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 sequentially adaptive treatment decisions for individual patients. They typically prioritize certain covariates over others, when looking at patient's characteristics and history, and use their own subjective weights when making treatment decisions. In the small sample size context, Bayesian variable selection methods can help finding which variables are really used in everyday practice 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). We use clinical relevance weights elicited from physician experts to construct prior distributions, with the goal to identify the most influential toxicities and other covariates used for dose adjustment. We conduct a simulation study to compare the performance of WBS compared the Lasso and SSVS. The simulations show that WBS has better performance and lower rates of false positives and false negatives than the other methods but depends strongly on the covariate weights.