Homogeneity Of Relative Risk Research Articles

Abstract MP071: Genetic Modification of Triggers of Acute Myocardial Infarction: Results From a Case-crossover Study in Costa Rica

Background: Coffee intake and heavy physical exertion are triggers of acute myocardial infarction (AMI). It is hypothesized that their triggering effects can be modified by genetic variation. Methods: The study population consisted of incident cases (n=1354 for coffee intake, and n=1101 for heavy physical exertion) of nonfatal AMI recruited in Costa Rica between 1994 and 2004. Coffee intake and heavy physical exertion were assessed with validated questionnaires. Blood samples were collected and genotyping was conducted. We used a case-crossover design to estimate the relative risks (RRs) and 95% confidence intervals (CIs) of AMI associated with each trigger stratified by the genotypes of each SNP included. Chi-square tests of homogeneity of RR across genotypic strata were used to assess effect modification by genetic factors. We included SNPs from both candidate genes (candidate SNPs) and previous genome-wide association studies (GWAS SNPs) related to the pathophysiological pathways of the triggering effects. Specifically, the candidate SNPs were from select genes related to sympathetic nervous system, renin-angiotensin system, caffeine metabolism, and habitual coffee consumption. The GWAS SNPs were those previously found to be associated with AMI/coronary artery disease, lipid metabolism, blood pressure, heart rate, blood coagulation, matrix metalloproteinase, and adhesion molecules. A p-value of 0.05 was considered statistically significant at first. We then performed Bonferroni correction and false discovery rate (FDR) adjustment to account for multiple testing. Results: Among the SNPs associated with AMI/coronary artery disease, rs10483853 significantly modified the triggering effect of coffee, and rs10507130 was significant for heavy physical exertion. Among the SNPs associated with blood pressure, rs935334 was significant for both triggers. Among the SNPs associated with heart rate, rs9398652, rs1541010 and rs1395479 were significant for coffee intake, and rs17287293 and rs223116 were significant for heavy physical exertion. Among the SNPs associated with blood coagulation, rs4460176, rs565229, and rs12367822 were significant for coffee intake, and rs2731672, rs10489087, rs647316, rs2138852, rs1473247, rs12367822 and rs1671152 were significant for heavy physical exertion. The two SNPs associated with serum matrix metalloproteinase (rs11225434 and rs495366) both significantly modified the triggering effect of heavy physical exertion. No SNPs remained statistically significant after Bonferroni correction or FDR adjustment. Conclusions: We identified several potential genetic modifiers of the triggering effects of coffee intake and heavy physical exertion on AMI. Among them, heart rate and blood coagulation traits appeared to be more important. Replication studies in other populations will help confirm and expand our findings.

Comparing diagnostic tests: test of hypothesis for likelihood ratios

Likelihood ratios (LRs) are used to characterize the efficiency of diagnostic tests. In this paper, we use the classical weighted least squares (CWLS) test procedure, which was originally used for testing the homogeneity of relative risks, for comparing the LRs of two or more binary diagnostic tests. We compare the performance of this method with the relative diagnostic likelihood ratio (rDLR) method and the diagnostic likelihood ratio regression (DLRReg) approach in terms of size and power, and we observe that the performances of CWLS and rDLR are the same when used to compare two diagnostic tests, while DLRReg method has higher type I error rates and powers. We also examine the performances of the CWLS and DLRReg methods for comparing three diagnostic tests in various sample size and prevalence combinations. On the basis of Monte Carlo simulations, we conclude that all of the tests are generally conservative and have low power, especially in settings of small sample size and low prevalence.

The impact of risk status, preexisting morbidity, and polypharmacy on treatment decisions concerning menopausal symptoms

Because each menopausal treatment has a unique benefit–risk profile, deciding whether menopausal hormone therapy (HT) or another menopausal treatment is appropriate for an individual requires assessing the risks and benefits of each treatment as well as determining the patient's risk factors, comorbidities, and current medications. Simple summary metrics for assessing net treatment effects on multiple outcomes, such as the Women's Health Initiative's (WHI) global index, are not necessarily generalizable beyond the study population in which they were derived. However, trial evidence can be translated to individual decisions by transforming relative risks into absolute risks. The validity of generalizing relative risks found in clinical trials to groups with differing baseline risks depends on the homogeneity of relative risks across various risk strata. Subgroup analyses from the major clinical studies of HT found its relative risks on cardiovascular disease, hip fracture, breast cancer, colorectal cancer, and ovarian cancer to be relatively uniform across strata defined by age, race/ethnicity, antecedent risk status, or prior disease. The number of significant subgroup findings in these studies tended to match the number expected by chance alone. However, data are limited for many subgroups, especially those with comorbidities. Strata defined by the concurrent use of drugs that could interact with HT would not be expected to be homogeneous, yet data on drug interactions are limited. More data are needed about the effects of menopausal treatments in diverse populations, and more attention is needed to translate this evidence into clinical practice and to develop tools to support informed decision making.

The impact of risk status, preexisting morbidity, and polypharmacy on treatment decisions concerning menopausal symptoms

Because each menopausal treatment has a unique benefit-risk profile, deciding whether menopausal hormone therapy (HT) or another menopausal treatment is appropriate for an individual requires assessing the risks and benefits of each treatment as well as determining the patient's risk factors, comorbidities, and current medications. Simple summary metrics for assessing net treatment effects on multiple outcomes, such as the Women's Health Initiative's (WHI) global index, are not necessarily generalizable beyond the study population in which they were derived. However, trial evidence can be translated to individual decisions by transforming relative risks into absolute risks. The validity of generalizing relative risks found in clinical trials to groups with differing baseline risks depends on the homogeneity of relative risks across various risk strata. Subgroup analyses from the major clinical studies of HT found its relative risks for cardiovascular disease, hip fracture, breast cancer, colorectal cancer, and ovarian cancer to be relatively uniform across strata defined by age, race/ethnicity, antecedent risk status, or prior disease. The number of significant subgroup findings in these studies tended to match the number expected by chance alone. However, data are limited for many subgroups, especially those with comorbidities. Strata defined by the concurrent use of drugs that can interact with HT are not expected to be homogeneous, yet data on drug interactions are limited. More data are needed about the effects of menopausal treatments in diverse populations, and more attention is needed to translate this evidence into clinical practice and to develop tools to support informed decision making.

Power and sample size for testing homogeneity of relative risks in prospective studies.

Power and sample-size formulas for testing the homogeneity of relative risks using the score method are presented. The homogeneity score test (Gart, 1985, Biometrika 72, 673-677) is formally equivalent to the Pearson chi-square test, although they look different. Results of this paper may be useful in assessing the validity of the model of a common relative risk before combining several 2 x 2 tables or in designing a prospective study for detecting heterogeneity of relative risks.

Homogeneity of the relative risk in cohort studies

We propose a statistical method to test the homogeneity of relative risk in cohort studies. We illustrate its application to an original data set from a low-risk group of end-stage renal disease patients on haemodialysis. The use of the method is as a global test against the omnibus alternative.