A nearest neighbor approach to predicting survival time with an application in chronic respiratory disease

The care for patients with chronic and progressive diseases often requires that reliable estimates of their remaining lifetime are made. The predominant method for obtaining such individual prognoses is to analyze historical data using Cox regression, and apply the resulting model to data from new patients. However, the black-box nature of the Cox regression model makes it unattractive for clinical practice. Instead most physicians prefer to relate a new patient to the histories of similar, individual patients that were treated before. This paper presents a prognostic inference method that combines the k-nearest neighbor paradigm with Cox regression. It yields survival predictions for individual patients, based on small sets of similar patients from the past, and can be used to implement a prognostic case-retrieval system. To evaluate the method, it was applied to data from patients with idiopathic interstitial pneumonia, a progressive and lethal lung disease. Experiments pointed out that the method competes well with Cox regression. The best predictive performance was obtained with a neighborhood size of 20.