Add to ORKGMeasurement error in BMI is known to be a complex process has serious consequences for traditional estimators. In this paper I examine the extent to which Stochastic Multiple Imputation approaches can successfully addressing this problem. Using both Monte Carlo simulations and real world data I show how the MI approach can provide an effective solution to measurement error in BMI in appropriate circumstances. The MI approach yields consistent estimates that efficiently use all the available data. 1
Background: Addressing missing data on body weight, height, or both is a challenge many researchers...
In much of applied statistics variables of interest are measured with error. In particular, regressi...
Many analyses of longitudinal cohorts require incorporating sampling weights to account for unequal ...
Measurement error in BMI is known to be a complex process has serious consequences for traditional ...
This paper examines the consequences of using self-reported measures of BMI when estimating the eff...
We examine what, if anything, we can learn about obesity rates using self-reported BMI once we allow...
Reliable measures of obesity are essential in order to develop effective policies to tackle the cost...
We designed an experiment to explore the extent of measurement error in body mass index (BMI), when ...
Using the nationally representative Slan dataset of 2007 we analyse the relationship between self-re...
To compare alternative models for the imputation of BMIM (measured weight in kilograms/measured heig...
Open access article. Creative Commons Attribution 3.0 Unported License (CC BY 3.0) appliesThis stud...
To assess time trends in measurement error of BMI and the sensitivity/specificity of classifying wei...
This study examined the feasibility of developing correction factors to adjust self-reported measure...
To investigate the issue of systematic bias in self-reported weight and height, and produce a simple...
Background: Body weight variability (BWV) is common in the general population and may act as a risk ...
Background: Addressing missing data on body weight, height, or both is a challenge many researchers...
In much of applied statistics variables of interest are measured with error. In particular, regressi...
Many analyses of longitudinal cohorts require incorporating sampling weights to account for unequal ...
Measurement error in BMI is known to be a complex process has serious consequences for traditional ...
This paper examines the consequences of using self-reported measures of BMI when estimating the eff...
We examine what, if anything, we can learn about obesity rates using self-reported BMI once we allow...
Reliable measures of obesity are essential in order to develop effective policies to tackle the cost...
We designed an experiment to explore the extent of measurement error in body mass index (BMI), when ...
Using the nationally representative Slan dataset of 2007 we analyse the relationship between self-re...
To compare alternative models for the imputation of BMIM (measured weight in kilograms/measured heig...
Open access article. Creative Commons Attribution 3.0 Unported License (CC BY 3.0) appliesThis stud...
To assess time trends in measurement error of BMI and the sensitivity/specificity of classifying wei...
This study examined the feasibility of developing correction factors to adjust self-reported measure...
To investigate the issue of systematic bias in self-reported weight and height, and produce a simple...
Background: Body weight variability (BWV) is common in the general population and may act as a risk ...
Background: Addressing missing data on body weight, height, or both is a challenge many researchers...
In much of applied statistics variables of interest are measured with error. In particular, regressi...
Many analyses of longitudinal cohorts require incorporating sampling weights to account for unequal ...