Work

Tests of calibration and goodness‐of‐fit in the survival setting

Year: 2015

Type: article

Abstract: To access the calibration of a predictive model in a survival analysis setting, several authors have extended the Hosmer-Lemeshow goodness-of-fit test to survival data. Grønnesby and Borgan developed a test under the proportional hazards assumption, and Nam and D'Agostino developed a nonparametric test that is applicable in a more general survival setting for data with limited censoring. We analyze the performance of the two tests and show that the Grønnesby-Borgan test attains appropriate size in a variety of settings, whereas the Nam-D'Agostino method has a higher than nominal Type 1 error when there is more than trivial censoring. Both tests are sensitive to small cell sizes. We develop a modification of the Nam-D'Agostino test to allow for higher censoring rates. We show that this modified Nam-D'Agostino test has appropriate control of Type 1 error and comparable power to the Grønnesby-Borgan test and is applicable to settings other than proportional hazards. We also discuss the application to small cell sizes.

Source: Statistics in Medicine

DOI: 10.1002/sim.6428

Authors: Olga V. Demler, Nina P. Paynter, Nancy R. Cook

Institutions: Brigham and Women's Hospital, Harvard University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Heart, Lung, and Blood Institute, National Cancer Institute

Awards: G1528515809, Novel Applications of Risk Reclassification Methods in CVD Risk Prediction, TRIAL OF ASPIRIN AND VITAMIN E IN WOMEN, Women's Health Study: Continued Follow-up