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Related Experiment Video

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Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

A targeted simulation-extrapolation method for evaluating biomarkers based on new technologies in precision medicine.

Dong Wang¹, Sue-Jane Wang², Joshua Xu¹

¹Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, FDA, Jefferson, Arkansas, USA.

Pharmaceutical Statistics

|December 22, 2021

Summary

This study introduces a statistical method to predict biomarker performance across different error rates, aiding precision medicine development. The approach helps plan future trials by projecting results from varying technologies and applications.

Keywords:

SIMEX biomarker measurement error misclassification next generation sequencing

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Area of Science:

Biostatistics
Biomarker Discovery
Precision Medicine

Background:

Novel biomarkers are crucial for precision medicine, but technologies like next-generation sequencing have inherent misclassification rates.
These misclassification rates pose challenges in applications such as liquid biopsy for early tumor detection.
Current biomarker development is often limited to specific technologies and settings, hindering broader application planning.

Purpose of the Study:

To develop a statistical method for projecting biomarker performance metrics under varying misclassification rates.
To enable informed planning of biomarker development and clinical trials by simulating different technological or application scenarios.

Main Methods:

An extended simulation extrapolation (SIMEX) approach was developed to project biomarker performance.
Simulation studies were conducted using logistic regression and proportional hazards models.
The method was validated using a lung cancer dataset with two gene panel biomarkers.

Main Results:

The proposed SIMEX-based method accurately projects biomarker performance when switching between different technology or application settings.
The method demonstrated good precision in simulations for logistic regression and proportional hazards models.
Analysis of a lung cancer dataset confirmed the feasibility of inferring implications across various scenarios with limited data.

Conclusions:

The extended SIMEX method provides a robust tool for projecting biomarker performance across diverse misclassification rates.
This approach facilitates strategic planning for biomarker development and clinical trials in precision medicine.
It allows researchers to assess the potential impact of different technologies and applications even with limited experimental data.