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 Genotype Pattern Mining for Pairs of Interacting Variants Underlying Digenic Traits

Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1160
Author(s):  
Atsuko Okazaki ◽  
Sukanya Horpaopan ◽  
Qingrun Zhang ◽  
Matthew Randesi ◽  
Jurg Ott
Keyword(s):  
Null Hypothesis ◽  
Pattern Mining ◽  
Genetic Diseases ◽  
Frequent Pattern Mining ◽  
Case Control ◽  
Frequent Pattern ◽  
Permutation Testing ◽  
Case Control Studies ◽  
P Values ◽  
Dna Variants

Some genetic diseases (“digenic traits”) are due to the interaction between two DNA variants, which presumably reflects biochemical interactions. For example, certain forms of Retinitis Pigmentosa, a type of blindness, occur in the presence of two mutant variants, one each in the ROM1 and RDS genes, while the occurrence of only one such variant results in a normal phenotype. Detecting variant pairs underlying digenic traits by standard genetic methods is difficult and is downright impossible when individual variants alone have minimal effects. Frequent pattern mining (FPM) methods are known to detect patterns of items. We make use of FPM approaches to find pairs of genotypes (from different variants) that can discriminate between cases and controls. Our method is based on genotype patterns of length two, and permutation testing allows assigning p-values to genotype patterns, where the null hypothesis refers to equal pattern frequencies in cases and controls. We compare different interaction search approaches and their properties on the basis of published datasets. Our implementation of FPM to case-control studies is freely available.

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.

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