Modeling survival and the development of second primary tumors among Hodgkin's disease patients

Hodgkin's disease (HD) is a cancer of the lymphatic system. Survivors of HD face varieties of consequent adverse effects, in which secondary primary tumors (SPT) is one of the most serious consequences. This dissertation is aimed to model time-to-SPT in the presence of death and HD relapses during follow-up.^ The model is designed to handle a mixture phenomenon of SPT and the influence of death. Relapses of HD are adjusted as a covariate. Proportional hazards framework is used to define SPT intensity function, which includes an exponential term to estimate explanatory variables. Death as a competing risk is considered according to different scenarios, depending on which terminal event comes first. Newton-Raphson method is used to estimate the parameter estimates in the end.^ The proposed method is applied to a real data set containing a group of HD patients. Several risk factors for the development of SPT are identified and the findings are noteworthy in the development of healthcare guidelines that may lead to the early detection or prevention of SPT.^

Determining the association between nativity status and mammography utilization among women in the United States using 2000 and 2005 NHIS data

Objective. To determine the association between nativity status and mammography utilization among women in the U.S. and assess whether demographic variables, socioeconomic factors healthcare access, breast cancer risk factors and acculturation variables were predictors in the relationship between nativity status and mammography in the past two years. ^ Methods. The NHIS collects demographic and health information using face-to-face interviews among a representative sample of the U.S. population and a cancer control module assessing screening behaviors is included every five years. Descriptive statistics were used to report demographic characteristics of women aged 40 and older who have received a mammogram in the last 2 years from 2000 and 2005. We used chi square analyses to determine statistically significant differences by mammography screening for each covariate. Logistic regression was used to determine whether demographic characteristics, socioeconomic characteristics, healthcare access, breast cancer risk factors and acculturation variables among foreign-born Hispanics affected the relationship between nativity status and mammography use in the past 2 years. ^ Results. In 2000, the crude model between nativity and mammography was significant but results were not significant after adjusting for health insurance, access and reported health status. Significant results were also reported for years in U.S. and mammography among foreign-born born women. In 2005, the crude model was also significant but results were not significant after adjusting for demographic factors. Furthermore, there was a significant finding between citizenship and mammography in the past 2 years. ^ Conclusions. Our study contributes to the literature as one of the first national-based studies assessing mammography in the past two years based on nativity status. Based on our findings, health insurance and access to care is an important predictor in mammography utilization among foreign-born women. For those with health care access, physician recommendation should further be assessed to determine whether women are made aware of mammography as a means to detect breast cancer at an early stage and further reduce the risk of mortality from the breast cancer.^

Whence a healthy mind: Correlation of physical fitness and academic performance among schoolchildren

Background. Public schools are a key forum in the fight for child health because of the opportunities they present for physical activity and fitness surveillance. However, because schools are evaluated and funded on the basis of standardized academic performance rather than physical activity, empirical research evaluating the connections between fitness and academic performance is needed to justify curriculum allocations to physical activity. ^ Methods. Analyses were based on a convenience sample of 315,092 individually-matched standardized academic (TAKS™) and fitness (FITNESSGRAM®) test records collected by 13 Texas school districts under state mandates. We categorized each fitness result in quintiles by age and gender and used a mixed effects regression model to compare the academic performance of the top and bottom fitness groups for each fitness test and grade level combination. ^ Results. All fitness variables except BMI showed significant, positive associations with academic performance after sociodemographic covariate adjustments, with effect sizes ranging from 0.07 (95% CI: 0.05,0.08) in girls trunklift-TAKS reading to 0.34 (0.32,0.35) in boys cardiovascular-TAKS math. Cardiovascular fitness showed the largest inter-quintile difference in TAKS score (32-75 points), followed by curl-ups. After an additional adjustment for BMI and curl-ups, cardiovascular associations peaked in 8th-9 th grades (maximum inter-quintile difference 142 TAKS points; effect size 0.75 (0.69,0.82) for 8th grade girls math) and showed dose-response characteristics across quintiles (p<0.001 for both genders and outcomes). BMI analysis demonstrated limited, non-linear association with academic performance after adjustment for sociodemographic, cardiovascular fitness and curl-up variables. Low-BMI Hispanic high school boys showed significantly lower TAKS scores than the moderate (but not high) BMI group. High-BMI non-Hispanic white high school girls showed significantly lower scores than the moderate (but not low) BMI group. ^ Conclusions. In this study, fitness was strongly and significantly related to academic performance. Cardiovascular fitness showed a distinct dose-response association with academic performance independent of other sociodemographic and fitness variables. The association peaked in late middle to early high school. The independent association of BMI to academic performance was only found in two sub-groups and was non-linear, with both low and high BMI posing risk relative to moderate BMI but not to each other. In light of our findings, we recommend that policymakers consider PE mandates in middle-high school and require linkage of academic and fitness records to facilitate longitudinal surveillance. School administrators should consider increasing PE time in pursuit of higher academic test scores, and PE practitioners should emphasize cardiovascular fitness over BMI reduction.^

The combined effect of pravastatin and aspirin on clinical cardiovascular events

The 3-hydroxy-3methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors, or statins, can achieve significant reductions in plasma low-density lipoprotein (LDL)-cholesterol levels. Experimental and clinical evidence now shows that some statins interfere with formation of atherosclerotic lesions independent of their hypolipidemic properties. Vulnerable plaque rupture can result in thrombus formation and artery occlusion; this plaque deterioration is responsible for most acute coronary syndromes, including myocardial infarction (MI), unstable angina, and coronary death, as well as coronary heart diseaseequivalent non-hemorrhagic stroke. Inhibition of HMG-CoA reductase has potential pleiotropic effects other than lipid-lowering, as statins block mevalonic acid production, a precursor to cholesterol and numerous other metabolites. Statins' beneficial effects on clinical events may also thus involve nonlipid-related mechanisms that modify endothelial function, inflammatory responses, plaque stability, and thrombus formation. Aspirin, routinely prescribed to post-MI patients as adjunct therapy, may potentiate statins beneficial effects, as aspirin does not compete metabolically with statins but acts similarly on atherosclerotic lesions. Common functions of both medications include inhibition of platelet activity and aggregation, reduction in atherosclerotic plaque macrophage cell count, and prevention of atherosclerotic vessel endothelial dysfunction. The Cholesterol and Recurrent Events (CARE) trial provides an ideal population in which to examine the combined effects of pravastatin and aspirin. Lipid levels, intermediate outcomes, are examined by pravastatin and aspirin status, and differences between the two pravastatin groups are found. A modified Cox proportional-hazards model with aspirin as a time-dependent covariate was used to determine the effect of aspirin and pravastatin on the clinical cardiovascular composite endpoint of coronary heart disease death, recurrent MI or stroke. Among those assigned to pravastatin, use of aspirin reduced the composite primary endpoint by 35%; this result was similar by gender, race, and diabetic status. Older patients demonstrated a nonsignificant 21% reduction in the primary outcome, whereas the younger had a significant reduction of 43% in the composite primary outcome. Secondary outcomes examined include coronary artery bypass graft (38% reduction), nonsurgical bypass, peripheral vascular disease, and unstable angina. Pravastatin and aspirin in a post-MI population was found to be a beneficial combination that seems to work through lipid and nonlipid, anti-inflammatory mechanisms. ^

Secondary left ventricular ejection fraction response to beta blocker therapy in heart failure patients

Chronic β-blocker treatment improves survival and left ventricular ejection fraction (LVEF) in patients with systolic heart failure (HF). Data on whether the improvement in LVEF after β-blocker therapy is sustained for a long term or whether there is a loss in LVEF after an initial gain is not known. Our study sought to determine the prevalence and prognostic role of secondary decline in LVEF in chronic systolic HF patients on β-blocker therapy and characterize these patients. Retrospective chart review of HF hospitalizations fulfilling Framingham Criteria was performed at the MEDVAMC between April 2000 and June 2006. Follow up vital status and recurrent hospitalizations were ascertained until May 2010. Three groups of patients were identified based on LVEF response to beta blockers; group A with secondary decline in LVEF following an initial increase, group B with progressive increase in LVEF and group C with progressive decline in LVEF. Covariate adjusted Cox proportional hazard models were used to examine differences in heart failure re-hospitalizations and all cause mortality between the groups. Twenty five percent (n=27) of patients had a secondary decline in LVEF following an initial gain. The baseline, peak and final LVEF in this group were 27.6±12%, 40.1±14% and 27.4±13% respectively. The mean nadir LVEF after decline was 27.4±13% and this decline occurred at a mean interval of 2.8±1.9 years from the day of beta blocker initiation. These patients were older, more likely to be whites, had advanced heart failure (NYHA class III/IV) more due to a non ischemic etiology compared to groups B & C. They were also more likely to be treated with metoprolol (p=0.03) compared to the other two groups. No significant differences were observed in combined risk of all cause mortality and HF re-hospitalization [hazard ratio 0.80, 95% CI 0.47 to 1.38, p=0.42]. No significant difference was observed in survival estimates between the groups. In conclusion, a late decline in LVEF does occur in a significant proportion of heart failure patients treated with beta blockers, more so in patients treated with metoprolol.^

Reconnaissance survey of Galveston Bay sediments for selective nonresidual trace metals

The nonresidual concentrations of five trace metals were determined for 322 sediments that were the product of a systematic sampling program of the entire Galveston Bay system. The nonresidual component of the trace metal concentration (e.g. that fraction of the metals that can be relatively easily removed from the sediments without complete destruction of the sediment particle) was considered to be more indicative of the anthropogenic metal pollution that has impacted the Galveston Bay ecosystem.^ For spatial analysis of the metal concentrations, the Galveston Bay system was divided into nine bay-areas, based on easily definable geological and geographical characteristics. Isopleth mapping analyses of these metal concentrations indicated a direct relationship with the $<$63$\mu$m fraction of the sediment (%FINE) in all of the bay areas. Covariate regression analyses indicated that position of the sediment within the Galveston Bay system (e.g. bay-area) was a better predictor of metal concentration than %FINE. Analysis of variance of the metals versus the bay-areas indicated that the five metals maintained a relatively constant order and magnitude of concentration for all the bay-areas.^ The major shipping channels of the Galveston Bay system, with their associated vessels and transported materials, are a likely source of metal pollution. However, these channels were not depositional corridors of high metal concentration. All metal concentration highs were found to be located away from the channels and associated with %FINE highs in the deeper portions of the bay-areas.^ Disturbance of the sediments, by the proposed widening and deepening of these channels, is not predicted to remobilize the trace metals. A more likely adverse effect on the health of the Galveston Bay ecosystem would come from the increase in turbidity of the water due to the dredging and in an extension of the salt water wedge farther north into the bay system. ^

Logistic regression with incompletely observed binary

Logistic regression is one of the most important tools in the analysis of epidemiological and clinical data. Such data often contain missing values for one or more variables. Common practice is to eliminate all individuals for whom any information is missing. This deletion approach does not make efficient use of available information and often introduces bias.^ Two methods were developed to estimate logistic regression coefficients for mixed dichotomous and continuous covariates including partially observed binary covariates. The data were assumed missing at random (MAR). One method (PD) used predictive distribution as weight to calculate the average of the logistic regressions performing on all possible values of missing observations, and the second method (RS) used a variant of resampling technique. Additional seven methods were compared with these two approaches in a simulation study. They are: (1) Analysis based on only the complete cases, (2) Substituting the mean of the observed values for the missing value, (3) An imputation technique based on the proportions of observed data, (4) Regressing the partially observed covariates on the remaining continuous covariates, (5) Regressing the partially observed covariates on the remaining continuous covariates conditional on response variable, (6) Regressing the partially observed covariates on the remaining continuous covariates and response variable, and (7) EM algorithm. Both proposed methods showed smaller standard errors (s.e.) for the coefficient involving the partially observed covariate and for the other coefficients as well. However, both methods, especially PD, are computationally demanding; thus for analysis of large data sets with partially observed covariates, further refinement of these approaches is needed. ^

REGRESSION ANALYSIS FOR STANDARDIZED MORTALITY RATIOS WITH APPLICATION TO OCCUPATIONAL FOLLOW-UP STUDIES

Traditional comparison of standardized mortality ratios (SMRs) can be misleading if the age-specific mortality ratios are not homogeneous. For this reason, a regression model has been developed which incorporates the mortality ratio as a function of age. This model is then applied to mortality data from an occupational cohort study. The nature of the occupational data necessitates the investigation of mortality ratios which increase with age. These occupational data are used primarily to illustrate and develop the statistical methodology.^ The age-specific mortality ratio (MR) for the covariates of interest can be written as MR(,ij...m) = ((mu)(,ij...m)/(theta)(,ij...m)) = r(.)exp (Z('')(,ij...m)(beta)) where (mu)(,ij...m) and (theta)(,ij...m) denote the force of mortality in the study and chosen standard populations in the ij...m('th) stratum, respectively, r is the intercept, Z(,ij...m) is the vector of covariables associated with the i('th) age interval, and (beta) is a vector of regression coefficients associated with these covariables. A Newton-Raphson iterative procedure has been used for determining the maximum likelihood estimates of the regression coefficients.^ This model provides a statistical method for a logical and easily interpretable explanation of an occupational cohort mortality experience. Since it gives a reasonable fit to the mortality data, it can also be concluded that the model is fairly realistic. The traditional statistical method for the analysis of occupational cohort mortality data is to present a summary index such as the SMR under the assumption of constant (homogeneous) age-specific mortality ratios. Since the mortality ratios for occupational groups usually increase with age, the homogeneity assumption of the age-specific mortality ratios is often untenable. The traditional method of comparing SMRs under the homogeneity assumption is a special case of this model, without age as a covariate.^ This model also provides a statistical technique to evaluate the relative risk between two SMRs or a dose-response relationship among several SMRs. The model presented has application in the medical, demographic and epidemiologic areas. The methods developed in this thesis are suitable for future analyses of mortality or morbidity data when the age-specific mortality/morbidity experience is a function of age or when there is an interaction effect between confounding variables needs to be evaluated. ^

SEVERITY OF IRON DEFICIENCY ANEMIA AND ITS RELATIONSHIP TO GROWTH AND MORBIDITY IN A POPULATION OF PRE-SCHOOLERS IN RURAL GUATEMALA (IRON DEFICIENCY, MORBIDITY, GROWTH)

The objectives of this study were to determine the nature of the relationship between severity of iron deficiency anemia, response to iron treatment, respiratory and gastrointestinal illness and weight change. Seventy-five pre-school children from rural Guatemala received daily oral iron therapy for an eleven week period, and were classified into one of three groups having different degrees of iron deficiency anemia. Anthropometric and biochemical data were collected prior and after iron treatment; morbidity data were collected throughout the period of treatment. The outcome variables were percentage weight change, percentage of total days ill with any type of symptom, percentage of total days ill with gastrointestinal symptoms, percentage of total days ill with respiratory symptoms, percentage of total days ill with combination syndrome symptoms. Age, sex and socio-economic status, were independent of any of the independent or outcome variables used. On the other hand, the level of hemoglobin covaried with the height of the children, the smallest children were the most severely anemic. The relationships between hemoglobin levels and weight change, frequency of morbidity (gastrointestinal, respiratory and combination syndrome) and total number of days ill with any symptomatology were investigated. No statistical significance was found in these analyses except when contrasting children with normal hemoglobin levels to iron deficient children, where the findings indicated the normal children experienced more gastrointestinal morbidity. The same relationship were again analyzed but including delta hemoglobin as covariate in the analysis, this latter one was found to be significant at 7% when the percentage of days ill from gastrointestinal morbidity was tested against the hemoglobin groups. The relationship found indicates that, all other covariates accounted for, the percentage of days ill from gastrointestinal morbidity will decrease approximately 1% for each 1% increase in delta of hemoglobin. ^

Improving adherence in African American women with uncontrolled primary hypertension

Background and Objectives: African American (AA) women are disproportionately affected with hypertension (HTN). The aim of this randomized controlled trial was to evaluate the effectiveness of a 6-week culturally-tailored educational intervention for AA women with primary HTN who lived in rural Northeast Texas. ^ Methods: Sixty AA women, 29 to 86 years (M 57.98 ±12.37) with primary HTN were recruited from four rural locations and randomized to intervention (n =30) and wait-list control groups ( n =30) to determine the effectiveness of the intervention on knowledge, attitudes, beliefs, social support, adherence to a hypertension regimen, and blood pressure (BP) control. Survey and BP measurements were collected at baseline, 3 weeks, 6 weeks (post intervention) and 6 months post intervention. Culturally-tailored educational classes were provided for 90 minutes once a week for 6 weeks in two local churches and a community center. The wait-list control group received usual care and were offered education at the conclusion of the data collection six months post-intervention. Linear mixed models were used to test for differences between the groups. ^ Results: A significant overall main effect (Time) was found for systolic blood pressure, F(3, 174) =11.104, p=.000, and diastolic blood pressure. F(3, 174) =4.781, p=.003 for both groups. Age was a significant covariate for diastolic blood pressure. F(1, 56) =6.798 p=.012. Participants 57 years or older (n=30) had lower diastolic BPS than participants younger than 57 (n=30). No significant differences were found between groups on knowledge, adherence, or attitudes. Participants with lower incomes had significantly less knowledge about HBP Prevention (r=.036, p=.006). ^ Conclusion: AA women who participated in a 6 week intervention program demonstrated a significant decrease in BP over a 6 month period regardless of whether they were in the intervention or control group. These rural AA women had a relatively good knowledge of HTN and reported an average level of compliance, compared to other populations. Satisfaction with the program was high and there was no attrition, suggesting that AA women will participate in research studies that are culturally tailored to them, held in familiar community locations, and conducted by a trusted person with whom they can identify. Future studies using a different program with larger sample sizes are warranted to try to decrease the high level of HTN-related complications in AA women. ^

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^

Slope estimation in a parametric measurement error model: A simulation study

In regression analysis, covariate measurement error occurs in many applications. The error-prone covariates are often referred to as latent variables. In this proposed study, we extended the study of Chan et al. (2008) on recovering latent slope in a simple regression model to that in a multiple regression model. We presented an approach that applied the Monte Carlo method in the Bayesian framework to the parametric regression model with the measurement error in an explanatory variable. The proposed estimator applied the conditional expectation of latent slope given the observed outcome and surrogate variables in the multiple regression models. A simulation study was presented showing that the method produces estimator that is efficient in the multiple regression model, especially when the measurement error variance of surrogate variable is large.^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^

Evaluation of the Hosmer -Lemeshow global goodness -of-fit statistic for mixed variable logistic regression models

The performance of the Hosmer-Lemeshow global goodness-of-fit statistic for logistic regression models was explored in a wide variety of conditions not previously fully investigated. Computer simulations, each consisting of 500 regression models, were run to assess the statistic in 23 different situations. The items which varied among the situations included the number of observations used in each regression, the number of covariates, the degree of dependence among the covariates, the combinations of continuous and discrete variables, and the generation of the values of the dependent variable for model fit or lack of fit.^ The study found that the $\rm\ C$g* statistic was adequate in tests of significance for most situations. However, when testing data which deviate from a logistic model, the statistic has low power to detect such deviation. Although grouping of the estimated probabilities into quantiles from 8 to 30 was studied, the deciles of risk approach was generally sufficient. Subdividing the estimated probabilities into more than 10 quantiles when there are many covariates in the model is not necessary, despite theoretical reasons which suggest otherwise. Because it does not follow a X$\sp2$ distribution, the statistic is not recommended for use in models containing only categorical variables with a limited number of covariate patterns.^ The statistic performed adequately when there were at least 10 observations per quantile. Large numbers of observations per quantile did not lead to incorrect conclusions that the model did not fit the data when it actually did. However, the statistic failed to detect lack of fit when it existed and should be supplemented with further tests for the influence of individual observations. Careful examination of the parameter estimates is also essential since the statistic did not perform as desired when there was moderate to severe collinearity among covariates.^ Two methods studied for handling tied values of the estimated probabilities made only a slight difference in conclusions about model fit. Neither method split observations with identical probabilities into different quantiles. Approaches which create equal size groups by separating ties should be avoided. ^

(Table 1) Age, fork length, tissue d15N and d13C and mercury content of arctic char (Salvelinus alpinus) across the Canadian Arctic

Among-lake variation in mercury (Hg) concentrations in landlocked Arctic char was examined in 27 char populations from remote lakes across the Canadian Arctic. A total of 520 landlocked Arctic char were collected from 27 lakes, as well as sediments and surface water from a subset of lakes in 1999, 2002, and 2005 to 2007. Size, length, age, and trophic position (d15N) of individual char were determined and relationships with total Hg (THg) concentrations investigated, to identify a common covariate for adjustment using analysis of covariance (ANCOVA). A subset of 216 char from 24 populations was used for spatial comparison, after length-adjustment. The influence of trophic position and food web length and abiotic characteristics such as location, geomorphology, lake area, catchment area, catchment-to-lake area ratio of the lakes on adjusted THg concentrations in char muscle tissue were then evaluated. Arctic char from Amituk Lake (Cornwallis Island) had the highest Hg concentrations (1.31 µg/g wet wt), while Tessisoak Lake (Labrador, 0.07 µg/g wet wt) had the lowest. Concentrations of THg were positively correlated with size, d15N, and age, respectively, in 88,71, and 58% of 24 char populations. Length and d15N were correlated in 67% of 24 char populations. Food chain length did not explain the differences in length-adjusted THg concentrations in char. No relationships between adjusted THg concentrations in char and latitude or longitude were found, however, THg concentrations in char showed a positive correlation with catchment-to-lake area ratio. Furthermore, we conclude that inputs from the surrounding environment may influence THg concentrations, and will ultimately affect THg concentrations in char as a result of predicted climate-driven changes that may occur in Arctic lake watersheds.