Abstract
Cardiovascular research generates a large variety of data including big and small datasets that pose interesting challenges to researchers in biomedical statistics. This paper covers a number of important methodology issues and the solutions that have been applied so far to try to solve them. The first issue is multiple testing and the use of false discovery rates to correct for multiplicity. This is illustrated with an application related to genotyping heart diseases. The second is measuring life extension at present and in the future. The third one is the issue of U and J curves and the questions they present. Finally, this paper studies visit-to-visit blood pressure variability as a predictor of cardiovascular events.