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Table 10 Bias/sensitivity analyses for measurement error correction

From: Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology

A. When should measurement error bias/sensitivity analyses be conducted?
 1. When assessment of the observed diet-disease associations was estimated using a crude instrument of dietary intake such as a FFQ.
 2. Essential when the study report aims to translate their findings into policy decision-making actions for a variety of stakeholders.
B. How does one select a method to conduct a model measurement error bias/sensitivity analysis?
 1. Aim to balance realistic modelling with practicality of conducting the modelling (e.g. availability of software).
 2. Report the measurement error bias/sensitivity analyses as transparently as possible, giving clear details of what was done and the assumptions made.
 3. Make the statistical analysis code used to conduct these measurement error bias/sensitivity analyses available either as supplementary web material or by publishing it as an appendix to the main report.
C. How does one assign values to the parameters of the model?
 1. Assign values based on the latest information from available data such as internal calibration sub-studies or external calibration studies with a similar design.
 2. Choose a range of plausible values in order to assess the impact on the overall findings of a range of scenarios.
 3. Evaluate the impact of departures from the assumptions of the classical measurement error model (such as correlated errors between the dietary instruments used or non-differential measurement error).
D. How does one present and interpret the measurement error bias/sensitivity analysis?
 1. Present the results in the form of a table or figure where it is possible for the reader to see the complete set of analyses performed.
 2. Quantify the direction of the bias based on departures from the classical measurement error model on the overall study findings (e.g. are the observed diet-disease associations likely to be over- estimated or under-estimated?).
 3. Describe the implications in light of the measurement error bias/sensitivity analysis (are the policy decisions changed or toned-down in light of these findings?).