THE RELATIONSHIP BETWEEN THE RHEOENCEPHALOGRAM AND CEREBRAL BLOOD FLOW

The rheoencephalogram (REG) is the change in the electrical impedance of the head that occurs with each heart beat. Without knowledge of the relationship between cerebral blood flow (Q) and the REG, the utility of the REG in the study of the cerebral vasculature is greatly limited. The hypothesis is that the relationship between the REG and Q when venous outflow is nonpulsatile is^ (DIAGRAM, TABLE OR GRAPHIC OMITTED...PLEASE SEE DAI)^ where K is a proportionality constant and Q is the mean Q.^ Pulsatile CBF was measured in the goat via a chronically implanted electromagnetic flowmeter. Electrodes were implanted in the ipsilateral cerebral hemisphere, and the REG was measured with a two electrode impedance plethysmograph. Measurements were made with the animal's head elevated so that venous flow pulsations were not transmitted from the heart to the cerebral veins. Measurements were made under conditions of varied cerebrovascular resistance induced by altering blood CO(,2) levels and under conditions of high and low cerebrospinal fluid pressures. There was a high correlation (r = .922-.983) between the REG calculated from the hypothesized relationship and the measured REG under all conditions.^ Other investigators have proposed that the REG results from linear changes in blood resistivity proportional to blood velocity. There was little to no correlation between the measured REG and the flow velocity ( r = .022-.306). A linear combination of the flow velocity and the hypothesized relationship between the REG and Q did not predict the measured REG significantly better than the hypothesized relationship alone in 37 out of 50 experiments.^ Jacquy proposed an index (F) of cerebral blood flow calculated from amplitudes and latencies of the REG. The F index was highly correlated (r = .929) with measured cerebral blood flow under control and hypercapnic conditions, but was not as highly correlated under conditions of hypocapnia (r = .723) and arterial hypotension (r = .681).^ The results demonstrate that the REG is not determined by mean cerebral blood flow, but by the pulsatile flow only. Thus, the utility of the REG in the determination of mean cerebral blood flow is limited. ^

Assessment of the effect on statistical power of regression model misspecification by using techniques of mathematical statistics and simulation study

Objectives. This paper seeks to assess the effect on statistical power of regression model misspecification in a variety of situations. ^ Methods and results. The effect of misspecification in regression can be approximated by evaluating the correlation between the correct specification and the misspecification of the outcome variable (Harris 2010).In this paper, three misspecified models (linear, categorical and fractional polynomial) were considered. In the first section, the mathematical method of calculating the correlation between correct and misspecified models with simple mathematical forms was derived and demonstrated. In the second section, data from the National Health and Nutrition Examination Survey (NHANES 2007-2008) were used to examine such correlations. Our study shows that comparing to linear or categorical models, the fractional polynomial models, with the higher correlations, provided a better approximation of the true relationship, which was illustrated by LOESS regression. In the third section, we present the results of simulation studies that demonstrate overall misspecification in regression can produce marked decreases in power with small sample sizes. However, the categorical model had greatest power, ranging from 0.877 to 0.936 depending on sample size and outcome variable used. The power of fractional polynomial model was close to that of linear model, which ranged from 0.69 to 0.83, and appeared to be affected by the increased degrees of freedom of this model.^ Conclusion. Correlations between alternative model specifications can be used to provide a good approximation of the effect on statistical power of misspecification when the sample size is large. When model specifications have known simple mathematical forms, such correlations can be calculated mathematically. Actual public health data from NHANES 2007-2008 were used as examples to demonstrate the situations with unknown or complex correct model specification. Simulation of power for misspecified models confirmed the results based on correlation methods but also illustrated the effect of model degrees of freedom on power.^

Detecting genetic and nutritional lung cancer risk factors related to folate metabolism using Bayesian generalized linear models

Complex diseases, such as cancer, are caused by various genetic and environmental factors, and their interactions. Joint analysis of these factors and their interactions would increase the power to detect risk factors but is statistically. Bayesian generalized linear models using student-t prior distributions on coefficients, is a novel method to simultaneously analyze genetic factors, environmental factors, and interactions. I performed simulation studies using three different disease models and demonstrated that the variable selection performance of Bayesian generalized linear models is comparable to that of Bayesian stochastic search variable selection, an improved method for variable selection when compared to standard methods. I further evaluated the variable selection performance of Bayesian generalized linear models using different numbers of candidate covariates and different sample sizes, and provided a guideline for required sample size to achieve a high power of variable selection using Bayesian generalize linear models, considering different scales of number of candidate covariates. ^ Polymorphisms in folate metabolism genes and nutritional factors have been previously associated with lung cancer risk. In this study, I simultaneously analyzed 115 tag SNPs in folate metabolism genes, 14 nutritional factors, and all possible genetic-nutritional interactions from 1239 lung cancer cases and 1692 controls using Bayesian generalized linear models stratified by never, former, and current smoking status. SNPs in MTRR were significantly associated with lung cancer risk across never, former, and current smokers. In never smokers, three SNPs in TYMS and three gene-nutrient interactions, including an interaction between SHMT1 and vitamin B12, an interaction between MTRR and total fat intake, and an interaction between MTR and alcohol use, were also identified as associated with lung cancer risk. These lung cancer risk factors are worthy of further investigation.^