Regression analysis applied to PVC histories: A Statistical procedure for evaluating antiarrhythmic drug efficacy

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Abstract
Suppression of premature ventricular contractions (PVCs) is one of the goals of antiarrhythmic therapy. In a clinical trial, however, it may be difficult to distinguish antiarrhythmic drug effect from spontaneous variation in PVCs. We propose the application of linear regression to PVC histories to ascertain drug effect in individual patients. The model determines which variables are important in explaining a patient's PVCs. One such variable indicates the presence or absence of the drug; the model determines whether the drug has an effect on the patient's PVCs, while compensating for the other explanatory variables. In addition to determining the statistical significance of any drug effect, the model estimates the strength of the effect for each patient. We demonstrate the method with data from a three-day clinical trial which used 24-hour Holter monitoring. The method is flexible and can be modified to apply to any clinical study design. It allows for inferences concerning populations and subpopulations of patients.