Statistical power analysis for hemodynamic cardiovascular safety pharmacology studies in beagle dogs

J Pharmacol Toxicol Methods. 2004 Sep-Oct;50(2):121-30. doi: 10.1016/j.vascn.2004.03.009.

Abstract

Introduction: We studied the statistical power of a replicated Latin square design where eight animals each receive a vehicle control and three dose levels of a drug on four separate dosing days. Cardiovascular parameters evaluated in the study were systolic arterial pressure, diastolic arterial pressure, left ventricular heart rate, and dP/dt(max).

Methods: Observations were simulated based on historical data and drug response profiles from cardiovascular safety pharmacology studies conducted at Lilly Research Laboratories. Statistical analysis for treatment effects was performed using a linear mixed model. Monotonicity of dose response was examined using sequential linear trend tests based on ordinal spacing of dose levels.

Results: The replicated Latin square design for cardiovascular safety pharmacology studies is shown to have at least an 80% power of detecting changes from control of at least a 10% increment in systolic and diastolic pressure and a 15% increment in heart rate and dP/dt(max). The power is not sensitive to the shape of dose response profile over time.

Discussion: Several unique features of our statistical power evaluation include the comparison of different covariance structures and drug response profiles. The procedure can also be applied to future power evaluations of other cardiovascular parameters, such as the QT interval, and the loss of statistical power due to missing observations.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Blood Pressure / drug effects
  • Data Interpretation, Statistical*
  • Dogs
  • Drug Evaluation, Preclinical / methods
  • Drug Evaluation, Preclinical / statistics & numerical data*
  • Electrocardiography
  • Heart Rate / drug effects
  • Linear Models
  • Pharmaceutical Preparations / administration & dosage
  • Research Design / statistics & numerical data*

Substances

  • Pharmaceutical Preparations