Adjustment for the measurement error in evaluating biomarker performances at baseline for future survival outcomes: Time-dependent receiver operating characteristic curve within a joint modelling framework

Ruwanthi Kolamunnage-Dona,Adina Najwa Kamarudin

doi:10.1177/2632084320972257

Abstract

The performance of a biomarker is defined by how well the biomarker is capable to distinguish between healthy and diseased individuals. This assessment is usually based on the baseline value of the biomarker; the value at the earliest time point of the patient follow-up, and quantified by ROC (receiver operating characteristic) curve analysis. However, the observed baseline value is often subjected to measurement error due to imperfect laboratory conditions and limited machine precision. Failing to adjust for measurement error may underestimate the true performance of the biomarker, and in a direct comparison, useful biomarkers could be overlooked. We develop a novel approach to account for measurement error when calculating the performance of the baseline biomarker value for future survival outcomes. We adopt a joint longitudinal and survival data modelling formulation and use the available longitudinally repeated values of the biomarker to make adjustment of the measurement error in time-dependent ROC curve analysis. Our simulation study shows that the proposed measurement error-adjusted estimator is more efficient for evaluating the performance of the biomarker than estimators ignoring the measurement error. The proposed method is illustrated using Mayo Clinic primary biliary cirrhosis (PBC) study.

Highlights

Due to current trends in medical practice towards personalised medicine, biomarkers have grown in importance in clinical studies
Due to computational simplicity of ðU^0Þlme, we considered it as a potential estimator for the time-dependent receiver operating characteristics (ROC) curve analysis, but ðU^0Þlme has not been previously used as an estimator of its own for the time-dependent ROC curve.[11]
We proposed a novel utility of the joint modelling framework within the theory of timedependent ROC curve analysis by developing a more efficient estimator that links the risk of failure and baseline biomarker

Summary

Introduction

Due to current trends in medical practice towards personalised medicine, biomarkers have grown in importance in clinical studies. More and more studies are conducted to discover biomarkers that can accurately signal a clinical endpoint, e.g. measures of liver function such as prothrombin index as indicators of liver fibrosis,[1] and in clinical practice, rapid tests of biomarkers hold the promise of prompt diagnosis of diseases for an improved outcome, e.g. sepsis.[2] In this article, we refer the term “biomarker” to a single biomarker such as prothrombin index or to a composite risk score. Due to imperfect laboratory conditions such as operator error, contamination, variable storage conditions, and limited machine precision, biomarkers are often subjected to substantial error in studies.[3] Failing to adjust for such measurement error may hinder the explanatory power of the biomarker, and in a direct comparison, Department of Health Data Science, Institute of Population Health, University of Liverpool, Liverpool, UK

Objectives

Methods

Conclusion