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

Tingting Hu1, Jianjin Xu2, Lan Huang1

  • 1U.S. Food and Drug Administration, 10903, New Hampshire Avenue, Building 66, Silver Spring, MD, 20993, USA.

Therapeutic Innovation & Regulatory Science
|June 13, 2020
PubMed
Summary
This summary is machine-generated.

A new normal likelihood ratio test (LRT) method effectively detects medical device signals using multi-site data. This statistical tool helps identify devices performing significantly differently from others in the same class.

Keywords:
False discovery rateMedical device comparisonMultiple sitesMultiple studiesType-I error

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Area of Science:

  • Biostatistics
  • Medical Device Regulation
  • Health Technology Assessment

Background:

  • Increasing number of medical device approvals necessitates post-market surveillance.
  • Understanding real-world device performance is crucial for patient safety.
  • Detecting outlier devices within a class is a key challenge.

Purpose of the Study:

  • To develop a statistical method for detecting "device signals" using multi-site and multi-device data.
  • To identify medical devices that perform significantly different from others in the same class.
  • To provide a tool for continuous outcome variables in device performance evaluation.

Main Methods:

  • Development of a normal likelihood ratio test (LRT) method, termed normal-LRT.
  • Incorporation of sample size information into the LRT framework.
  • Application to multi-site and multi-device datasets with continuous outcome measures.

Main Results:

  • Extensive simulations demonstrate the normal-LRT method controls Type-I error and False Discovery Rate (FDR).
  • The method exhibits good statistical power and sensitivity in signal detection.
  • A hypothetical case study involving 6 medical devices of the same class was analyzed.

Conclusions:

  • The normal-LRT method is a viable tool for detecting medical device signals.
  • It is suitable for analyzing data from multiple sites and multiple devices.
  • The method is effective when the outcome of interest is a continuous measurement.