PubMed · 14096261
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F AYKAN, T DOGANER. 1963. [MICTION SYNCOPE].. https://pubmed.ncbi.nlm.nih.gov/14096261/
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In traditional epidemiological studies the association between phenotype (risk factor) and disease is often biased by confounding and reverse causation. As a person's genotype is assigned by a seemingly random process, genes are potentially useful instrumental variables for adjusting for such bias. This type of adjustment combines information on the genotype-disease association and the genotype-phenotype association to estimate the phenotype-disease association and has become known as Mendelian randomization. The information on genotype-disease and genotype-phenotype may well come from a meta-analysis. In such a synthesis, a multivariate approach needs to be used whenever some studies provide evidence on both the genotype-phenotype and genotype-disease associations. This paper presents two multivariate meta-analytical models, which differ in their treatment of the heterogeneities (between-study variances). Heterogeneities on the genotype-phenotype and genotype-disease associations may be highly correlated, but a multivariate model that parameterizes the heterogeneity directly is difficult to fit because that correlation is poorly estimated. We advocate an alternative model that treats the heterogeneities on genotype-phenotype and phenotype-disease as being independent. This model fits readily and implicitly defines the correlation between the heterogeneities on genotype-phenotype and genotype-disease. We show how either maximum likelihood or a Bayesian approach with vague prior distributions can be used to fit the alternative model.
Early detection of coronary heart disease (CHD) in its pre-clinical stages may offer a way to reduce the impact of this endemic disease on society. Coronary calcification accompanies the development of an atherosclerotic plaque, the pathological substrate of CHD, and its identification is currently possible by means of electron beam tomography (EBT). This is a non-invasive imaging test that quantifies the extent of coronary artery plaque calcification by means of a calcium score. In this study, we show that an age-sex based calcium score percentile (CS%) provides an invaluable predictor for myocardial infarction (MI), and examine how CS% is related to traditional risk factors. We study two separate groups of patients: 172 patients who underwent EBT screening after surviving an MI (retrospective group); and 676 asymptomatic subjects who were screened and followed for several years for the occurrence of an MI (prospective group). We use CS% with traditional risk factors in logistic regression models to: (1) compare patients in the retrospective and prospective groups, and (2) develop a mortality model for MIs that occurred in the prospective group. These logistic regressions are used to develop a joint model for the relative log-odds of an MI, which is non-linear in the covariates of the mortality model. We also use baseline covariates in the prospective group to fit an event-time regression model and estimate probabilities for 2 and 3 year exposure periods. The event-time regression provides independent estimates of the relative odds that are associated with risk factors. CS%, smoking, and their interaction were preeminent as predictors in the joint model for the relative odds of an MI and in the event-time regression, although the effects of smoking and CS% were generally subadditive. These models provide important information on the risks associated with elevated CS% levels.
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