Interrupted Time Series Analysis Techniques in Pharmacovigilance

Abstract

This thesis considers an approach to evaluate the effectiveness of risk communications for prescription drugs by performing interrupted time series analysis of prescription drug volumes prior to and after the risk communication date.

The paper presents methods for detecting change in the presence of autocorrelation and techniques to reduce bias in estimation. Statistical results and data plots are presented for 63 data series. Size and power of the statistical techniques are considered, and a correspondence analysis between these statistical techniques and a small group of physicians is performed.

The methods considered in this thesis correspond weakly with physician sentiment, and exhibit inflated type I errors in the presence of significant autocorrelation.