An individual's baroreflex sensitivity is typically described by the relationship between serial manipulations in systolic blood pressure and the changes in pulse interval. Although this experimental approach is essentially within-subjects in nature, least squares regression (LSR) analysis is typically employed by researchers to derive sensitivity slopes (gains) for individual subjects. These individual gains are then pooled as summary measures for various samples or experimental conditions. We highlight that the underlying assumption for LSR of case independence is violated with such an approach, resulting in possible estimation biases and compromised statistical power. Using a typical data set, we introduce more appropriate analyses based on the linear mixed model, which takes into account the correlated nature of the data at the individual subject level. We encourage researchers to consider the linear mixed model approach because it is more efficient, in that the whole data set is analysed in one step, is associated with less bias and results in greater statistical power compared with conventional analyses of baroreflex sensitivity for samples of subjects.