Bayesian variable selection based on clinical relevance weights in small sample studies-Application to colon cancer.

ABSTRACT: Using clinical data to model the medical decisions behind sequential treatment actions raises methodological challenges. Physicians often have access to many covariates that may be used when making sequential treatment decisions for individual patients. Statistical variable selection methods may help finding which of these variables are used for this decision in everyday practice. When the sample size is not large, Bayesian variable selection methods can address this setting and allow for expert information to be incorporated into prior distributions. Motivated by clinical practice data involving repeated dose adaptation for Irinotecan in colorectal metastatic cancer, we propose a modification of the stochastic search variable selection (SSVS) method, which we call weight-based SSVS (WBS).

SUBMITTER: Boulet S 

PROVIDER: S-EPMC6590366 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature