Binomial regression with a misclassified covariate and outcome

Luo, Sheng; Chan, Wenyaw; Detry, Michelle A.; Massman, Paul J.; Doody, Rachelle S.

doi:10.1177/0962280212441965

Cited by 7 publications

(7 citation statements)

References 54 publications

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“…In general, the chance of misdiagnosis (or misclassification) brings a situation of overparameterization of the respective statistical analysis, where the misdiagnosis probability cannot be estimated from the data directly. To deal with this situation, there are non-trivial solutions in the statistical literature (33, 34). However, the discussion about these advanced solutions was out of the scope of this study.…”

Section: Discussionmentioning

confidence: 99%

Impact of imperfect diagnosis in ME/CFS association studies

Malato

Graça

Sepúlveda

2022

Preprint

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The absence of an objective disease biomarker puts studies of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) under the curse of imperfect diagnosis. This problem leads to frequent reports that fail to reproduce prior published studies. To address the impact of imperfect diagnosis on the robustness of studies’ conclusions, we conducted a simulation study to quantify the statistical power to detect a disease association with a hypothetical binary factor in the presence of imperfect diagnosis. Using the classical case-control design, studies with sample sizes of less than 500 individuals per group could not reach the target power of at least 80% to detect realistic disease associations. We then recreated serological association studies in which the chance of imperfect diagnosis was combined with the probability of misclassifying a binary factor, as it happens in a typical serological association study. In this case, the target power of 80% could only be achieved for studies with more than 1000 individuals per group. Given the current sample sizes of ME/CFS studies, our results suggest that most studies are likely to be underpowered due to imperfect diagnosis alone. To increase reproducibility across studies, we provided some practical recommendations, such as the use of standard case definitions together with multi-centric study designs, and routine reporting of power calculations under a non-negligible chance of misdiagnosis. Our results can also inform the design of future studies under the assumption of misdiagnosis.

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Section: Discussionmentioning

confidence: 99%

Impact of imperfect diagnosis in ME/CFS association studies

Malato

Graça

Sepúlveda

2022

Preprint

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“…Notwithstanding controlling for the body mass index in the respective association analysis and the exclusion of known diseases, it is unclear whether the obesity observed in these patients was a direct consequence of ME/CFS or instead caused by another ongoing disease strongly associated with fatigue. A solution to this problem is to use more advanced statistical methodology where misclassification can be directly included in the data analysis (17,18). However, given the complexity of this methodology, we argue that a stronger collaboration between the ME/CFS research community and statistical geneticists should be reached.…”

Section: Discussionmentioning

confidence: 99%

Review of the Quality Control Checks Performed by Current Genome-Wide and Targeted-Genome Association Studies on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

et al. 2020

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“…Measurement error in statistical and risk science is a well-studied topic in the literature [ 14 – 18 ]. However, effects of measurement error on the strength of concentration-response relationships in toxicological studies have been limited.…”

Section: Introductionmentioning

confidence: 99%

Bayesian network-based framework for exposure-response study design and interpretation

2019

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Conventional environmental-health risk-assessment methods are often limited in their ability to account for uncertainty in contaminant exposure, chemical toxicity and resulting human health risk. Exposure levels and toxicity are both subject to significant measurement errors, and many predicted risks are well below those distinguishable from background incident rates in target populations. To address these issues methods are needed to characterize uncertainties in observations and inferences, including the ability to interpret the influence of improved measurements and larger datasets. Here we develop a Bayesian network (BN) model to quantify the joint effects of measurement errors and different sample sizes on an illustrative exposure-response system. Categorical variables are included in the network to describe measurement accuracies, actual and measured exposures, actual and measured response, and the true strength of the exposure-response relationship. Network scenarios are developed by fixing combinations of the exposure-response strength of relationship (none, medium or strong) and the accuracy of exposure and response measurements (low, high, perfect). Multiple cases are simulated for each scenario, corresponding to a synthetic exposure response study sampled from the known scenario population. A learn-from-cases algorithm is then used to assimilate the synthetic observations into an uninformed prior network, yielding updated probabilities for the strength of relationship. Ten replicate studies are simulated for each scenario and sample size, and results are presented for individual trials and their mean prediction. The model as parameterized yields little-to-no convergence when low accuracy measurements are used, though progressively faster convergence when employing high accuracy or perfect measurements. The inferences from the model are particularly efficient when the true strength of relationship is none or strong with smaller sample sizes. The tool developed in this study can help in the screening and design of exposure-response studies to better anticipate where such outcomes can occur under different levels of measurement error. It may also serve to inform methods of analysis for other network models that consider multiple streams of evidence from multiple studies of cumulative exposure and effects.

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Binomial regression with a misclassified covariate and outcome

Cited by 7 publications

References 54 publications

Impact of imperfect diagnosis in ME/CFS association studies

Impact of imperfect diagnosis in ME/CFS association studies

Review of the Quality Control Checks Performed by Current Genome-Wide and Targeted-Genome Association Studies on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Bayesian network-based framework for exposure-response study design and interpretation

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