Measurement error in continuous endpoints in randomised trials: problems and solutions

TL;DR

This paper investigates the effects of measurement error in continuous endpoints in randomized trials and proposes correction methods using external calibration samples to improve statistical inference.

Contribution

It introduces new correction estimators for different types of measurement error and provides software implementation for improved analysis in trials with error-prone endpoints.

Findings

Ignoring measurement error can bias treatment effect estimates for systematic and differential errors.

Using external calibration samples reduces bias and improves inference, especially when measurement error is non-classical.

The proposed methods are implemented in an R package, facilitating practical application.

Abstract

In randomised trials, continuous endpoints are often measured with some degree of error. This study explores the impact of ignoring measurement error, and proposes methods to improve statistical inference in the presence of measurement error. Three main types of measurement error in continuous endpoints are considered: classical, systematic and differential. For each measurement error type, a corrected effect estimator is proposed. The corrected estimators and several methods for confidence interval estimation are tested in a simulation study. These methods combine information about error-prone and error-free measurements of the endpoint in individuals not included in the trial (external calibration sample). We show that if measurement error in continuous endpoints is ignored, the treatment effect estimator is unbiased when measurement error is classical, while Type-II error is increased at a given sample size. Conversely, the estimator can be substantially biased when measurement error is systematic or differential. In those cases, bias can largely be prevented and inferences improved upon using information from an external calibration sample, of which the required sample size increases as the strength of the association between the error-prone and error-free endpoint decreases. Measurement error correction using already a small (external) calibration sample is shown to improve inferences and should be considered in trials with error-prone endpoints. Implementation of the proposed correction methods is accommodated by a new software package for R.