details of the comparison. The comparisons show that the RAST CUSUM chart is more efficient at detecting a sudden increase in the odds of mortality than the RA Bernoulli CUSUM chart, especially when the fraction of censored observations is relatively low or when a small increase in the odds of mortality occurs. We also discuss the impact of the amount of training data used to estimate chart parameters as well as the implementation of the RAST CUSUM chart during prospective monitoring.
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