scholarly journals Confidence intervals for the odds ratio in case-control studies: The state of the art

1979 ◽  
Vol 32 (1-2) ◽  
pp. 69-77 ◽  
Author(s):  
Joseph L. Fleiss
Keyword(s):  
Confidence Intervals ◽  
Odds Ratio ◽  
State Of The Art ◽  
Case Control ◽  
The State ◽  
Case Control Studies

scholarly journals The association between Helicobacter pylori and obesity: a systematic review and meta-analysis of case–control studies

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Ali Baradaran ◽  
Hojat Dehghanbanadaki ◽  
Sara Naderpour ◽  
Leila Mohammadi Pirkashani ◽  
Abdolhalim Rajabi ◽  
...  
Keyword(s):  
Odds Ratio ◽  
Meta Analysis ◽  
Pooled Analysis ◽  
Case Control ◽  
Case Control Studies ◽  
Newcastle Ottawa Scale ◽  
Prevalence Of Obesity ◽  
Quantitative Analyses ◽  
H Pylori

Abstract Introduction The relationship between H. pylori infection and obesity development has remained controversial among various studies. The aim of this study was to clarify the pooled effect of H. pylori infection on the development of obesity and vice versa. Methods We searched international databases including Medline (PubMed), Web of sciences, Scopus, EMBASE, Cochrane, Ovid, and CINHAL to retrieve all case–control studies reporting the effect of H. pylori on obesity and vice versa, which had been published in English between January 1990 and June 2019. The quality of included studies was assessed by the Modified Newcastle–Ottawa Scale for Case–Control studies. The logarithm of the odds ratio (OR) and its standard error was used for the meta-analysis. Results Eight case–control studies with 25,519 participants were included for qualitative and quantitative analyses. The pooled analysis showed that obese participants had a higher risk of H. pylori infection than lean participants with an odds ratio of 1.46 (95%CI: 1.26, 1.68). Also, the pooled analysis revealed that participants infected by H. pylori had a higher risk of obesity than non-infected participants with an odds ratio of 1.01 (95%CI: 1.01, 1.02). Conclusion The results of this meta-analysis showed that there was a positive correlation between the risk of H. pylori infection and the prevalence of obesity development. Thus, H. pylori positive patients were more likely to be obese, and obese individuals had higher risks of H. pylori infection.

Ethnic differences in risk factors for subarachnoid hemorrhage

2007 ◽  
Vol 107 (3) ◽  
pp. 522-529 ◽  
Author(s):  
Vibhor Krishna ◽  
Dong H. Kim
Keyword(s):  
Risk Factors ◽  
Subarachnoid Hemorrhage ◽  
Alcohol Consumption ◽  
Cohort Studies ◽  
Odds Ratio ◽  
Case Control ◽  
Ethnic Composition ◽  
Effect Estimate ◽  
Case Control Studies ◽  
Current Smoking

Object Studies on risk factors for subarachnoid hemorrhage (SAH) show heterogeneity. For example, hypertension has been found to be a significant risk factor in some studies but not in others. The authors hypothesized that differences in the ethnicity of the populations studied could account for these findings. Methods A metaanalysis was performed using 17 case-control and 10 cohort studies that met specified inclusion criteria. The authors used a random-effect model to calculate the pooled effect estimates for current smoking, hypertension, and alcohol consumption. A meta–regression analysis was performed using the ethnic composition of the study populations as a covariate. Studies were classified as multiethnic or monoethnic, and the pooled effect estimates were compared. Results Analysis of the cohort studies yielded a pooled effect estimate or risk ratio of 3.18 (95% confidence interval [CI] 2.37–4.26) for current smoking, 3.05 (95% CI 2.09–4.44) for hypertension, and 2.46 (95% CI 1.42–4.24) for alcohol consumption at a rate of 150 g/week or more. The results were similar for the case-control studies. For current smoking, the ethnic composition of the study population was a statistically significant predictor of heterogeneity among case-control studies (p < 0.001, even after application of the Bonferroni correction). The risk for SAH among current smokers was higher in multiethnic populations (odds ratio 3.832) than in monoethnic populations (odds ratio 2.487). Conclusions The results of this metaanalysis suggest that differences in susceptibility to the harmful health effects of smoking may be one cause of the observed differences in SAH incidence for different ethnic groups. The role of ethnicity in risk factors for SAH should be considered in future studies.

Confidence intervals for predictive values with an emphasis to case–control studies

2007 ◽  
Vol 26 (10) ◽  
pp. 2170-2183 ◽  
Author(s):  
Nathaniel D. Mercaldo ◽  
Kit F. Lau ◽  
Xiao H. Zhou
Keyword(s):  
Confidence Intervals ◽  
Case Control ◽  
Case Control Studies ◽  
Predictive Values

scholarly journals Association of Helicobacter pylori Infection with the Risk of Metabolic Syndrome and Insulin Resistance: An Updated Systematic Review and Meta-Analysis

2021 ◽  
Author(s):  
Mobin Azami ◽  
Hamid Reza Baradaran ◽  
Parisa Kohnepoushi ◽  
Lotfolah Saed ◽  
Asra Moradkhani ◽  
...  
Keyword(s):  
Systematic Review ◽  
Insulin Resistance ◽  
Metabolic Syndrome ◽  
Helicobacter Pylori ◽  
Cohort Studies ◽  
Odds Ratio ◽  
Meta Analysis ◽  
Case Control ◽  
Case Control Studies ◽  
H Pylori

Abstract Background Conflicting results of recent studies on the association between Helicobacter pylori (H. pylori) infection and the risk of insulin resistance and metabolic syndrome explored the need for updated meta-analysis on this issue. Therefore, this systematic review aimed to estimate the pooled effect of H. pylori infection on the risk of insulin resistance and metabolic syndrome. Methods To identify case-control studies and cohort studies evaluating the association of H. pylori infection with insulin resistance and metabolic syndrome, a comprehensive literature search was performed from international databases including Medline (PubMed), Web of Sciences, Scopus, EMBASE, and CINHAL from January 1990 until January 2021. We used odds ratio with its 95% confidence interval (95%CI) to quantify the effect of case-control studies and risk ratio with its 95%CI for the effect of cohort studies. Results 22 studies with 206911 participants were included for meta-analysis. The pooled estimate of odds ratio between H. pylori infection and metabolic syndrome in case-control studies was 1.19 (95%CI: 1.05, 1.35; I2 = 0%), and in cohort studies, the pooled risk ratio was 1.31 (95%CI: 1.13, 1.51; I2 = 0%). Besides, case-control studies showed the pooled odds ratio of 1.54 (95%CI: 1.19, 1.98; I2 = 6.88%) for the association between H. pylori infection and insulin resistance. Conclusion A positive association was found between H. pylori infection and insulin resistance as well as metabolic syndrome, so planning to eliminate or eradicate H. pylori infection could be an effective solution to improve metabolic syndrome or insulin resistance, and vice versa.

A Computer Program to Estimate Power and Relative Efficiency of Flexibly Matched Case-control Studies

2005 ◽  
Vol 44 (05) ◽  
pp. 693-696 ◽  
Author(s):  
O. Gefeller ◽  
H. Brenner ◽  
T. Stürmer
Keyword(s):  
Computer Program ◽  
Relative Efficiency ◽  
Odds Ratio ◽  
Null Hypothesis ◽  
Case Control ◽  
Case Control Studies ◽  
Test Statistic ◽  
Matched Case ◽  
Haenszel Test ◽  
Flexible Matching

Summary Objectives: We recently introduced the concept of flexible matching strategies with varying proportions of a dichotomous matching factor among controls to increase power and efficiency of case-control studies. We now present a method and a computer program to calculate power and relative efficiency compared to an unmatched design varying the proportion of the matching factor in controls over all possible values from 0 to 100 percent. Methods: For all these values, the program calculates the expected variance of the combined Mantel-Haenszel odds ratio and determines the power using the standard error of the expected combined Mantel-Haenszel odds ratio under the null hypothesis as derived from the Mantel-Haenszel test statistic without continuity correction. Results: Thereby, the program allows estimating the optimal prevalence of the matching factor in selected controls for a given scenario which often differs from the prevalence in cases. It furthermore allows to estimate loss in power and efficiency compared to optimal matching by suboptimal matching. Conclusions: Estimations like these are helpful with respect to the decision when to stop efforts to optimize the degree of matching during the recruitment of controls. Our program will strongly facilitate assessing the benefits of flexible matching strategies.

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

2015 ◽  
Vol 11 (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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