Table 1.

Examples of measurement error corrections, models and bias analyses

Measure with error	Methods	Applied example	Ref.
Serum measurement of Vitamin D	Regression calibration	To account for measurement error, serum measurements were calibrated to assay measurements (the preferred reference standard) using data from an earlier study containing measurements of both assay and serum of Vitamin D	73
Smoking status reported by health care providers	Multiple imputation	Clinical assessments of smoking status were available only for an internal validation subgroup. Multiple imputation was used to account for the potential measurement error in health care provider-reported smoking status for the remaining patients	74
Low-density lipoprotein cholesterol (LDL-c) measurement	SIMEX	Effect estimate of LDL-c on coronary artery disease was corrected for bias in the error contaminated LDL-c measurements using the Simulation Extrapolation (SIMEX) method	75
Self-reported dietary fibre intake	Regression calibration	Repeated measurement of error-prone self-reported dietary feedback was used to estimate within-person variation to correct for measurement error via regression calibration	76
Diagnostic tests for pulmonary tuberculosis (PTB)	Latent class analysis	Results from six diagnostic tests for PTB were available which were considered error-contaminated measurements of PTB infection. A latent class model was developed to estimate diagnostic accuracy in the absence of a gold standard	77
Self-reported influenza vaccination status	Quantitative bias analysis	Monte Carlo simulations were performed to evaluate the impact of measurement error in the relation between vaccination status of pregnant women and preterm birth, assuming a range of plausible accuracy values for self-reported influenza vaccination	78