Estimating the mean and standard deviation of concentration distributions using censored samples

Environmental data sets of pollutant concentrations in air, water, and soil frequently include unquantified sample values reported only as being below the analytical method detection limit. These values, referred to as censored values, should be considered in the estimation of distribution parameters as each represents some value of pollutant concentration between zero and the detection limit. Most of the currently accepted methods for estimating the population parameters of environmental data sets containing censored values rely upon the assumption of an underlying normal (or transformed normal) distribution. This assumption can result in unacceptable levels of error in parameter estimation due to the unbounded left tail of the normal distribution. With the beta distribution, which is bounded by the same range of a distribution of concentrations, $\rm\lbrack0\le x\le1\rbrack,$ parameter estimation errors resulting from improper distribution bounds are avoided. This work developed a method that uses the beta distribution to estimate population parameters from censored environmental data sets and evaluated its performance in comparison to currently accepted methods that rely upon an underlying normal (or transformed normal) distribution. Data sets were generated assuming typical values encountered in environmental pollutant evaluation for mean, standard deviation, and number of variates. For each set of model values, data sets were generated assuming that the data was distributed either normally, lognormally, or according to a beta distribution. For varying levels of censoring, two established methods of parameter estimation, regression on normal ordered statistics, and regression on lognormal ordered statistics, were used to estimate the known mean and standard deviation of each data set. The method developed for this study, employing a beta distribution assumption, was also used to estimate parameters and the relative accuracy of all three methods were compared. For data sets of all three distribution types, and for censoring levels up to 50%, the performance of the new method equaled, if not exceeded, the performance of the two established methods. Because of its robustness in parameter estimation regardless of distribution type or censoring level, the method employing the beta distribution should be considered for full development in estimating parameters for censored environmental data sets. ^

Evaluating statistical methods used to estimate the number of postsynaptic receptors.

Calcium levels in spines play a significant role in determining the sign and magnitude of synaptic plasticity. The magnitude of calcium influx into spines is highly dependent on influx through N-methyl D-aspartate (NMDA) receptors, and therefore depends on the number of postsynaptic NMDA receptors in each spine. We have calculated previously how the number of postsynaptic NMDA receptors determines the mean and variance of calcium transients in the postsynaptic density, and how this alters the shape of plasticity curves. However, the number of postsynaptic NMDA receptors in the postsynaptic density is not well known. Anatomical methods for estimating the number of NMDA receptors produce estimates that are very different than those produced by physiological techniques. The physiological techniques are based on the statistics of synaptic transmission and it is difficult to experimentally estimate their precision. In this paper we use stochastic simulations in order to test the validity of a physiological estimation technique based on failure analysis. We find that the method is likely to underestimate the number of postsynaptic NMDA receptors, explain the source of the error, and re-derive a more precise estimation technique. We also show that the original failure analysis as well as our improved formulas are not robust to small estimation errors in key parameters.

Isolation and {\it in vitro}-immunosuppressive characterization of a novel cyclosporine metabolite

Cyclosporine (CsA) has shown great benefit to organ transplant recipients, as an immunosuppressive drug. To optimize CsA immunosuppressive therapy, pharmacodynamic evaluation of serial patient serum samples after CsA administration, using mixed lymphocyte culture (MLC) assays, revealed in vitro serum immunosuppressive activity of a CsA-like, ether-extractable component, associated with good clinical outcome in vivo. Since the in vitro immunosuppressive CsA metabolites, M-17 and M-1, are erythrocyte-bound, the immunosuppressive activity demonstrated in patient serum suggests that other immunosuppressive metabolites need exist. To test this hypothesis and obtain CsA metabolites for study, ether-extracted bile from tritiated and nonradioactive CsA-treated pigs was processed by novel high performance liquid and thin-layer chromatography (HPLC and HPTLC) techniques. Initial MLC screening of potential metabolites revealed a component, designated M-E, to have immunosuppressive activity. Pig bile-derived M-E was characterized as a CsA metabolite, by radioactive CsA tracer studies, by 56% crossreactivity in CsA radioimmunoassay, and by mass spectrometric (MS) analysis. MS revealed a CsA ring structure, hydroxylated at a site other than at amino acid one. M-E was different than M-1 and M-17, as demonstrated by different retention properties for each metabolite, using HPTLC and a novel rhodamine B/ $\alpha$-cyclodextrin stain, and using HPLC, performed by Sandoz, that revealed M-E to be different than previously characterized metabolites. The immunosuppressive activity of M-E was quantified by determination of mean metabolite potency ratio in human MLC assays, which was found to be 0.79 $\pm$ 0.23 (CsA, 1.0). Similar to parent drug, M-E revealed inter-individual differences in its immunosuppressive activity. M-E demonstrates inhibition of IL-2 production by concanavalin A stimulated C3H mouse spleen cells, similar to CsA, as determined with an IL-2 dependent mouse cytotoxic T-cell line. ^

Inherited and noninherited sources of variation in metastasis and the growth rate of tumors: Implications for tumor evolution and therapy

I have undertaken measurements of the genetic (or inherited) and nongenetic (or noninherited) components of the variability of metastasis formation and tumor diameter doubling time in more than 100 metastatic lines from each of three murine tumors (sarcoma SANH, sarcoma SA4020, and hepatocarcinoma HCA-I) syngeneic to C3Hf/Kam mice. These lines were isolated twice from lung metastases and analysed immediately thereafter to obtain the variance to spontaneous lung metastasis and tumor diameter doubling time. Additional studies utilized cells obtained from within 4 passages of isolation. Under the assumption that no genetic differences in metastasis formation or diameter doubling time existed among the cells of a given line, the variance within a line would estimate nongenetic variation. The variability derived from differences between lines would represent genetic origin. The estimates of the genetic contribution to the variation of metastasis and tumor diameter doubling time were significantly greater than zero, but only in the metastatic lines of tumor SANH was genetic variation the major source of metastatic variability (contributing 53% of the variability). In the tumor cell lines of SA4020 and HCA-I, however, the contribution of nongenetic factors predominated over genetic factors in the variability of the number of metastasis and tumor diameter doubling time. A number of other parameters examined, such as DNA content, karyotype, and selection and variance analysis with passage in vivo, indicated that genetic differences existed within the cell lines and that these differences were probably created by genetic instability. The mean metastatic propensity of the lines may have increased somewhat during their isolation and isotransplantation, but the variance was only slightly affected, if at all. Analysis of the DNA profiles of the metastatic lines of SA4020 and HCA-I revealed differences between these lines and their primary parent tumors, but not among the SANH lines and their parent tumor. Furthermore, there was a direct correlation between the extent of genetic influence on metastasis formation and the ability of the tumor cells to develop resistance to cisplatinum. Thus although nongenetic factors might predominate in contributing to metastasis formation, it is probably genetic variation and genetic instability that cause the progression of tumor cells to a more metastatic phenotype and leads to the emergence of drug resistance. ^

Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

Assessing homogeneity in workplace exposure under stationary and non-stationary scenarios

In most epidemiological studies, historical monitoring data are scant and must be pooled to identify occupational groups with homogeneous exposures. Homogeneity of exposure is generally assessed in a group of workers who share a common job title or work in a common area. While published results suggest that the degree of homogeneity varies widely across job groups, less is known whether such variation differs across industrial sectors, classes of contaminants, or in the methods used to group workers. Relying upon a compilation of results presented in the literature, patterns of homogeneity among nearly 500 occupational groups of workers were evaluated on the basis of type of industry and agent. Additionally, effects of the characteristics of the sampling strategy on estimated indicators of homogeneity of exposure were assessed. ^ Exposure profiles for occupational groups of workers have typically been assessed under the assumption of stationarity, i.e., the mean exposure level and variance of the distribution that describes the underlying population of exposures are constant over time. Yet, the literature has shown that occupational exposures have declined in the last decades. This renders traditional methods for the description of exposure profiles inadequate. Thus, work was needed to develop appropriate methods to assess homogeneity for groups of workers whose exposures have changed over time. A study was carried out applying mixed effects models with a term for temporal trend to appropriately describe exposure profiles of groups of workers in the nickel-producing industry over a 20-year period. Using a sub-set of groups of nickel-exposed workers, another study was conducted to develop and apply a framework to evaluate the assumption of stationarity of the variances in the presence of systematic changes in exposure levels over time. ^

Outcome-based adaptive randomization for binary data in clinical trials---Compare and contrast

Monte Carlo simulation has been conducted to investigate parameter estimation and hypothesis testing in some well known adaptive randomization procedures. The four urn models studied are Randomized Play-the-Winner (RPW), Randomized Pôlya Urn (RPU), Birth and Death Urn with Immigration (BDUI), and Drop-the-Loses Urn (DL). Two sequential estimation methods, the sequential maximum likelihood estimation (SMLE) and the doubly adaptive biased coin design (DABC), are simulated at three optimal allocation targets that minimize the expected number of failures under the assumption of constant variance of simple difference (RSIHR), relative risk (ORR), and odds ratio (OOR) respectively. Log likelihood ratio test and three Wald-type tests (simple difference, log of relative risk, log of odds ratio) are compared in different adaptive procedures. ^ Simulation results indicates that although RPW is slightly better in assigning more patients to the superior treatment, the DL method is considerably less variable and the test statistics have better normality. When compared with SMLE, DABC has slightly higher overall response rate with lower variance, but has larger bias and variance in parameter estimation. Additionally, the test statistics in SMLE have better normality and lower type I error rate, and the power of hypothesis testing is more comparable with the equal randomization. Usually, RSIHR has the highest power among the 3 optimal allocation ratios. However, the ORR allocation has better power and lower type I error rate when the log of relative risk is the test statistics. The number of expected failures in ORR is smaller than RSIHR. It is also shown that the simple difference of response rates has the worst normality among all 4 test statistics. The power of hypothesis test is always inflated when simple difference is used. On the other hand, the normality of the log likelihood ratio test statistics is robust against the change of adaptive randomization procedures. ^

THE RELATIONSHIP BETWEEN DEGREE OF BLOOD PRESSURE REDUCTION AND MORTALITY AMONG HYPERTENSIVES IN THE HYPERTENSION DETECTION AND FOLLOW-UP PROGRAM

The relationship between degree of diastolic blood pressure (DBP) reduction and mortality was examined among hypertensives, ages 30-69, in the Hypertension Detection and Follow-up Program (HDFP). The HDFP was a multi-center community-based trial, which followed 10,940 hypertensive participants for five years. One-year survival was required for inclusion in this investigation since the one-year annual visit was the first occasion where change in blood pressure could be measured on all participants. During the subsequent four years of follow-up on 10,052 participants, 568 deaths occurred. For levels of change in DBP and for categories of variables related to mortality, the crude mortality rate was calculated. Time-dependent life tables were also calculated so as to utilize available blood pressure data over time. In addition, the Cox life table regression model, extended to take into account both time-constant and time-dependent covariates, was used to examine the relationship change in blood pressure over time and mortality.^ The results of the time-dependent life table and time-dependent Cox life table regression analyses supported the existence of a quadratic function which modeled the relationship between DBP reduction and mortality, even after adjusting for other risk factors. The minimum mortality hazard ratio, based on a particular model, occurred at a DBP reduction of 22.6 mm Hg (standard error = 10.6) in the whole population and 8.5 mm Hg (standard error = 4.6) in the baseline DBP stratum 90-104. After this reduction, there was a small increase in the risk of death. There was not evidence of the quadratic function after fitting the same model using systolic blood pressure. Methodologic issues involved in studying a particular degree of blood pressure reduction were considered. The confidence interval around the change corresponding to the minimum hazard ratio was wide and the obtained blood pressure level should not be interpreted as a goal for treatment. Blood pressure reduction was attributed, not only to pharmacologic therapy, but also to regression to the mean, and to other unknown factors unrelated to treatment. Therefore, the surprising results of this study do not provide direct implications for treatment, but strongly suggest replication in other populations. ^

Contribution of the genes of the renin-angiotensin system to interindividual differences in blood pressure levels in Caucasians from Rochester, Minnesota

The Renin-Angiotensin system (RAS) regulates blood pressure through its effects on vascular tone, renal hemodynamics, and renal sodium and fluid balance. The genes encoding the four major components of the RAS, angiotensinogen, renin, angiotensin I-converting enzyme (ACE), and angiotensin II receptor type 1 (AT1), have been investigated as candidate genes in the pathogenesis of essential hypertension. However, studies have primarily focused on small samples of diseased individuals, and, therefore, have provided little information about the determinants of interindividual variation in blood pressure (BP) in the general population.^ Using data from a large population-based sample from Rochester, MN, I have evaluated the contribution of variation in the region of the RAS genes to interindividual variation in systolic, diastolic, and mean arterial pressure in the population-at-large. Marker genotype data from four polymorphisms located within or very near these genes were first collected on 3,974 individuals from 583 randomly ascertained three-generation pedigrees. Haseman-Elston regression and variance component methods of linkage analysis were then carried out to estimate the proportion of interindividual variance in BP attributable to the effects of variation at these four measured loci.^ A significant effect of the ACE locus on interindividual variation in mean arterial pressure (MAP) was detected in a sample of siblings belonging to the youngest generation. After allowing for measured covariates, this effect accounted for 15-25% of the interindividual variance in MAP, and was even greater in a subset with a positive family history of hypertension. When gender-specific analyses were carried out, this effect was significant in males but not in females. Extended pedigree analyses also provided evidence for an effect of the ACE locus on interindividual variation in MAP, but no difference between males and females was observed. Circumstantial evidence suggests that the ACE gene itself may be responsible for the observed effects on BP, although the possibility that other genes in the region may be at play cannot be excluded.^ No definitive evidence for an effect of the renin, angiotensinogen, or AT1 loci on interindividual variation in BP was obtained in this study, suggesting that the impact of these genes on BP may not be great in the Caucasian population-at-large. However, this does not preclude a larger effect of these genes in some subsets of individuals, especially among those with clinically manifest hypertension or coronary heart disease, or in other populations. ^

Application of stellar photometry to the analysis of microarray images

Improvements in the analysis of microarray images are critical for accurately quantifying gene expression levels. The acquisition of accurate spot intensities directly influences the results and interpretation of statistical analyses. This dissertation discusses the implementation of a novel approach to the analysis of cDNA microarray images. We use a stellar photometric model, the Moffat function, to quantify microarray spots from nylon microarray images. The inherent flexibility of the Moffat shape model makes it ideal for quantifying microarray spots. We apply our novel approach to a Wilms' tumor microarray study and compare our results with a fixed-circle segmentation approach for spot quantification. Our results suggest that different spot feature extraction methods can have an impact on the ability of statistical methods to identify differentially expressed genes. We also used the Moffat function to simulate a series of microarray images under various experimental conditions. These simulations were used to validate the performance of various statistical methods for identifying differentially expressed genes. Our simulation results indicate that tests taking into account the dependency between mean spot intensity and variance estimation, such as the smoothened t-test, can better identify differentially expressed genes, especially when the number of replicates and mean fold change are low. The analysis of the simulations also showed that overall, a rank sum test (Mann-Whitney) performed well at identifying differentially expressed genes. Previous work has suggested the strengths of nonparametric approaches for identifying differentially expressed genes. We also show that multivariate approaches, such as hierarchical and k-means cluster analysis along with principal components analysis, are only effective at classifying samples when replicate numbers and mean fold change are high. Finally, we show how our stellar shape model approach can be extended to the analysis of 2D-gel images by adapting the Moffat function to take into account the elliptical nature of spots in such images. Our results indicate that stellar shape models offer a previously unexplored approach for the quantification of 2D-gel spots. ^

Racial disparities in CD4 counts at initial HIV-1 diagnosis: Analysis of the Adult Spectrum of HIV disease dataset and public health implications

Introduction. The HIV/AIDS disease burden disproportionately affects minority populations, specifically African Americans. While sexual risk behaviors play a role in the observed HIV burden, other factors including gender, age, socioeconomics, and barriers to healthcare access may also be contributory. The goal of this study was to determine how far down the HIV/AIDS disease process people of different ethnicities first present for healthcare. The study specifically analyzed the differences in CD4 cell counts at the initial HIV-1 diagnosis with respect to ethnicity. The study also analyzed racial differences in HIV/AIDS risk factors. ^ Methods. This is a retrospective study using data from the Adult Spectrum of HIV Disease (ASD), collected by the City of Houston Department of Health. The ASD database contains information on newly reported HIV cases in the Harris County District Hospitals between 1989 and 2000. Each patient had an initial and a follow-up report. The extracted variables of interest from the ASD data set were CD4 counts at the initial HIV diagnosis, race, gender, age at HIV diagnosis and behavioral risk factors. One-way ANOVA was used to examine differences in baseline CD4 counts at HIV diagnosis between racial/ethnic groups. Chi square was used to analyze racial differences in risk factors. ^ Results. The analyzed study sample was 4767. The study population was 47% Black, 37% White and 16% Hispanic [p<0.05]. The mean and median CD4 counts at diagnosis were 254 and 193 cells per ml, respectively. At the initial HIV diagnosis Blacks had the highest average CD4 counts (285), followed by Whites (233) and Hispanics (212) [p<0.001 ]. These statistical differences, however, were only observed with CD4 counts above 350 [p<0.001], even when adjusted for age at diagnosis and gender [p<0.05]. Looking at risk factors, Blacks were mostly affected by intravenous drug use (IVDU) and heterosexuality, whereas Whites and Hispanics were more affected by male homosexuality [ p<0.05]. ^ Conclusion. (1) There were statistical differences in CD4 counts with respect to ethnicity, but these differences only existed for CD4 counts above 350. These differences however do not appear to have clinical significance. Antithetically, Blacks had the highest CD4 counts followed by Whites and Hispanics. (2) 50% of this study group clinically had AIDS at their initial HIV diagnosis (median=193), irrespective of ethnicity. It was not clear from data analysis if these observations were due to failure of early HIV surveillance, HIV testing policies or healthcare access. More studies need to be done to address this question. (3) Homosexuality and bisexuality were the biggest risk factors for Whites and Hispanics, whereas for Blacks were mostly affected by heterosexuality and IVDU, implying a need for different public health intervention strategies for these racial groups. ^

Genome-wide association study of LDL-cholesterol over ApoB ratio

Genome-Wide Association Study analytical (GWAS) methods were applied in a large biracial sample of individuals to investigate variation across the genome for its association with a surrogate low-density lipoprotein (LDL) particle size phenotype, the ratio of LDL-cholesterol level over ApoB level. Genotyping was performed on the Affymetrix 6.0 GeneChip with approximately one million single nucleotide polymorphisms (SNPs). The ratio of LDL cholesterol to ApoB was calculated, and association tests used multivariable linear regression analysis with an additive genetic model after adjustment for the covariates sex, age and BMI. Association tests were performed separately in African Americans and Caucasians. There were 9,562 qualified individuals in the Caucasian group and 3,015 qualified individuals in the African American group. Overall, in Caucasians two statistically significant loci were identified as being associated with the ratio of LDL-cholesterol over ApoB: rs10488699 (p<5 x10-8, 11q23.3 near BUD13) and the SNP rs964184 (p<5 x10-8 11q23.3 near ZNF259). We also found rs12286037 ((p<4x10-7) (11q23.3) near APOA5/A4/C3/A1 with suggestive associate in the Caucasian sample. In exploratory analyses, a difference in the pattern of association between individuals taking and not taking LDL-cholesterol lowering medications was observed. Individuals who were not taking medications had smaller p-value than those taking medication. In the African-American group, there were no significant (p<5x10-8) or suggestive associations (p<4x10-7) with the ratio of LDL-cholesterol over ApoB after adjusting for age, BMI, and sex and comparing individuals with and without LDL-cholesterol lowering medication. Conclusions: There were significant and suggestive associations between SNP genotype and the ratio of LDL-cholesterol to ApoB in Caucasians, but these associations may be modified by medication treatment.^

A Bayesian approach to estimating the intraclass correlation coefficient

A Bayesian approach to estimating the intraclass correlation coefficient was used for this research project. The background of the intraclass correlation coefficient, a summary of its standard estimators, and a review of basic Bayesian terminology and methodology were presented. The conditional posterior density of the intraclass correlation coefficient was then derived and estimation procedures related to this derivation were shown in detail. Three examples of applications of the conditional posterior density to specific data sets were also included. Two sets of simulation experiments were performed to compare the mean and mode of the conditional posterior density of the intraclass correlation coefficient to more traditional estimators. Non-Bayesian methods of estimation used were: the methods of analysis of variance and maximum likelihood for balanced data; and the methods of MIVQUE (Minimum Variance Quadratic Unbiased Estimation) and maximum likelihood for unbalanced data. The overall conclusion of this research project was that Bayesian estimates of the intraclass correlation coefficient can be appropriate, useful and practical alternatives to traditional methods of estimation. ^

The association between CD4+ lymphocyte count and measures of body composition in HIV-infected men

Purpose. This project was designed to describe the association between wasting and CD4 cell counts in HIV-infected men in order to better understand the role of wasting in progression of HIV infection.^ Methods. Baseline and prevalence data were collected from a cross-sectional survey of 278 HIV-infected men seen at the Houston Veterans Affairs Medical Center Special Medicine Clinic, from June 1, 1991 to January 1, 1994. A follow-up study was conducted among those at risk, to investigate the incidence of wasting and the association between wasting and low CD4 cell counts. Wasting was described by four methods. Z-scores for age-, sex-, and height-adjusted weight; sex-, and age-adjusted mid-arm muscle circumference (MAMC); and fat-free mass; and the ratio of extra-cellular mass (ECM) to body-cell mass (BCM) $>$ 1.20. FFM, ECM, and BCM were estimated from bioelectrical impedance analysis. MAMC was calculated from triceps skinfold and mid-arm circumference. The relationship between wasting and covariates was examined with logistic regression in the cross-sectional study, and with Poisson regression in the follow-up study. The association between death and wasting was examined with Cox's regression.^ Results. The prevalence of wasting ranged from 5% (weight and ECM:BCM) to almost 14% (MAMC and FFM) among the 278 men examined. The odds of wasting, associated with baseline CD4 cell count $<$200, was significant for each method but weight, and ranged from 4.6 to 12.7. Use of antiviral therapy was significantly protective of MAMC, FFM and ECM:BCM (OR $\approx$ 0.2), whereas the need for antibacterial therapy was a risk (OR 3.1, 95% CI 1.1-8.7). The average incidence of wasting ranged from 4 to 16 per 100 person-years among the approximately 145 men followed for 160 person-years. Low CD4 cell count seemed to increase the risk of wasting, but statistical significance was not reached. The effect of the small sample size on the power to detect a significant association should be considered. Wasting, by MAMC and FFM, was significantly associated with death, after adjusting for baseline serum albumin concentration and CD4 cell count.^ Conclusions. Wasting by MAMC and FFM were strongly associated with baseline CD4 cell counts in both the prevalence and incidence study and strong predictors of death. Of the two methods, MAMC is convenient, has available reference population data, may be the most appropriate for assessing the nutritional status of HIV-infected men. ^

CHARACTERIZATION OF THE COUNT RATE PERFORMANCE AND EVALUATION OF THE EFFECTS OF HIGH COUNT RATES ON MODERN GAMMA CAMERAS

CHARACTERIZATION OF THE COUNT RATE PERFORMANCE AND EVALUATION OF THE EFFECTS OF HIGH COUNT RATES ON MODERN GAMMA CAMERAS Michael Stephen Silosky, B.S. Supervisory Professor: S. Cheenu Kappadath, Ph.D. Evaluation of count rate performance (CRP) is an integral component of gamma camera quality assurance and measurement of system dead time (τ) is important for quantitative SPECT. The CRP of three modern gamma cameras was characterized using established methods (Decay and Dual Source) under a variety of experimental conditions. For the Decay method, input count rate was plotted against observed count rate and fit to the paralyzable detector model (PDM) to estimate τ (Rates method). A novel expression for observed counts as a function of measurement time interval was derived and the observed counts were fit to this expression to estimate τ (Counts method). Correlation and Bland-Altman analysis were performed to assess agreement in estimates of τ between methods. The dependencies of τ on energy window definition and incident energy spectrum were characterized. The Dual Source method was also used to estimate τ and its agreement with the Decay method under identical conditions and the effects of total activity and the ratio of source activities were investigated. Additionally, the effects of count rate on several performance metrics were evaluated. The CRP curves for each system agreed with the PDM at low count rates but deviated substantially at high count rates. Estimates of τ for the paralyzable portion of the CRP curves using the Rates and Counts methods were highly correlated (r=0.999) but with a small (~6%) difference. No significant difference was observed between the highly correlated estimates of τ using the Decay or Dual Source methods under identical experimental conditions (r=0.996). Estimates of τ increased as a power-law function with decreasing ratio of counts in the photopeak to the total counts and linearly with decreasing spectral effective energy. Dual Source method estimates of τ varied as a quadratic with the ratio of the single source to combined source activities and linearly with total activity used across a large range. Image uniformity, spatial resolution, and energy resolution degraded linearly with count rate and image distorting effects were observed. Guidelines for CRP testing and a possible method for the correction of count rate losses for clinical images have been proposed.