The average percent change (APC) is often used to measure temporal trends. Under the assumption of linearity on the logarithmic scale, the APC is estimated by using a generalized linear model. A serious limitation of least-squares type estimators is their sensitivity to outliers. The goal of this study is two-fold: firstly, we propose a robust and easy-to-compute measure of the temporal trend based on the median of the rates (median percent change - MPC), rather than their mean; secondly, we investigate the performance of several models for estimating the rate of change when some of the most common model assumptions are violated. We provide some general guidance on the practices of the estimation of temporal trends when using different models under different circumstances. Also, we analyzed an English cancer registration dataset to illustrate the proposed method. The MPC provides a robust alternative to APC. We believe that, as a good practice, both APC and MPC should be presented when sensitivity issues arise. The modelling of data subsets, in any case, should reflect the peculiarity of the process from where the dataset has originated
