  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.
