Simulation Extrapolation Method for Cox Regression Model with a Mixture of Berkson and Classical Errors in the Covariates using Calibration Data

Tapsoba, Jean de Dieu; Chao, E. C. T.; Wang, Ching‐Yun

doi:10.1515/ijb-2018-0028

Cited by 5 publications

(4 citation statements)

References 32 publications

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“…Mallick et al (2002) found that the mixture error bias in their relative risks for thyroid disease and radiation fallout ranged from 3.2 to 42.7% [15]. Tapsoba et al (2019) studying medications in HIV patients report biases from 0 to 22% depending on the correction method [33]. These values are close to our findings, as is their assumed percentage of Berkson error in the exposure that lies between 20 and 80%.…”

Section: Discussionsupporting

confidence: 84%

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Quantifying the short-term effects of air pollution on health in the presence of exposure measurement error: a simulation study of multi-pollutant model results

et al. 2021

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Background Most epidemiological studies estimate associations without considering exposure measurement error. While some studies have estimated the impact of error in single-exposure models we aimed to quantify the effect of measurement error in multi-exposure models, specifically in time-series analysis of PM2.5, NO2, and mortality using simulations, under various plausible scenarios for exposure errors. Measurement error in multi-exposure models can lead to effect transfer where the effect estimate is overestimated for the pollutant estimated with more error to the one estimated with less error. This complicates interpretation of the independent effects of different pollutants and thus the relative importance of reducing their concentrations in air pollution policy. Methods Measurement error was defined as the difference between ambient concentrations and personal exposure from outdoor sources. Simulation inputs for error magnitude and variability were informed by the literature. Error-free exposures with their consequent health outcome and error-prone exposures of various error types (classical/Berkson) were generated. Bias was quantified as the relative difference in effect estimates of the error-free and error-prone exposures. Results Mortality effect estimates were generally underestimated with greater bias observed when low ratios of the true exposure variance over the error variance were assumed (27.4% underestimation for NO2). Higher ratios resulted in smaller, but still substantial bias (up to 19% for both pollutants). Effect transfer was observed indicating that less precise measurements for one pollutant (NO2) yield more bias, while the co-pollutant (PM2.5) associations were found closer to the true. Interestingly, the sum of single-pollutant model effect estimates was found closer to the summed true associations than those from multi-pollutant models, due to cancelling out of confounding and measurement error bias. Conclusions Our simulation study indicated an underestimation of true independent health effects of multiple exposures due to measurement error. Using error parameter information in future epidemiological studies should provide more accurate concentration-response functions.

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Section: Discussionsupporting

confidence: 84%

“…The mixture model consists of the variables described in eqs. 1 and 2, along with a latent intermediate variable L, between A and C, that allows for mixtures of Berkson and classical error as described elsewhere [15,33]. Briefly, the model is:…”

Section: Mixture Errormentioning

confidence: 99%

Quantifying the short-term effects of air pollution on health in the presence of exposure measurement error: a simulation study of multi-pollutant model results

et al. 2021

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“…Apanasovich et al (2009) developed the basic theory for SIMEX in semiparametric problems using kernel‐based estimation methods. Tapsoba et al (2019) applied SIMEX to Cox regression analysis when some covariates are possibly contaminated with a mixture of Berkson and classical errors.…”

Section: Biomarker Measurement Error In Regression Analysismentioning

confidence: 99%

Biomarker data with measurement error in medical research: A literature review

Wang,

Hwang,

Song

2024

WIREs Computational Stats

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A biomarker is a measurable indicator of the severity or presence of a disease or medical condition in biomedical or epidemiological research. Biomarkers may help in early diagnosis and prevention of diseases. Several biomarkers have been identified for many diseases such as carbohydrate antigen 19‐9 for pancreatic cancer. However, biomarkers may be measured with errors due to many reasons such as specimen collection or day‐to‐day within‐subject variability of the biomarker, among others. Measurement error in the biomarker leads to bias in the regression parameter estimation for the association of the biomarker with disease in epidemiological studies. In addition, measurement error in the biomarkers may affect standard diagnostic measures to evaluate the performance of biomarkers such as the receiver operating characteristic (ROC) curve, area under the ROC curve, sensitivity, and specificity. Measurement error may also have an effect on how to combine multiple cancer biomarkers as a composite predictor for disease diagnosis. In follow‐up studies, biomarkers are often collected intermittently at examination times, which may be sparse and typically biomarkers are not observed at the event times. Joint modeling of longitudinal and time‐to‐event data is a valid approach to account for measurement error in the analysis of repeatedly measured biomarkers and time‐to‐event outcomes. In this article, we provide a literature review on existing methods to correct for estimation in regression analysis, diagnostic measures, and joint modeling of longitudinal biomarkers and survival outcomes when the biomarkers are measured with errors.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Robust Methods Statistical and Graphical Methods of Data Analysis > EM Algorithm Statistical Models > Survival Models

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“…The outcome variable Y was assigned by sampling from normal distribution under a model of the form Y = 1 + 2Z + 1T + N(0, 1). Simulations were conducted for scenarios where ω T|Z = 0, 1, 2, and for scenarios where σ 2 X|T = 0.2, 0.5, 1, similar to the ranges of measurement errors that have been posited in simulations in a range of epidemiologic substantive areas (16)(17)(18).…”

Section: Simulation Examplementioning

confidence: 99%

Reducing Bias Due to Exposure Measurement Error Using Disease Risk Scores

Richardson¹,

Keil²,

Cole³

et al. 2020

American Journal of Epidemiology

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Suppose that an investigator wants to estimate an association between a continuous exposure variable and an outcome, adjusting for a set of confounders. If the exposure variable suffers classical measurement error, in which the measured exposures are distributed with independent error around the true exposure, then an estimate of the covariate-adjusted exposure-outcome association may be biased. We propose an approach to estimate a marginal exposure-outcome association in the setting of classical exposure measurement error using a disease score-based approach to standardization to the exposed sample. First, we show that the proposed marginal estimate of the exposure-outcome association will suffer less bias due to classical measurement error than the covariate-conditional estimate of association when the covariates are predictors of exposure. Second, we show that if an exposure validation study is available with which to assess exposure measurement error then the proposed marginal estimate of the exposure-outcome association can be corrected for measurement error more efficiently than the covariate-conditional estimate of association. We illustrate both of these points using simulations and an empirical example using data from the Orinda Longitudinal Study of Myopia (1989-2001).

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Simulation Extrapolation Method for Cox Regression Model with a Mixture of Berkson and Classical Errors in the Covariates using Calibration Data

Cited by 5 publications

References 32 publications

Quantifying the short-term effects of air pollution on health in the presence of exposure measurement error: a simulation study of multi-pollutant model results

Quantifying the short-term effects of air pollution on health in the presence of exposure measurement error: a simulation study of multi-pollutant model results

Biomarker data with measurement error in medical research: A literature review

Reducing Bias Due to Exposure Measurement Error Using Disease Risk Scores

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