Does it always help to adjust for misclassification of a binary outcome in logistic regression?

Luan, Xianqun; Pan, Wei; Gerberich, Susan Goodwin; Carlin, Bradley P.

doi:10.1002/sim.2094

Cited by 21 publications

(17 citation statements)

References 11 publications

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“…The value of the validated phenotype data in association analyses has been considered in the literature. Estimated misclassification rates from external sources can be used to account for misclassification (Carroll, Ruppert, Stefanski, & Crainiceanu, 2006; Luan, Pan, Gerberich, & Carlin, 2005). Alternatively, when controls are accurately selected, the algorithm-derived probability of being a case can be directly applied for association testing based on a logistic regression model for the binary phenotype status (Sinnott et al, 2014).…”

Section: | Discussionmentioning

confidence: 99%

Phenotype validation in electronic health records based genetic association studies

Wang

Damrauer

Zhang

et al. 2017

Genetic Epidemiology

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The linkage between electronic health records (EHRs) and genotype data makes it plausible to study the genetic susceptibility of a wide range of disease phenotypes. Despite that EHR-derived phenotype data are subjected to misclassification, it has been shown useful for discovering susceptible genes, particularly in the setting of phenome-wide association studies (PheWAS). It is essential to characterize discovered associations using gold standard phenotype data by chart review. In this work, we propose a genotype stratified case-control sampling strategy to select subjects for phenotype validation. We develop a closed-form maximum-likelihood estimator for the odds ratio parameters and a score statistic for testing genetic association using the combined validated and error-prone EHR-derived phenotype data, and assess the extent of power improvement provided by this approach. Compared with case-control sampling based only on EHR-derived phenotype data, our genotype stratified strategy maintains nominal type I error rates, and result in higher power for detecting associations. It also corrects the bias in the odds ratio parameter estimates, and reduces the corresponding variance especially when the minor allele frequency is small.

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Section: | Discussionmentioning

confidence: 99%

Phenotype validation in electronic health records based genetic association studies

Wang

Damrauer

Zhang

et al. 2017

Genetic Epidemiology

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“…The extent of misclassification can be described using quantities such as sensitivity, specificity, and negative and positive predictive values (provided a gold standard exists for comparison). Researchers have explored methods for incorporating external information about sensitivity/specificity to account for outcome misclassification . However, these quantities can vary from population to population and from phenotype to phenotype, and it is difficult to know the extent of phenotype misclassification in a particular population without performing further phenotype validation .…”

Section: Statistical Issues Related To Biobank Researchmentioning

confidence: 99%

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

Beesley

Salvatore

Fritsche

et al. 2019

Statistics in Medicine

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Biobanks linked to electronic health records provide rich resources for health‐related research. With improvements in administrative and informatics infrastructure, the availability and utility of data from biobanks have dramatically increased. In this paper, we first aim to characterize the current landscape of available biobanks and to describe specific biobanks, including their place of origin, size, and data types. The development and accessibility of large‐scale biorepositories provide the opportunity to accelerate agnostic searches, expedite discoveries, and conduct hypothesis‐generating studies of disease‐treatment, disease‐exposure, and disease‐gene associations. Rather than designing and implementing a single study focused on a few targeted hypotheses, researchers can potentially use biobanks' existing resources to answer an expanded selection of exploratory questions as quickly as they can analyze them. However, there are many obvious and subtle challenges with the design and analysis of biobank‐based studies. Our second aim is to discuss statistical issues related to biobank research such as study design, sampling strategy, phenotype identification, and missing data. We focus our discussion on biobanks that are linked to electronic health records. Some of the analytic issues are illustrated using data from the Michigan Genomics Initiative and UK Biobank, two biobanks with two different recruitment mechanisms. We summarize the current body of literature for addressing these challenges and discuss some standing open problems. This work complements and extends recent reviews about biobank‐based research and serves as a resource catalog with analytical and practical guidance for statisticians, epidemiologists, and other medical researchers pursuing research using biobanks.

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“…The lack of other significant covariates on the prevalences of CE might be because permanent teeth have recently erupted at the age of 7, and they have not been exposed enough to infectious agents and/or to the well-known loss of power associated with the presence of misclassification (see, e.g., Luan et al 2005). Regarding the incidence of CE, the results indicate that the later the child starts brushing or the higher the number of between-meal snacks, the higher the probability of developing caries.…”

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confidence: 99%

Modeling of Multivariate Monotone Disease Processes in the Presence of Misclassification

García-Zattera

Jara

Lesaffre

et al. 2012

Journal of the American Statistical Association

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Motivated by a longitudinal oral health study, the Signal-Tandmobiel R study, we propose a multivariate binary inhomogeneous Markov model in which unobserved correlated response variables are subject to an unconstrained misclassification process and have a monotone behavior. The multivariate baseline distributions and Markov transition matrices of the unobserved processes are defined as a function of covariates through the specification of compatible full conditional distributions. Distinct misclassification models are discussed. In all cases, the possibility that different examiners were involved in the scoring of the responses of a given subject across time is taken into account. A full Bayesian implementation of the model is described and its performance is evaluated using simulated data. We provide theoretical and empirical evidence that the parameters can be estimated without any external information about the misclassification parameters. Finally, the analyses of the motivating study are presented. Appendices 1-7 are available in the online supplementary materials.

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Does it always help to adjust for misclassification of a binary outcome in logistic regression?

Cited by 21 publications

References 11 publications

Phenotype validation in electronic health records based genetic association studies

Phenotype validation in electronic health records based genetic association studies

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

Modeling of Multivariate Monotone Disease Processes in the Presence of Misclassification

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