Bias and Variance Evaluation of QT Interval Correction Methods

Abstract

There is an increasing regulatory emphasis on assessing drug-induced QT interval prolongation. Since QT interval is correlated with heart rate (HR), assessment of drug-induced QT interval prolongation should be made at a standardized HR, resulting in the need to correct QT interval (QTc) for HR. This study investigates the statistical properties of QTc intervals using individual based correction (IBC), population based correction (PBC), or fixed correction (FC) methods under both the linear and log-linear regression models for the QT–RR relationship where RR is the time elapsing between two consecutive heart beats (inversely related to HR through RR = 60/HR). This study shows that QTc intervals using PBC and FC methods are conditionally biased. The QTc interval using the IBC method is conditionally unbiased under the linear regression model, but is conditionally biased under the log-linear regression model. It also shows that under both the linear and log-linear regression models, the conditional variances of the QTc intervals using the three correction methods satisfy the order FC ≤ PBC ≤ IBC. Suggestions for analyzing QT intervals based on these findings are discussed.

Key Words:

• Conditional inferences
• Fixed correction
• Individual based correction
• Population based correction
• QT interval
• QT interval correction

Notes

Unit for QT and RR intervals: millisecond

Unit for QTc interval: millisecond