Likelihood Ratio Test Method for Multiple Medical Devices Comparison Using Multiple-Site Data with Continuous Outcomes.

Abstract

With a record number of medical devices approved or cleared, it is important to understand the performance of devices once they are on the market. Using data from multiple medical devices and multiple sites, the problem of interest in this article is to detect if a device is a signal; that is, if a device performs significantly different from other devices of the same class, when the outcome of interest is a continuous variable. We develop a normal likelihood ratio test (LRT) method, henceforth referred to as normal-LRT, by incorporating sample size information into the methodological framework, to detect device signals using multi-site and multi-device data. It is shown via extensive simulation that the proposed method controls the type-I error and false discovery rate (FDR), while having good power and sensitivity. This method is applied to a hypothetical case study, in which 6 medical devices of the same class are compared. The normal-LRT method can be considered as a tool for device signal detection using data from multi-site and multi-device when the outcome of interest is a continuous measurement.