Analysis of 24-hour blood pressure data.
Blood pressure is not constant over the course of a 24-h period, but exhibits a predictable and characteristic rise and decline during the day. Although the general shape of this pattern is similar from patient to patient, the knowledge of an individual's blood pressure at one or two points on this curve is of no predictive value in estimating the remainder of the curve. Since critical events are associated with both the maximum and minimum blood pressures that an individual experiences, a characterization of this curve can be very important. The development of antihypertensive agents historically presumed that the reduction in blood pressure associated with therapy would be, if not constant, at least adequate throughout the entire period. However, with the advent of less frequent dosing, the importance of assuring that blood pressure was adequately controlled over the 24-h period became important. This created interest in two basic types of comparisons. One is the comparison of dosing regimens, e.g., comparing a once-a-day regimen with a twice-a-day regimen. The second is a comparison of two therapies with the same regimen; for example, two doses designed to be administered twice a day. The shape of the curves in the first case is inherently different, whereas they have a similar configuration in the second. Many techniques have been attempted, but few recommended for these types of comparisons. Examples include time series, the use of composite indices, univariate statistical procedures, multivariate procedures, and mathematical characterization of the curves with subsequent comparison of model parameters. This presentation will provide a background and overview of several of these methodologies and their relative utility.
Rodda
B
B
Schering-Plough Research Institute, Kenilworth, New Jersey, USA.
Hajian
G
G
Tsai
K T
KT
Mellars
L
L
Capece
L
L
D001794:Blood Pressure / Q000502:physiology*; D018660:Blood Pressure Monitoring, Ambulatory; D002940:Circadian Rhythm / Q000502:physiology*; D003627:Data Interpretation, Statistical; D005583:Fourier Analysis; D006801:Humans; D013223:Statistics as Topic / Q000379:methods*