Instrumental variable approach to covariate measurement error in generalized linear models

Abarin, Taraneh; Wang, Liqun

doi:10.1007/s10463-010-0319-0

Cited by 9 publications

(8 citation statements)

References 33 publications

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“…This can also be visualized in Figure , where the variability of the OLS calibration curve is greater than that of GGLM‐ID under different realizations of the same data. For further improvements, the best approach would be to extend the GGLM framework to a heteroscedastic errors‐in‐variables method . The validation of LIC measurements by MRI relaxometry is an ongoing field of study …”

Section: Discussionmentioning

confidence: 99%

A novel gamma GLM approach to MRI relaxometry comparisons

Kapre

Zhou

et al. 2020

Magnetic Resonance in Med

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Purpose:To demonstrate that constant coefficient of variation (CV), but nonconstant absolute variance in MRI relaxometry (T 1 , T 2 , R 1 , R 2 ) data leads to erroneous conclusions based on standard linear models such as ordinary least squares (OLS).We propose a gamma generalized linear model identity link (GGLM-ID) framework that factors the inherent CV into parameter estimates. We first examined the effects on calculations of contrast agent relaxivity before broadening to other applications such as analysis of variance (ANOVA) and liver iron content (LIC). Methods: Eight models including OLS and GGLM-ID were initially fit to data obtained on sulfated dextran iron oxide (SDIO) nanoparticles. Both a resampling simulation on the data as well as two separate Monte Carlo simulations (with and without concentration error) were performed to determine mean square error (MSE) and type I error rate. We then evaluated the performance of OLS/GGLM-ID on R 1 repeatability and LIC data sets. Results: OLS had an MSE of 4-5× that of GGLM-ID as well as a type I error rate of 20-30%, whereas GGLM-ID was near the nominal 5% level in the relaxivity study.Only OLS found statistically significant effects of MRI facility on relaxivity in an R 1 repeatability study, but no significant differences were found in a resampling, whereas GGLM was more consistent. GGLM-ID was also superior to OLS for modeling LIC. Conclusions: OLS leads to erroneous conclusions when analyzing MRI relaxometry data. GGLM-ID factors in the inherent CV of an MRI experiment, leading to more reproducible conclusions. K E Y W O R D S coefficient of variation, gamma GLM, MRI relaxometry, relaxivity, repeatability | 1593 KAPRE Et Al.

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Section: Discussionmentioning

confidence: 99%

A novel gamma GLM approach to MRI relaxometry comparisons

Kapre

Zhou

et al. 2020

Magnetic Resonance in Med

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“…Chin, 2010;Arlot & Celisse, 2010). In addition, the OnPLS and IVE methods (Abarin & Wang, 2012;Hardin & Carroll, 2003) can be used to help compensate for the impacts of measurement error on overfitting. Such approaches are particularly important given that high-dimensional, low sample size data sets often used to justify PLS-PM are particularly prone to overfitting and accompanying Type I error inflation (Fan, Guo, & Hao, 2012;Forstmeier & Schielzeth, 2011;Subramanian & Simon, 2013).…”

Section: Is Pls-pm Capable Of Validating Measurement Models?mentioning

confidence: 99%

Reflections on Partial Least Squares Path Modeling

McIntosh

Edwards

Antonakis

2014

Organizational Research Methods

182

125

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The purpose of the present article is to take stock of a recent exchange in Organizational Research Methods between critics (Rönkkö & Evermann, 2013) and proponents (Henseler et al., 2014) of partial least squares path modeling (PLS-PM). The two target articles were centered around six principal issues, namely whether PLS-PM: (1) can be truly characterized as a technique for structural equation modeling (SEM); (2) is able to correct for measurement error; (3) can be used to validate measurement models; (4) accommodates small sample sizes; (5) is able to provide null hypothesis tests for path coefficients; and (6) can be employed in an exploratory, model-building fashion. We summarize and elaborate further on the key arguments underlying the exchange, drawing from the broader methodological and statistical literature in order to offer additional thoughts concerning the utility of PLS-PM and ways in which the technique might be improved. We conclude with recommendations as to whether and how PLS-PM serves as a viable contender to SEM approaches for estimating and evaluating theoretical models.

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“…Instrumental variable approach has been used by other authors to deal with errors-invariables problem in general non-linear models, for example, Amemiya (1985), Amemiya (1990), Schennach (2007), Hsiao (2011), andAbarin &Wang (2012). In particular, Schennach (2007) and Wang & Hsiao (2011) showed that the non-linear measurement error models are generally identified when instrumental variables are available.…”

Section: Introductionmentioning

confidence: 99%

Instrument Assisted Regression for Errors in Variables Models with Binary Response

Wang

2014

Scandinavian J Statistics

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We study errors-in-variables problems when the response is binary and instrumental variables are available. We construct consistent estimators through taking advantage of the prediction relation between the unobservable variables and the instruments. The asymptotic properties of the new estimator are established, and illustrated through simulation studies. We also demonstrate that the method can be readily generalized to generalized linear models and beyond. The usefulness of the method is illustrated through a real data example.

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Instrumental variable approach to covariate measurement error in generalized linear models

Cited by 9 publications

References 33 publications

A novel gamma GLM approach to MRI relaxometry comparisons

A novel gamma GLM approach to MRI relaxometry comparisons

Reflections on Partial Least Squares Path Modeling

Instrument Assisted Regression for Errors in Variables Models with Binary Response

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