Stem cell transplant is often considered the last hope for the survival for many cancer patients. The CD34+ cell content of a collection of stem cells has appeared as the most reliable indicator of the quantity of desired cells in a peripheral blood stem cell harvest and is used as a surrogate measure of the sample quality. Factors predicting the yield of CD34+ cells in a collection are not yet fully understood. Throughout the literature, there has been conflicting evidence with regards to age, gender, disease status, and prior radiation. In addition to the factors that have already been explored, we are interested in finding a cancer-chemotherapy interaction and to develop a predictive model to better identify which patients will be good candidates for this procedure. Because the amount of CD34+ cells is highly skewed, most traditional statistical methods are inappropriate without some transformation. A Bayesian generalized regression model was used to explain the variation of CD34+ collected from the sample by the cancer chemotherapy interaction. Missing data was modeled as unknown parameters to include the entire data set in the analysis. Posterior estimates are obtained using Markov chain methods. Posterior distributions identified weight and gender as well as some cancer-chemotherapy interactions as significant factors. Predictive posterior distributions can be used to identify which patients are good candidates for this procedure.