In with the old, in with the new: machine learning for time to event biomedical research

Abstract

The predictive modeling literature for biomedical applications is dominated by biostatistical methods for survival analysis, and more recently some out of the box machine learning approaches. In this article, we show a presentation of a machine learning method appropriate for time-to-event modeling in the area of prostate cancer long-term disease progression. Using XGBoost adapted to long-term disease progression, we developed a predictive model for 118 788 patients with localized prostate cancer at diagnosis from the Department of Veterans Affairs (VA). Our model accounted for patient censoring. Harrell's c-index for our model using only features available at the time of diagnosis was 0.757 95% confidence interval [0.756, 0.757]. Our results show that machine learning methods like XGBoost can be adapted to use accelerated failure time (AFT) with censoring to model long-term risk of disease progression. The long median survival justifies and requires censoring. Overall, we show that an existing machine learning approach can be used for AFT outcome modeling in prostate cancer, and more generally for other chronic diseases with long observation times.

Keywords: machine learning; predictive modeling; survival analysis; xgboost.

Figure 1.

Cohort selection.

Figure 2.

SHAP predictor importance (average impact on model output magnitude).

Figure 3.

(A) Instance level predictor importance for hypothetical patient 1 with more aggressive disease at diagnosis—Hypothetical patient 1 timeline. (B) Instance level predictor importance for hypothetical patient 1 with more aggressive disease at diagnosis—Outcome prediction. (C) Instance level predictor importance for hypothetical patient 2 with less aggressive disease at diagnosis—Hypothetical patient 2 timeline. (D) Instance level predictor importance for hypothetical patient 2 with less aggressive disease at diagnosis—Outcome prediction.