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Simulating cancer progression events: state-based model transitions with flexible correlation

Abstract Number:

2313 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Elizabeth Handorf (1), J. Robert Beck (2), Daniel Geynisman (2)

Institutions:

(1) Rutgers University, Rutgers Cancer Institute of New Jersey, N/A, (2) Fox Chase Cancer Center, N/A

Co-Author(s):

J. Robert Beck  
Fox Chase Cancer Center
Daniel Geynisman  
Fox Chase Cancer Center

First Author:

Elizabeth Handorf  
Rutgers University, Rutgers Cancer Institute of New Jersey

Presenting Author:

Elizabeth Handorf  
Rutgers University, Rutgers Cancer Institute of New Jersey

Abstract Text:

In advanced cancers, patients undergo multiple different lines of therapies, switching treatments when their disease progresses. Using longitudinal data sources, such as those obtained via Electronic Health Records (EHRs), researchers can study effects of common therapy sequences. New models for studying therapy sequence are needed to inform clinical decisions when prospective trial data is absent; however, to evaluate the performance of such models, a method for simulating EHR-like longitudinal data is required. Here, we develop a method for simulating paths through a state-based model. This method allows transition times to depend on treatments and observed covariates and incorporates within-patient correlation. This is important as patients' outcomes across states may be dependent due to difficult-to-quantify factors (e.g., disease aggressiveness, response to prior therapy, evolution of the mutational landscape). We propose to introduce within-patient correlation using a copula. This flexible class of multivariate models allows for researchers to generate outcomes with a range of within-patient correlation structures: Gaussian, T, and Clayton's copula will be considered.

Keywords:

simulation methods|copula|cancer applications|multi-state models|survival|EHR

Sponsors:

ENAR

Tracks:

Miscellaneous

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