Print  Close

A Partially Linear Dynamic Single-Index Cox Regression Model

Abstract Number:

3811 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Yiwei Li (1), Yuyan Wang (1), Mengling Liu (1)

Institutions:

(1) New York University Grossman School of Medicine, N/A

Co-Author(s):

Yuyan Wang  
New York University Grossman School of Medicine
Mengling Liu  
New York University Grossman School of Medicine

First Author:

Yiwei Li  
New York University Grossman School of Medicine

Presenting Author:

Yiwei Li  
N/A

Abstract Text:

In examining multiple time-dependent exposures in relation to time-to-event outcomes, the classical Cox regression model is limited in use due to its strong linearity assumption. While several Cox regression models have been developed to bypass this assumption, they overlook the temporal variations in the exposure mixture's impact on log hazard. To bridge this gap, we propose a novel Partial Linear Dynamic Single-Index Cox regression model. This model combines the time varying impact of exposure on the survival risk through an unknown nonparametric single-index function with the linear effects of additional covariates. We employed regression spline tensor basis to approximate the single-index function and propose a profile optimization algorithm to estimate the model. We also present LRT to compare our proposed model with the simple time-dependent cox model. After establishing the large sample properties for the proposed estimator, we evaluate its finite-sample performance under extensive simulation scenarios. We exemplify our model's application with the NYU CHES cohort.

Keywords:

environmental exposure |time-dependent exposure|exposure mixture|cox regression| |

Sponsors:

Section on Statistics in Epidemiology

Tracks:

Statistical Issues in Environmental Epidemiology

Can this be considered for alternate subtype?

Yes

Are you interested in volunteering to serve as a session chair?

No

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

I understand