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.
Highlights
In randomised controlled trials, continuous endpoints are often measured with some degree of error
In the case that the error in Y ∗ is different for the two treatment groups, it is assumed that the external calibration sample is in the form of a small pilot study where both treatments are allocated (i.e., Y ∗ and Y are both measured after assignment of X)
For the corrected estimator and θ1 = 1.05 or θ1 = 1.25 and RY2 ∗,Y = 0.8, percentage bias, empirical standard error (EmpSE) and Squared root of Mean Squared Error (SqrtMSE) of βY are reasonably small for K ≥ 10
Summary
Continuous endpoints are often measured with some degree of error. L Nab, RHH Groenwold, PMJ Welsing, M van Smeden energy intake [5]) In these examples, the continuous endpoint measurements contain error in the sense that the recorded endpoints do not unequivocally reflect the endpoint one aims to measure. We provide an exploration of problems and solutions for measurement error in continuous trial endpoints. For illustration of the problems and solutions for measurement error in continuous endpoints we consider one published trial that examined the efficacy and tolerability of lowdose iron-supplements during pregnancy [16].
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