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Mendelian randomization (MR) is a framework for assessing causal inference using cross-sectional data in combination with genetic information. This paper summarizes statistical methods commonly applied and strait forward to use for conducting MR analyses including those taking advantage of the rich dataset of SNP-trait associations that were revealed in the last decade through large-scale genome-wide association studies. Using these data, powerful MR studies are possible. However, the causal estimate may be biased in case the assumptions of MR are violated. The source and the type of this bias are described while providing a summary of the mathematical formulas that should help estimating the magnitude and direction of the potential bias depending on the specific research setting. Finally, methods for relaxing the assumptions and for conducting sensitivity analyses are discussed. Future researches in the field of MR include the assessment of non-linear causal effects, and automatic detection of invalid instruments.
Mendelian randomization (MR) is a framework for assessing causal inference using cross-sectional data in combination with genetic information. This paper summarizes statistical methods commonly applied and strait forward to use for conducting MR analyses including those taking advantage of the rich dataset of SNP-trait associations that were revealed in the last decade through large-scale genome-wide association studies. Using these data, powerful MR studies are possible. However, the causal estimate may be biased in case the assumptions of MR are violated. The source and the type of this bias are described while providing a summary of the mathematical formulas that should help estimating the magnitude and direction of the potential bias depending on the specific research setting. Finally, methods for relaxing the assumptions and for conducting sensitivity analyses are discussed. Future researches in the field of MR include the assessment of non-linear causal effects, and automatic detection of invalid instruments.
Disregarded Measurement Uncertainty Contributions and Their Magnitude in Measuring Plasma Glucose
(2020)
Background:
Each measurement is subject to measurement uncertainty (MU). Consequently, each measurement of plasma glucose concentration used for diagnosis and monitoring of diabetes mellitus (DM) is affected. Although concepts and methods of MU are well established in many fields of science and technology, they are presently only incompletely implemented by medical laboratories, neglecting MU of target values of internal quality control (IQC) materials.
Methods:
An empirical and practical approach for the estimation of MU based on the analysis of routine IQC using control samples with assigned target values is presented. Its feasibility is demonstrated exemplarily by analyzing IQC data from one year obtained for glucose employing the hexokinase method with IQC of two different concentrations.
Results:
Combined relative extended (k = 2) MU comprising bias, coefficient of variation (CV), and MU of the target values assigned to control materials were about 9% with a lower (~ 56 mg/dL; ~3.1 mmol/L) and 8% with a higher (~ 346 mg/dL; ~19.2 mmol/L) concentration sample, analyzing IQC of one year from three different devices.
Conclusions:
Estimation of MU in this study is quite reliable due to the large number of IQC data from one year. The MU of the target values of the commercial control material in this study was considerably larger than other MU contributions, ie, standard deviation and bias. In the future, the contribution of MU of commercial IQC should be addressed more carefully and technologies to measure glucose should be geared toward smaller MU possible, as needed, especially for glucose concentration measurements in diagnosis and management of DM.