scholarly journals A Method to adjust for measurement error in multiple exposure variables measured with correlated errors in the absence of an internal validation study

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1486
Author(s):  
Alexander K. Muoka ◽  
George O. Agogo ◽  
Oscar O. Ngesa ◽  
Henry G. Mwambi
Keyword(s):  
Sensitivity Analysis ◽  
Measurement Error ◽  
Validation Study ◽  
Real Data ◽  
Epidemiological Studies ◽  
Correlated Errors ◽  
Internal Validation ◽  
Multivariate Method ◽  
Multiple Exposures ◽  
Naive Method

Difficulty in obtaining the correct measurement for an individual’s longterm exposure is a major challenge in epidemiological studies that investigate the association between exposures and health outcomes. Measurement error in an exposure biases the association between the exposure and a disease outcome. Usually, an internal validation study is required to adjust for exposure measurement error; it is challenging if such a study is not available. We propose a general method for adjusting for measurement error where multiple exposures are measured with correlated errors (a multivariate method) and illustrate the method using real data. We compare the results from the multivariate method with those obtained using a method that ignores measurement error (the naive method) and a method that ignores correlations between the errors and true exposures (the univariate method). It is found that ignoring measurement error leads to bias and underestimates the standard error. A sensitivity analysis shows that the magnitude of adjustment in the multivariate method is sensitive to the magnitude of measurement error, sign, and the correlation between the errors. We conclude that the multivariate method can be used to adjust for bias in the outcome-exposure association in a case where multiple exposures are measured with correlated errors in the absence of an internal validation study. The method is also useful in conducting a sensitivity analysis on the magnitude of measurement error and the sign of the error correlation.

scholarly journals A Method to Adjust for Measurement Error in Three Exposures Measured with Correlated Errors in the Absence of Internal Validation Study

2019 ◽  
Author(s):  
Alexander K. Muoka ◽  
George Agogo ◽  
Oscar Ngesa ◽  
Henry Mwambi
Keyword(s):  
Measurement Error ◽  
Validation Study ◽  
Real Data ◽  
Epidemiological Studies ◽  
Correlated Errors ◽  
Exposure Measurement ◽  
Internal Validation ◽  
Exposure Measurement Error ◽  
Correlated Exposures

Abstract Difficulty in obtaining the correct measurement for an individual's long-term exposure is a major challenge in epidemiological studies that investigate the association between exposures and health outcomes. Measurement error in an exposure biases the association between the exposure and a disease outcome. Usually an internal validation study is required to adjust for exposure measurement error; it is challenging if such a study is not available. We proposed a method (trivariate method) that adjusts for measurement error in three correlated exposures in the absence of internal validation study and illustrated the method using real data. We compared the results from the proposed method with those obtained using a method that ignores measurement error and a method that ignores correlations between the errors and true exposures (the univariate method). It was found that ignoring measurement error leads to bias and underestimates the standard error. It was also found that the magnitude of adjustment in the trivariate method is sensitive to the magnitude of measurement error, sign and correlation between the errors. We conclude that the proposed method can be used to adjust for bias in the outcome-exposure association in a case where three exposures are measured with correlated errors in the absence of an internal validation study. The method is useful in conducting a sensitivity analysis on the magnitude of measurement error and the sign of the error correlation.

scholarly journals Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study

Biometrics ◽  
2019 ◽  
Vol 75 (3) ◽  
pp. 927-937 ◽  
Author(s):  
Juned Siddique ◽  
Michael J. Daniels ◽  
Raymond J. Carroll ◽  
Trivellore E. Raghunathan ◽  
Elizabeth A. Stuart ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Measurement Error ◽  
Error Correction ◽  
Validation Study ◽  
Intervention Studies ◽  
Dietary Intervention ◽  
External Validation ◽  
External Validation Study ◽  
Measurement Error Correction

scholarly journals Comparison of duplicate portion and 24 h recall as reference methods for validating a FFQ using urinary markers as the estimate of true intake

2015 ◽  
Vol 114 (8) ◽  
pp. 1304-1312 ◽  
Author(s):  
Laura Trijsburg ◽  
Jeanne H. M. de Vries ◽  
Hendriek C. Boshuizen ◽  
Paul J. M. Hulshof ◽  
Peter C. H. Hollman ◽  
...  
Keyword(s):  
Measurement Error ◽  
Validation Study ◽  
Reference Method ◽  
Bias Error ◽  
Correlated Errors ◽  
Web Based ◽  
Urinary Markers ◽  
Reference Methods ◽  
Duplicate Portion ◽  
Better Than

AbstractAs FFQ are subject to measurement error, associations between self-reported intake by FFQ and outcome measures should be adjusted by correction factors obtained from a validation study. Whether the correction is adequate depends on the characteristics of the reference method used in the validation study. Preferably, reference methods should (1) be unbiased and (2) have uncorrelated errors with those in the FFQ. The aim of the present study was to assess the validity of the duplicate portion (DP) technique as a reference method and compare its validity with that of a commonly used reference method, the 24 h recall (24hR), for protein, K and Na using urinary markers as the unbiased reference method. For 198 subjects, two DP, two FFQ, two urinary biomarkers and between one and fifteen 24hR (web based and/or telephone based) were collected within 1·5 years. Multivariate measurement error models were used to estimate bias, error correlations between FFQ and DP or 24hR, and attenuation factors of these methods. The DP was less influenced by proportional scaling bias (0·58 for protein, 0·72 for K and 0·52 for Na), and correlated errors between DP and FFQ were lowest (protein 0·28, K 0·17 and Na 0·19) compared with the 24hR. Attenuation factors (protein 0·74, K 0·54 and Na 0·43) also indicated that the DP performed better than the 24hR. Therefore, the DP is probably the best available reference method for FFQ validation for nutrients that currently have no generally accepted recovery biomarker.

Correction of Risk Estimates for Measurement Error in Epidemiology

1995 ◽  
Vol 34 (05) ◽  
pp. 503-510 ◽  
Author(s):  
S. A. Bashir ◽  
S. W. Duffy
Keyword(s):  
Risk Factors ◽  
Measurement Error ◽  
Validation Study ◽  
Validation Studies ◽  
Epidemiological Studies ◽  
Study Data ◽  
Risk Estimates ◽  
User Friendly ◽  
Considerable Pressure

Abstract:Epidemiologists are under considerable pressure to acknowledge the presence of measurement error in the determination of risk factors. Repeatability and validation studies are often prescribed in conjunction with epidemiological studies. We describe some practical uses for repeatability and validation study data, in terms of correcting risk estimates for measurement error. Commonly available methods are described, with their advantages and shortcomings. A user-friendly computer program to carry out the analyses described accompanies the paper.

scholarly journals Mendelian randomisation for mediation analysis: current methods and challenges for implementation

2021 ◽  
Author(s):  
Alice R. Carter ◽  
Eleanor Sanderson ◽  
Gemma Hammerton ◽  
Rebecca C. Richmond ◽  
George Davey Smith ◽  
...  
Keyword(s):  
Measurement Error ◽  
Causal Inference ◽  
Mediation Analysis ◽  
Instrumental Variable ◽  
Real Data ◽  
Mendelian Randomisation ◽  
Differential Measurement ◽  
Individual Level ◽  
Differential Measurement Error ◽  
Summary Data

AbstractMediation analysis seeks to explain the pathway(s) through which an exposure affects an outcome. Traditional, non-instrumental variable methods for mediation analysis experience a number of methodological difficulties, including bias due to confounding between an exposure, mediator and outcome and measurement error. Mendelian randomisation (MR) can be used to improve causal inference for mediation analysis. We describe two approaches that can be used for estimating mediation analysis with MR: multivariable MR (MVMR) and two-step MR. We outline the approaches and provide code to demonstrate how they can be used in mediation analysis. We review issues that can affect analyses, including confounding, measurement error, weak instrument bias, interactions between exposures and mediators and analysis of multiple mediators. Description of the methods is supplemented by simulated and real data examples. Although MR relies on large sample sizes and strong assumptions, such as having strong instruments and no horizontally pleiotropic pathways, our simulations demonstrate that these methods are unaffected by confounders of the exposure or mediator and the outcome and non-differential measurement error of the exposure or mediator. Both MVMR and two-step MR can be implemented in both individual-level MR and summary data MR. MR mediation methods require different assumptions to be made, compared with non-instrumental variable mediation methods. Where these assumptions are more plausible, MR can be used to improve causal inference in mediation analysis.

scholarly journals The Effect of Random Error in Exposure Measurement upon the Shape of the Exposure Response

Dose-Response ◽  
2005 ◽  
Vol 3 (4) ◽  
pp. dose-response.0 ◽  
Author(s):  
Kenny S. Crump
Keyword(s):  
Measurement Error ◽  
Measurement Errors ◽  
Epidemiological Data ◽  
Epidemiological Studies ◽  
Random Errors ◽  
Exposure Measurement ◽  
Considerable Uncertainty ◽  
Exposure Response ◽  
Log Normal ◽  
Normally Distributed

Although statistical analyses of epidemiological data usually treat the exposure variable as being known without error, estimated exposures in epidemiological studies often involve considerable uncertainty. This paper investigates the theoretical effect of random errors in exposure measurement upon the observed shape of the exposure response. The model utilized assumes that true exposures are log-normally distributed, and multiplicative measurement errors are also log-normally distributed and independent of the true exposures. Under these conditions it is shown that whenever the true exposure response is proportional to exposure to a power r, the observed exposure response is proportional to exposure to a power K, where K < r. This implies that the observed exposure response exaggerates risk, and by arbitrarily large amounts, at sufficiently small exposures. It also follows that a truly linear exposure response will appear to be supra-linear—i.e., a linear function of exposure raised to the K-th power, where K is less than 1.0. These conclusions hold generally under the stated log-normal assumptions whenever there is any amount of measurement error, including, in particular, when the measurement error is unbiased either in the natural or log scales. Equations are provided that express the observed exposure response in terms of the parameters of the underlying log-normal distribution. A limited investigation suggests that these conclusions do not depend upon the log-normal assumptions, but hold more widely. Because of this problem, in addition to other problems in exposure measurement, shapes of exposure responses derived empirically from epidemiological data should be treated very cautiously. In particular, one should be cautious in concluding that the true exposure response is supra-linear on the basis of an observed supra-linear form.

Evaluating the power of the causal impact method in observational studies of HCV treatment as prevention

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Pantelis Samartsidis ◽  
Natasha N. Martin ◽  
Victor De Gruttola ◽  
Frank De Vocht ◽  
Sharon Hutchinson ◽  
...  
Keyword(s):  
Time Series ◽  
Measurement Error ◽  
Time Series Data ◽  
Temporal Trends ◽  
Epidemiological Studies ◽  
Series Data ◽  
Treatment As Prevention ◽  
Open Problems ◽  
Multiple Characteristics ◽  
Causal Impact

Abstract Objectives The causal impact method (CIM) was recently introduced for evaluation of binary interventions using observational time-series data. The CIM is appealing for practical use as it can adjust for temporal trends and account for the potential of unobserved confounding. However, the method was initially developed for applications involving large datasets and hence its potential in small epidemiological studies is still unclear. Further, the effects that measurement error can have on the performance of the CIM have not been studied yet. The objective of this work is to investigate both of these open problems. Methods Motivated by an existing dataset of HCV surveillance in the UK, we perform simulation experiments to investigate the effect of several characteristics of the data on the performance of the CIM. Further, we quantify the effects of measurement error on the performance of the CIM and extend the method to deal with this problem. Results We identify multiple characteristics of the data that affect the ability of the CIM to detect an intervention effect including the length of time-series, the variability of the outcome and the degree of correlation between the outcome of the treated unit and the outcomes of controls. We show that measurement error can introduce biases in the estimated intervention effects and heavily reduce the power of the CIM. Using an extended CIM, some of these adverse effects can be mitigated. Conclusions The CIM can provide satisfactory power in public health interventions. The method may provide misleading results in the presence of measurement error.

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