Poor adherence with antihypertensive therapies is a major factor in the low rates of blood pressure control among people with hypertension. Patient adherence is influenced by a large number of interacting factors but their exact impact is not well understood, partly because it is difficult to measure adherence. Longitudinal prescription data can be used as a measure of drug supply and are particularly useful to identify interruptions and changes of treatment. Obtaining a medicine does not ensure its use; however, it has been established that continuous collection of prescription medications is a useful marker of adherence. We found 20 studies published in the last 10 years that used large prescription databases to investigate adherence with antihypertensive therapies. These were assessed in terms of patient selection, the definition of the adherence outcome(s), and statistical modeling. There was large variation between studies, limiting their comparability. Particular methodological problems included: the failure to identify an inception cohort, which ensures baseline comparability, in four studies; the exclusion of patients who could not be followed up, which results in a selection bias, in 17 studies; failure to validate outcome definitions; and failure to model the discrete-time structure of the data in all the studies we examined. Although the data give repeated measurements on patients, none of the studies attempted to model patient-level variability. Studies of such observational data have inherent limitations, but their potential has not been fully realized in the modeling of adherence with antihypertensive drugs. Many of the studies we reviewed found high rates of nonadherence to antihypertensive therapies despite differences in populations and methods used. Adherence rates from one database ranged from 34% to 78% at 1 year. Some studies found women had better adherence than men, while others found the reverse. Novel approaches to analyzing data from such databases are required to use the information available appropriately and avoid the problems of bias.