Correction of odds ratios in case-control studies for exposure misclassification with partial knowledge of the degree of agreement among experts who assessed exposures

ObjectivesEstimates of association between exposures and diseases are often distorted by error in exposure classification. When the validity of exposure assessment is known, this can be used to adjust these estimates. When exposure is assessed by experts, even if validity is not known, we sometimes have information about interrater reliability. We present a Bayesian method for translating the knowledge of interrater reliability, which is often available, into knowledge about validity, which is often needed but not directly available, and applying this to correct odds ratios (OR).MethodsThe method allows for inclusion of observed potential confounders in the analysis, as is common in regression-based control for confounding. Our method uses a novel type of prior on sensitivity and specificity. The approach is illustrated with data from a case-control study of lung cancer risk and occupational exposure to diesel engine emissions, in which exposure assessment was made by detailed job history interviews with study subjects followed by expert judgement.ResultsUsing interrater agreement measured by kappas (κ), we estimate sensitivity and specificity of exposure assessment and derive misclassification-corrected confounder-adjusted OR. Misclassification-corrected and confounder-adjusted OR obtained with the most defensible prior had a posterior distribution centre of 1.6 with 95% credible interval (Crl) 1.1 to 2.6. This was on average greater in magnitude than frequentist point estimate of 1.3 (95% Crl 1.0 to 1.7).ConclusionsThe method yields insights into the degree of exposure misclassification and appears to reduce attenuation bias due to misclassification of exposure while the estimated uncertainty increased.

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Types of Carbohydrates Intake during Pregnancy and Frequency of a Small for Gestational Age Newborn: A Case-Control Study

Nutrients ◽

10.3390/nu11030523 ◽

2019 ◽

Vol 11 (3) ◽

pp. 523 ◽

Cited By ~ 1

Author(s):

Carmen Amezcua-Prieto ◽

Juan Martínez-Galiano ◽

Naomi Cano-Ibáñez ◽

Rocío Olmedo-Requena ◽

Aurora Bueno-Cavanillas ◽

...

Keyword(s):

Gestational Age ◽

Small For Gestational Age ◽

Conditional Logistic Regression ◽

Case Control ◽

Inverse Association ◽

Odds Ratios ◽

Case Control Studies ◽

Logistic Regression Models ◽

Matched Case ◽

Brown Bread

The objective of this study was to assess the relationship between consumption of different types of carbohydrates (CHO) during pregnancy and the risk of having a small for gestational age (SGA) newborn. A retrospective matched case–control design was carried out with a total of 518 mother-offspring pairs. A total of 137 validated items were included in the food frequency questionnaire (FFQ). Conditional logistic regression models were used to calculate crude odds ratios (cORs) and adjusted odds ratios (aORs) with 95% confidence intervals (CIs). Having more than 75 g/day of brown bread showed an inverse association with SGA (aOR = 0.64, CI 0.43–0.96). In contrast, an intake of industrial sweets more than once a day (aOR = 2.70, CI 1.42–5.13), or even 2–6 times a week (aOR = 1.84, CI 1.20–2.82), increased the odds of having a SGA newborn. During pregnancy, the higher the increase of wholegrain cereal and bread, the lower the possibility of having a SGA newborn, but the opposite occurred with refined sugar products—just consuming industrial bakery products or pastries twice a week increased the odds of having an SGA infant. Case–control studies cannot verify causality and only show associations, which may reflect residual confusion due to the presence of unknown factors. It is possible that a high consumption of sugary foods is a marker of a generally poor lifestyle.

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O2E.4 Evaluation of exposure assessment methods in epidemiological studies: the welding example

Occupational and Environmental Medicine ◽

10.1136/oem-2019-epi.55 ◽

2019 ◽

Vol 76 (Suppl 1) ◽

pp. A21.1-A21

Author(s):

Susan Peters ◽

Jerome Lavoue ◽

Marissa Baker ◽

Hans Kromhout

Keyword(s):

Lung Cancer ◽

Ultraviolet Radiation ◽

Exposure Assessment ◽

Epidemiological Studies ◽

Case Control ◽

Assessment Methods ◽

Welding Fumes ◽

Welding Fume ◽

Case Control Studies ◽

Eye Burns

Exposure assessment quality is a fundamental consideration in the design and evaluation of observational studies. High quality exposure assessment is particularly relevant for outcomes with long latency, such as cancer, where detailed information on past exposures are often missing and must therefore be estimated.For the IARC Monograph on welding, the exposure group provided an overview of assessment methods used in the key epidemiological studies. Strengths and weaknesses of each study were assessed, along with their potential effects on interpretation of risk estimates.For the association between lung cancer and welding fume exposure, 9 cohort and 10 case-control studies were reviewed. For ocular melanoma and ultraviolet radiation (UVR) from welding, 7 case-control studies were reviewed. Quality criteria were: full occupational histories, and standardized, blinded and quantitative exposure assessment. Additional criteria for lung cancer: specifically assessing welding fumes and using information on welding tasks. For ocular melanoma: assessing artificial and solar radiation separately, taking into account eye burns, eye protection and welding type.Exposure assessment of welding fumes by applying a ‘welding-exposure matrix’ (n=2) or welding-specific questionnaires (n=3) were considered highest quality, followed by case-by-case expert assessment (n=5) or general job-exposure matrices (JEMs, n=4). Job title alone was considered less informative (n=5). For exposure to UVR, JEMs were most informative (n=2), followed by self-reported eye burns and self-reported exposure from specific welding types (n=2), although caution is advised regarding recall bias. Assessing welding fume exposure or ever exposure to welding arcs as proxy for UVR was considered less informative. For both exposures, ever versus never welder, or assessments based on data collected from proxies, were considered least informative.The overall evaluation was that there is sufficient evidence in humans for the carcinogenicity of welding fumes and ultraviolet radiation from welding.

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Do Case-Control Studies Always Estimate Odds Ratios?

American Journal of Epidemiology ◽

10.1093/aje/kwaa167 ◽

2020 ◽

Author(s):

Jeremy A Labrecque ◽

Myriam M G Hunink ◽

M Arfan Ikram ◽

M Kamran Ikram

Keyword(s):

Incidence Rate ◽

Case Control Study ◽

Case Control ◽

Small Sample ◽

Incidence Density ◽

Odds Ratios ◽

Case Control Studies ◽

Rate Ratios ◽

Study Designs ◽

Control Study

Abstract Case-control studies are an important part of the epidemiologic literature, yet confusion remains about how to interpret estimates from different case-control study designs. We demonstrate that not all case-control study designs estimate odds ratios. On the contrary, case-control studies in the literature often report odds ratios as their main parameter even when using designs that do not estimate odds ratios. Only studies using specific case-control designs should report odds ratios, whereas the case-cohort and incidence-density sampled case-control studies must report risk ratio and incidence rate ratios, respectively. This also applies to case-control studies conducted in open cohorts, which often estimate incidence rate ratios. We also demonstrate the misinterpretation of case-control study estimates in a small sample of highly cited case-control studies in general epidemiologic and medical journals. We therefore suggest that greater care be taken when considering which parameter is to be reported from a case-control study.

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Robust Bayesian Sensitivity Analysis for Case–Control Studies with Uncertain Exposure Misclassification Probabilities

The International Journal of Biostatistics ◽

10.1515/ijb-2013-0044 ◽

2015 ◽

Vol 11 (1) ◽

Cited By ~ 1

Author(s):

Timothy Shin Heng Mak ◽

Nicky Best ◽

Lesley Rushton

Keyword(s):

Prior Distribution ◽

Odds Ratio ◽

Case Control ◽

Sensitivity Analyses ◽

Feasible Region ◽

Exposure Misclassification ◽

Case Control Studies ◽

Unknown Parameters ◽

Prior Distributions ◽

Posterior Inference

AbstractExposure misclassification in case–control studies leads to bias in odds ratio estimates. There has been considerable interest recently to account for misclassification in estimation so as to adjust for bias as well as more accurately quantify uncertainty. These methods require users to elicit suitable values or prior distributions for the misclassification probabilities. In the event where exposure misclassification is highly uncertain, these methods are of limited use, because the resulting posterior uncertainty intervals tend to be too wide to be informative. Posterior inference also becomes very dependent on the subjectively elicited prior distribution. In this paper, we propose an alternative “robust Bayesian” approach, where instead of eliciting prior distributions for the misclassification probabilities, a feasible region is given. The extrema of posterior inference within the region are sought using an inequality constrained optimization algorithm. This method enables sensitivity analyses to be conducted in a useful way as we do not need to restrict all of our unknown parameters to fixed values, but can instead consider ranges of values at a time.

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Correction to “Using Pooled Exposure Assessment to Improve Efficiency in Case-Control Studies,” by Clarice R. Weinberg and David M. Umbach; 55, 718-726, September 1999

Biometrics ◽

10.1111/biom.12261 ◽

2014 ◽

Vol 70 (4) ◽

pp. 1061-1061 ◽

Cited By ~ 7

Keyword(s):

Exposure Assessment ◽

Case Control ◽

Case Control Studies ◽

Improve Efficiency

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Adjusted Odds Ratios for Case-Control Studies with Missing Confounder Data in Controls

Epidemiology ◽

10.1097/00001648-199705000-00008 ◽

1997 ◽

Vol 8 (3) ◽

pp. 275 ◽

Cited By ~ 12

Author(s):

Samy Suissa ◽

Michael D. deB. Edwardes

Keyword(s):

Case Control ◽

Odds Ratios ◽

Case Control Studies

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Case-control studies in neurosurgery

Journal of Neurosurgery ◽

10.3171/2014.5.jns132329 ◽

2014 ◽

Vol 121 (2) ◽

pp. 285-296 ◽

Cited By ~ 14

Author(s):

Cody L. Nesvick ◽

Clinton J. Thompson ◽

Frederick A. Boop ◽

Paul Klimo

Keyword(s):

Logistic Regression ◽

Observational Studies ◽

Case Control Study ◽

Low Cost ◽

Case Control ◽

Odds Ratios ◽

Case Control Studies ◽

Inappropriate Use ◽

Kaplan Meier ◽

Control Study

Object Observational studies, such as cohort and case-control studies, are valuable instruments in evidence-based medicine. Case-control studies, in particular, are becoming increasingly popular in the neurosurgical literature due to their low cost and relative ease of execution; however, no one has yet systematically assessed these types of studies for quality in methodology and reporting. Methods The authors performed a literature search using PubMed/MEDLINE to identify all studies that explicitly identified themselves as “case-control” and were published in the JNS Publishing Group journals (Journal of Neurosurgery, Journal of Neurosurgery: Pediatrics, Journal of Neurosurgery: Spine, and Neurosurgical Focus) or Neurosurgery. Each paper was evaluated for 22 descriptive variables and then categorized as having either met or missed the basic definition of a case-control study. All studies that evaluated risk factors for a well-defined outcome were considered true case-control studies. The authors sought to identify key features or phrases that were or were not predictive of a true case-control study. Those papers that satisfied the definition were further evaluated using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist. Results The search detected 67 papers that met the inclusion criteria, of which 32 (48%) represented true case-control studies. The frequency of true case-control studies has not changed with time. Use of odds ratios (ORs) and logistic regression (LR) analysis were strong positive predictors of true case-control studies (for odds ratios, OR 15.33 and 95% CI 4.52–51.97; for logistic regression analysis, OR 8.77 and 95% CI 2.69–28.56). Conversely, negative predictors included focus on a procedure/intervention (OR 0.35, 95% CI 0.13–0.998) and use of the word “outcome” in the Results section (OR 0.23, 95% CI 0.082–0.65). After exclusion of nested case-control studies, the negative correlation between focus on a procedure/intervention and true case-control studies was strengthened (OR 0.053, 95% CI 0.0064–0.44). There was a trend toward a negative association between the use of survival analysis or Kaplan-Meier curves and true case-control studies (OR 0.13, 95% CI 0.015–1.12). True case-control studies were no more likely than their counterparts to use a potential study design “expert” (OR 1.50, 95% CI 0.57–3.95). The overall average STROBE score was 72% (range 50–86%). Examples of reporting deficiencies were reporting of bias (28%), missing data (55%), and funding (44%). Conclusions The results of this analysis show that the majority of studies in the neurosurgical literature that identify themselves as “case-control” studies are, in fact, labeled incorrectly. Positive and negative predictors were identified. The authors provide several recommendations that may reverse the incorrect and inappropriate use of the term “case-control” and improve the quality of design and reporting of true case-control studies in neurosurgery.

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