Simulation–Extrapolation for Bias Correction with Exposure Uncertainty in Radiation Risk Analysis Utilizing Grouped Data

Misumi, Munechika; Furukawa, Kyoji; Cologne, John; Cullings, Harry M.

doi:10.1111/rssc.12225

Cited by 9 publications

(14 citation statements)

References 31 publications

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“…Careful modeling is the goal of individual site-specific analyses that are ongoing (24–27). A fourth limitation is that, although several methods exist for accounting for random dosimetry error, including a Bayesian approach (16) and a simulation-extrapolation approach (28), we used only the method of Pierce et al (6) because our goal was to further understand the results of Grant et al . (1), who employed that method.…”

Section: Discussionmentioning

confidence: 99%

Effect of Heterogeneity in Background Incidence on Inference about the Solid-Cancer Radiation Dose Response in Atomic Bomb Survivors

Cologne¹,

Sugiyama

French³

et al. 2019

Radiation Research

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A recent analysis of solid cancer incidence in the Life Span Study of atomic bomb survivors (Hiroshima and Nagasaki, Japan) found evidence of a nonlinear, upwardly curving radiation dose response among males but not among females. Further analysis of this new and unexpected finding was necessary. We used two approaches to investigate this finding. In one approach, we excluded individual cancer sites or groups of sites from all solid cancers. In the other approach, we used joint analysis to allow for heterogeneity in background-rate parameters across groups of cancers with dissimilar trends in background rates. Exclusion of a few sites led to the disappearance of curvature among males in the remaining collection of solid cancers; some of these influential sites have unique features in their background age-specific incidence that are not captured by a background-rate model fit to all solid cancers combined. Exclusion of a few sites also led to an appearance of curvature among females. Misspecification of background rates can cause bias in inference about the shape of the dose response, so heterogeneity of background rates might explain at least part of the all solid cancer dose-response difference in curvature between males and females. We conclude that analysis based on all solid cancers as a single outcome is not the optimal method to assess radiation risk for solid cancer in the Life Span Study; joint analysis with suitable choices of cancer groups might be preferable by allowing for background-rate heterogeneity across sites while providing greater power to assess radiation risk than analyses of individual sites.

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Section: Discussionmentioning

confidence: 99%

Effect of Heterogeneity in Background Incidence on Inference about the Solid-Cancer Radiation Dose Response in Atomic Bomb Survivors

Cologne¹,

Sugiyama

French³

et al. 2019

Radiation Research

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“…Exposure to radiation is assumed to be subject to a combination of Berkson and classical measurement errors. 22,44…”

Section: Application Of Simexmentioning

confidence: 99%

“…Exposure to radiation is assumed to be subject to a combination of Berkson and classical measurement errors. 22,44 More recently, Yang et al 34 furthered the above-mentioned works by adapting the SIMEX method to multivariate non-parametric measurement error regression models under similar assumptions of measurement error structure and also a SIMEX estimation technique similar to that proposed by Liang and Ren. 13 The simulation results were also quite similar in that the bias associated with the SIMEX estimator was lower and the variance of the SIMEX estimator was higher than that of the naïve estimator-an intuitive explanation to which is given clearly on page 147 of their article.…”

Section: Model-based Application Of Simexmentioning

confidence: 99%

Simulation extrapolation method for measurement error: A review

Sevilimedu

2022

Stat Methods Med Res

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Measurement error is pervasive in statistics due to the non-availability of authentic data. The reasons for measurement error mainly relate to cost, convenience, and human error. Measurement error can result in non-negligible bias due to attenuated estimates, reduced power of statistical tests, and lower coverage probabilities of the coefficient estimators in a regression model. Several methods have been proposed to correct for measurement error, all of which can be grouped into two broad categories based on the underlying model—functional and structural. Functional models provide flexibility and robustness to estimators by placing minimal or no assumptions on the distribution of the mismeasured covariate or by treating them as a fixed entity, as opposed to a structural model which treats the underlying mismeasured covariates as random with a specified structure. The simulation extrapolation method is one method that is used for the partial correction of measurement error in both structural and functional models. Reviews of measurement error correction techniques are available in the literature. However, none of the previously conducted reviews has exclusively focused on simulation extrapolation and its application in continuous measurement error models, despite its widespread use and ease of application. We attempt to close this gap in the literature by highlighting its development over the past two and a half decades.

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“…SIMEX permits flexibility in modeling because it does not require specifying an error structure model or a ranking of groups [48,66]. SIMEX, a simulation-based, error invariant method of manipulating the estimated model parameters to reduce the effects of measurement errors, can reduce bias in models with measurement error-prone variables [50,67,68]. The Kalman filter estimate of observation error associated with the selected VI time series for all 16 plots was used in conjunction with the interpolation errors in the SIMEX algorithm to correct for bias in the initial model parameters.…”

Section: Bias Correctionmentioning

confidence: 99%

“…Using the initial estimate of measurement variance and fitted parameters, SIMEX simulates increasing amounts of variance in the data and re-parameterizes the model. After several iterations of reparameterizing the model with inflated variance, SIMEX extrapolates across the estimated (and biased) parameters to find the parameters with no variance [50,67]. To implement the SIMEX algorithm, the selected model and framework were refit with modified observations simulated to represent the inflated observation error estimate [69].…”

Section: Bias Correctionmentioning

confidence: 99%

A Model to Estimate Leaf Area Index in Loblolly Pine Plantations Using Landsat 5 and 7 Images

et al. 2021

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Vegetation indices calculated from remotely sensed satellite imagery are commonly used within empirically derived models to estimate leaf area index in loblolly pine plantations in the southeastern United States. The data used to parameterize the models typically come with observation errors, resulting in biased parameters. The objective of this study was to quantify and reduce the effects of observation errors on a leaf area index (LAI) estimation model using imagery from Landsat 5 TM and 7 ETM+ and over 1500 multitemporal measurements from a Li-Cor 2000 Plant Canopy Analyzer. Study data comes from a 16 quarter 1 ha plot with 1667 trees per hectare (2 m × 3 m spacing) fertilization and irrigation research site with re-measurements taken between 1992 and 2004. Using error-in-variable methods, we evaluated multiple vegetation indices, calculated errors associated with their observations, and corrected for them in the modeling process. We found that the normalized difference moisture index provided the best correlation with below canopy LAI measurements (76.4%). A nonlinear model that accounts for the nutritional status of the stand was found to provide the best estimates of LAI, with a root mean square error of 0.418. The analysis in this research provides a more extensive evaluation of common vegetation indices used to estimate LAI in loblolly pine plantations and a modeling framework that extends beyond the typical linear model. The proposed model provides a simple to use form allowing forest practitioners to evaluate LAI development and its uncertainty in historic pine plantations in a spatial and temporal context.

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Simulation–Extrapolation for Bias Correction with Exposure Uncertainty in Radiation Risk Analysis Utilizing Grouped Data

Cited by 9 publications

References 31 publications

Effect of Heterogeneity in Background Incidence on Inference about the Solid-Cancer Radiation Dose Response in Atomic Bomb Survivors

Effect of Heterogeneity in Background Incidence on Inference about the Solid-Cancer Radiation Dose Response in Atomic Bomb Survivors

Simulation extrapolation method for measurement error: A review

A Model to Estimate Leaf Area Index in Loblolly Pine Plantations Using Landsat 5 and 7 Images

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