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Measurement error in continuous endpoints in randomised trials: Problems and solutions
Author(s) -
Nab L.,
Groenwold R.H.H.,
Welsing P.M.J.,
Smeden M.
Publication year - 2019
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8359
Subject(s) - estimator , sample size determination , observational error , type i and type ii errors , statistics , calibration , confidence interval , error detection and correction , inference , computer science , nominal level , mean squared error , standard error , point estimation , mathematics , algorithm , artificial intelligence
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.