The comparison of time series of multivariate data is a long-standing problem in many applications in the clinical domain. We propose two approaches to retrieve from a hospital data warehouse the k patients P1, ..., Pn with a chemotherapy history that is most similar to patient Q: the first is based on warping distance, together with an initial alignment using imputed values. The second is based on the volume of the Kiviat tube. In implementing the Euclidean distance, we investigate the addition of null events to achieve similar cardinality, and dynamic time warping, a widely-used technique in the comparison of time series data. The investigations are based on a real world clinical database.