Bayesian statistical methods are widely being used in a variety of fields, such as biomedical research, public health, social sciences and banking, among others. Researchers may fit Bayesian models in SAS using the PROC MCMC procedure. However, default options offered to researchers for sampling algorithms for Markov Chains in this procedure are based on the Metropolis sampling method and can sometimes be inefficient and take a high number of iterations to converge. The Hamiltonian Monte Carlo algorithm is an alternative sampling methods that usually converges faster than Metropolis approaches. We show how to use this algorithm in PROC MCMC using the ALG statement and compare its performance with the default option using clustered data to fit a hierarchical logistic regression model.