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

Epidemiology ◽  
1997 ◽  
Vol 8 (3) ◽  
pp. 275 ◽  
Author(s):  
Samy Suissa ◽  
Michael D. deB. Edwardes
Keyword(s):  
Case Control ◽  
Odds Ratios ◽  
Case Control Studies

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

Nutrients ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 523 ◽  
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.

scholarly journals Do Case-Control Studies Always Estimate Odds Ratios?

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.

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

2017 ◽  
Vol 75 (2) ◽  
pp. 155-159 ◽  
Author(s):  
Igor Burstyn ◽  
Paul Gustafson ◽  
Javier Pintos ◽  
Jérôme Lavoué ◽  
Jack Siemiatycki
Keyword(s):  
Exposure Assessment ◽  
Sensitivity And Specificity ◽  
Interrater Reliability ◽  
Credible Interval ◽  
Case Control ◽  
Point Estimate ◽  
Exposure Misclassification ◽  
Odds Ratios ◽  
Case Control Studies ◽  
Distribution Centre

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.

Case-control studies in neurosurgery

2014 ◽  
Vol 121 (2) ◽  
pp. 285-296 ◽  
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.

Use of Two Data Sources to Estimate Odds Ratios in Case-Control Studies

Epidemiology ◽  
1993 ◽  
Vol 4 (4) ◽  
pp. 327-335 ◽  
Author(s):  
Carolyn D. Drews ◽  
W. Dana Flanders ◽  
Andrzej S. Kosinski
Keyword(s):  
Case Control ◽  
Data Sources ◽  
Odds Ratios ◽  
Case Control Studies

scholarly journals Association between interleukin-17F rs763780 polymorphism and psoriasis risk: A meta-analysis

2021 ◽  
Vol 0 ◽  
pp. 1-6
Author(s):  
Zhi Xiang ◽  
Zhimin Hao ◽  
Pangen Cui ◽  
Lin Lin ◽  
Min Chen ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Genetic Mutation ◽  
Case Control ◽  
Biomedical Literature ◽  
Cochrane Central Register ◽  
Odds Ratios ◽  
Case Control Studies ◽  
Functional Sites ◽  
Increased Risk ◽  
Interleukin 17F

Background: The polymorphism of interleukin-17F rs763780 has been found to have a probable association with increased risk of developing psoriasis. Aims: This study aims to get a more convincing estimation of the association between the interleukin-17F rs763780 T /C polymorphism and psoriasis risk. Methods: Two authors independently searched the databases including PubMed, EMBASE, Cochrane Central Register of Controlled Trials, Chinese National Knowledge Infrastructure, Wanfang and Chinese Biomedical Literature Databases for case–control studies which reported the odds ratios with 95% confidence intervals comparing genotype and allele frequencies of the interleukin-17F rs763780 polymorphism in patients with psoriasis versus participants without psoriasis. Results: A total of seven case–control studies incorporating 1824 cases and 1585 controls were identified. The pooled odds ratios indicated that interleukin-17F rs763780 C allele was a risk factor for psoriasis in allele frequency, recessive model and homozygote model (P < 0.05). Subgroup analysis by ethnicity further indicated that the C allele was closely related to increased risk of psoriasis in Asian populations (P < 0.05), but not in Caucasians. Limitations: Only a few studies on the interleukin-17F rs763780 polymorphism in psoriasis have been reported till date, thus the data is insufficient. Only one gene polymorphic site was selected for this study, and it is not clear whether other genetic mutation functional sites affect the gene. Further studies on confounding effects of other genetic polymorphisms are needed. Conclusion: The present meta-analysis results suggested that the interleukin-17F rs763780 T /C is significantly associated with psoriasis risk in Asians.

scholarly journals Technical Note: Efficient and accurate estimation of genotype odds ratios in biobank-based unbalanced case-control studies

2019 ◽  
Author(s):  
Rounak Dey ◽  
Seunggeun Lee
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Case Control ◽  
Accurate Estimation ◽  
Genome Wide Association Studies ◽  
Odds Ratios ◽  
Case Control Studies ◽  
Genome Wide ◽  
A Genome ◽  
Wide Scale

AbstractIn genome-wide association studies (GWASs), genotype log-odds ratios (LORs) quantify the effects of the variants on the binary phenotypes, and calculating the genotype LORs for all of the markers is required for several downstream analyses. Calculating genotype LORs at a genome-wide scale is computationally challenging, especially when analyzing large-scale biobank data, which involves performing thousands of GWASs phenome-wide. Since most of the binary phenotypes in biobank-based studies have unbalanced (case : control = 1 : 10) or often extremely unbalanced (case : control = 1 : 100) case-control ratios, the existing methods cannot provide a scalable and accurate way to estimate the genotype LORs. The traditional logistic regression provides biased LOR estimates in such situations. Although the Firth bias correction method can provide unbiased LOR estimates, it is not scalable for genome-wide or phenome-wide scale association analyses typically used in biobank-based studies, especially when the number of non-genetic covariates is large. On the other hand, the saddlepoint approximation-based test (fastSPA), which can provide accurate p values and is scalable to analyse large-scale biobank data, does not provide the genotype LOR estimates as it is a score-based test. Here, we propose a scalable method based on score statistics, to accurately estimate the genotype LORs, adjusting for non-genetic covariates. Comparing to the Firth method, our proposed method reduces the computational complexity from O(nK2 + K3) to O(n), where n is the sample-size, and K is the number of non-genetic covariates. Our method is ~ 10x faster than the Firth method when 15 covariates are being adjusted for. Through extensive numerical simulations, we show that the proposed method is both scalable and accurate in estimating the genotype ORs in genome-wide or phenome-wide scale.

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