Many endocrine systems are regulated by pulsatile hormones – hormones that are secreted intermittently in boluses rather than continuously over time. To study pulsatile secretion, blood is drawn every few minutes for an extended period. The result is a time series of hormone concentrations for each individual. The goal is to estimate pulsatile hormone secretion features such as frequency, location, duration, and amount of pulsatile and non-pulsatile secretion and compare these features between groups. Various statistical approaches to analyzing these data have been proposed, but validation has generally focused on one hormone. Thus, we lack a broad understanding of each method's performance. By using simulated data with features seen in reproductive and stress hormones, we investigated the performance of three recently developed statistical approaches for analyzing pulsatile hormone data and compared them to a frequently used deconvolution approach. We found that methods incorporating a changing baseline modeled both constant and changing baseline shapes well; however, the added model flexibility resulted in a slight increase in bias in other model parameters. When pulses were well defined and baseline constant, Bayesian approaches performed similar to the existing deconvolution method. The increase in computation time of Bayesian approaches offered improved estimation and more accurate quantification of estimation variation in situations where pulse locations were not clearly identifiable. Within the class of deconvolution models for fitting pulsatile hormone data, the Bayesian approach with a changing baseline offered adequate results over the widest range of data. Copyright © 2013 John Wiley & Sons, Ltd.