Correcting for regression dilution bias: comparison of methods for a single predictor variable

By C. Frost, S. Thompson. 2000

310 citations recorded by the source corpus.

Subfields: Public Health, Environmental and Occupational Health, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e54a034cd89788f51419e6f15397f646b66a30bf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Public Health, Environmental and Occupational Health

In Statistics and Probability

Citations in the corpus

Interactions in Fixed Effects Regression Models (2020, background): “…random variance to the realizations of the interaction term, thus evoking a bias toward zero, similar to what has been termed attenuation bias (also known as regression dilution), which occurs because of random measurement errors in independent variables (Frost and Thompson 2000; Spearman 1904).”
Use and misuse of the reduced major axis for line-fitting. (2009, methodology): “A number of variations in the application of the method of moments estimator have been discussed by Frost and Thompson (2000).”
Head size, age and gender adjustment in MRI studies: a necessary nuisance? (2010, background): “2) Inevitable segmentation errors in measurements of TIV will bias the coefficients towards zero and away from one by an amount that depends on the size of the random errors in the measurement of TIV relative to the variability of true TIV in any given population (Frost and Thompson, 2000).”
Efficient Design for Mendelian Randomization Studies: Subsample and 2-Sample Instrumental Variable Estimators (2013, background): “In this case, imprecision in the estimation of the G-X and G-Y associations is analogous to nondifferential measurement error in an observational estimate and results in bias towards the null similar to regression dilution bias (15).”
Essential Medical Statistics (2003, background): “Frost and Thompson (2000) compare a number of methods to correct for regression dilution bias.”
Stacking species distribution models and adjusting bias by linking them to macroecological models (2014, background): “This refers to a bias in the estimation of the strength of an effect due to unaccounted variability in the predictor (see Frost & Thompson, 2000, for a review).”
The UKPDS risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56). (2001, methodology): “Since HbA "c , blood pressure and lipid measurements are all subject to within-person variation, users of the model should consider the effect of regression dilution on estimates [ 51 ].”
Bias due to participant overlap in two‐sample Mendelian randomization (2016, background): “As covariance is a linear operator in both its arguments, we may therefore expect the bias of the 2SLS estimator to be approximately linear in the proportion of overlap between the samples.”
Measuring Need Satisfaction and Frustration in Educational and Work Contexts: The Need Satisfaction and Frustration Scale (NSFS) (2016, background): “Because of this, the relationship between our predictors and outcomes may be underestimated—an effect known as ‘‘regression dilution’’ or ‘‘regression attenuation’’ (Frost and Thompson 2000; Fuller 1987).”
Predicting the distribution of vulnerable marine ecosystems in the deep sea using presence-background models (2014, background): “…becomes squashed; where a species predicted response to a variable becomes flatter and broader than the true functional response (i.e. more general; Frost and Thompson, 2000; Menke et al., 2009), although it has also been observed to become sharper where the opposite occurs (i.e. more specific;…”
Ratios: A short guide to confidence limits and proper use (2007, unknown): “This bias is often called “attenuation” or “regression dilution” (cf. Spearman, 1904; Schmidt & Hunter, 1996; DeShon, 1998; Frost & Thompson, 2000; Charles, 2005).”
fMRI-adaptation and category selectivity in human ventral temporal cortex: regional differences across time scales. (2010, background): “…responses of nonrepeated odd trials to the mean of nonrepeated even trials verified that one is an appropriate number to use as a comparison for the fMRI-A ratios (Supplemental Table S2), the regression slopes may underestimate the fMRI-A ratio due to regression dilution (Frost and Thompson 2000).”
Fine‐scale environmental variation in species distribution modelling: regression dilution, latent variables and neighbourly advice (2011, methodology): “…the random errors of different predictor variables and the methodology used for correction (Bohrnstedt & Carter 1971; Phillips & Davey Smith 1991; Frost & Thompson 2000), the extent of measures and inferred coverage of each variable (Thuiller et al. 2004; Barbet-Massin, Thuiller, & Jiguet in…”
Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic (2016, methodology): “It is possible that alternative methods of bias adjustment could work better in the MR context, such as the plethora of approaches discussed in [13,23,24].”
Robust negative impacts of climate change on African agriculture (2010, background): “Such noise in the explanatory variables will induce attenuation bias towards [20].”