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Recalibration of time-varying covariate Cox model in external validation

Abstract Number:

2165 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Haekyung Jeon-Slaughter (1), Xiaofei Chen (2)

Institutions:

(1) University of Texas Southwestern Medical Center, N/A, (2) N/A, N/A

Co-Author:

Xiaofei Chen  
N/A

First Author:

Haekyung Jeon-Slaughter  
University of Texas Southwestern Medical Center

Presenting Author:

Haekyung Jeon-Slaughter  
University of Texas Southwestern Medical Center

Abstract Text:

External validation is to validate a prediction model using external population data different from the original development cohort and often requires recalibration to preserve the accuracy of the model outcome prediction. Assessing the calibration of Cox model in external validation requires not only visualizing calibration plots but also testing the significant difference between the original model and recalibrated model with regard to the intercept and slopes. However, in the case of the Cox model, there is no intercept (γ 0) to estimate. Alternative to the existing method of testing a logistic regression model (Yvonne et al., 2016), γ0+ γ1 (Xβ ̂ ) for recalibrating and testing γ0=0 and γ1=1, we conducted a log-likelihood test of two time-varying covariate Cox models-the original model (γ1=1) vs a recalibrated model (γ1 = γ ̃) where γ ̃ is a new estimated coefficient of Cox model on the linear predictor Xβ ̂ (original development cohort) from the external cohort data. The study exemplifies these methods to externally validate the Veterans Affairs (VA) women cardiovascular disease (CVD) risk score to non-Veteran women-civilians and active military service members.

Keywords:

External validation|Recalibration|Women |Cardiovascular Disease| Risk Score|Veterans

Sponsors:

Health Policy Statistics Section

Tracks:

Miscellaneous

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