Measurement error is often neglected in medical literature: a systematic review

Brakenhoff, Timo B.; Mitroiu, Marian; Keogh, Ruth H.; Moons, Karel G.M.; Groenwold, Rolf H H; Smeden, Maarten van

doi:10.1016/j.jclinepi.2018.02.023

Cited by 82 publications

(60 citation statements)

References 39 publications

(47 reference statements)

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“…In most cases, the limiting factor for performing these adjusted analyses will be the availability of quantitative information regarding the measurement error. However, our impression is that even when such quantitative information is available, the adjusted analyses are not being performed …”

Section: Resultsmentioning

confidence: 99%

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1—Basic theory and simple methods of adjustment

Keogh

Shaw

Gustafson

et al. 2020

Statistics in Medicine

Self Cite

107

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Measurement error and misclassification of variables frequently occur in epidemiology and involve variables important to public health. Their presence can impact strongly on results of statistical analyses involving such variables. However, investigators commonly fail to pay attention to biases resulting from such mismeasurement. We provide, in two parts, an overview of the types of error that occur, their impacts on analytic results, and statistical methods to mitigate the biases that they cause. In this first part, we review different types of measurement error and misclassification, emphasizing the classical, linear, and Berkson models, and on the concepts of nondifferential and differential error.We describe the impacts of these types of error in covariates and in outcome variables on various analyses, including estimation and testing in regression models and estimating distributions. We outline types of ancillary studies required to provide information about such errors and discuss the implications of covariate measurement error for study design. Methods for ascertaining sample size requirements are outlined, both for ancillary studies designed to provide information about measurement error and for main studies where the exposure of interest is measured with error. We describe two of the simpler methods, regression calibration and simulation extrapolation (SIMEX), that adjust for bias in regression coefficients caused by measurement error in continuous covariates, and illustrate their use through examples drawn from the Observing Protein and Energy (OPEN) dietary validation study. Finally, we review software available for implementing these methods. The second part of the article deals with more advanced topics.

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Section: Resultsmentioning

confidence: 99%

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1—Basic theory and simple methods of adjustment

Keogh

Shaw

Gustafson

et al. 2020

Statistics in Medicine

Self Cite

107

View full text Add to dashboard Cite

show abstract

“…28 The use of measurement error corrections is still rare in applied biomedical studies despite an abundance of measurement error problems usually reported as an afterthought to a study. 14,15 Indeed, to our knowledge, no measurement error correction methods have been used so far in the analysis of biomedical trials to correct for measurement error in the endpoint. This may in part be due to a common misconception that measurement error can only affect trial inference by reducing the precision of estimating the effect of treatment and increasing Type-II error, which can be improved by increasing the study sample size.…”

Section: Discussionmentioning

confidence: 99%

“…[8][9][10][11][12][13] In addition, the application of correction methods for measurement errors in the applied medical literature is unusual. 14,15 We provide an exploration of problems and solutions for measurement error in continuous trial endpoints. For illustration of the problems and solutions for measurement error in continuous endpoints, we consider one published trial that examined the efficacy and tolerability of low-dose iron supplements during pregnancy.…”

Section: Introductionmentioning

confidence: 99%

Measurement error in continuous endpoints in randomised trials: Problems and solutions

Nab

Groenwold

Welsing

et al. 2019

Statistics in Medicine

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In randomised trials, continuous endpoints are often measured with some degree of error. This study explores the impact of ignoring measurement error and proposes methods to improve statistical inference in the presence of measurement error. Three main types of measurement error in continuous endpoints are considered: classical, systematic, and differential. For each measurement error type, a corrected effect estimator is proposed. The corrected estimators and several methods for confidence interval estimation are tested in a simulation study. These methods combine information about error‐prone and error‐free measurements of the endpoint in individuals not included in the trial (external calibration sample). We show that, if measurement error in continuous endpoints is ignored, the treatment effect estimator is unbiased when measurement error is classical, while Type‐II error is increased at a given sample size. Conversely, the estimator can be substantially biased when measurement error is systematic or differential. In those cases, bias can largely be prevented and inferences improved upon using information from an external calibration sample, of which the required sample size increases as the strength of the association between the error‐prone and error‐free endpoint decreases. Measurement error correction using already a small (external) calibration sample is shown to improve inferences and should be considered in trials with error‐prone endpoints. Implementation of the proposed correction methods is accommodated by a new software package for R.

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“…This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [12] and previous approaches in cataloging methods in high-impact literature [13,14]. Our goals were to (1) document reports of potential misclassification of high risk sexual behaviors in observational studies focusing on sexual minority men and HIV transmission, and (2) characterize the use of bias adjustment methods in correcting the proxies for sexual risk behaviors.…”

Section: Methodsmentioning

confidence: 99%

Bias Adjustment Techniques Are Underutilized in HIV Sexual Risk Estimation: A Systematic Review

Tran

Goldstein

Welles

2018

IJERPH

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Background: Valid measurement of determinants of HIV infection among men who have sex with men (MSM) is critical for intervention planning and resource allocation. However, sexual minority research concerning HIV risk often relies on proxy exposures of sexual behaviors such as sexual orientation and partner gender. Inferring high risk sexual behaviors (i.e., condomless anal intercourse) from these proxies inaccurately captures HIV risk, but few studies have attempted to correct for this bias. Methods: We performed a systematic review of methodological practices for estimating risk of HIV infection among MSM. Results: We identified 32 studies in which high risk sexual behavior was assessed: 82% (n = 26) measured and used sexual risk behaviors (e.g., condomless anal intercourse or sexual positioning) to assess risk of HIV infection; 9% (n = 3) used proxy measures; and 9% (n = 3) used both behavior and proxy variables. Various treatments of misclassification reported by investigators included the following: 82% (n = 26) discussed misclassification of sexual behavior as a potential limitation; however, among these studies, no attempts were made to correct misclassification; 12% (n = 4) did not report exposure misclassification, and 6% (n = 2) explicitly considered this information bias and conducted a Bayesian approach to correct for misclassification. Conclusions: Our systematic review indicates that a majority of studies engaging in collecting primary data have taken additional steps to acquire detailed information regarding sexual risk behaviors. However, reliance on population-based surveys may still lead to potentially biased estimates. Thus, bias analytic techniques are potential tools to control for any suspected biases.

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Measurement error is often neglected in medical literature: a systematic review

Cited by 82 publications

References 39 publications

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1—Basic theory and simple methods of adjustment

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1—Basic theory and simple methods of adjustment

Measurement error in continuous endpoints in randomised trials: Problems and solutions

Bias Adjustment Techniques Are Underutilized in HIV Sexual Risk Estimation: A Systematic Review

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