Estimation and testing of parameters under constraints for correlated data

This dissertation work is motivated by problems encountered in the analysis of some toxicological and clinical trials data, where repeated measurements are made on each subject, and the investigator expects trends in mean response among dose groups and/or time points. There are two components to this research. The first component focuses on estimation of parameters subject to inequality constraints, when the covariance matrix of the unrestricted estimator is non-diagonal. In particular, statistical properties of several available constrained estimators are investigated theoretically and via simulations under different covariance structures. The second component is developing a simple, yet statistically appropriate methodology for testing hypotheses in a linear mixed effects model with an inequality constraint in the alternative. Since in many applications one cannot be certain about the normality of the data, a bootstrap based methodology using MINQUE-Williams' type test is implemented for testing the above hypotheses. The resulting methodology is illustrated by re-analyzing the blood mercury level data provided in Cao et al. (2011)

Evaluation of patient-reported delays and affordability-related barriers to care in head and neck cancer

OBJECTIVE: To examine the prevalence and predictors of patient-reported barriers to care among survivors of head and neck squamous cell carcinoma and the association with health-related quality of life (HRQOL) outcomes. STUDY DESIGN: Retrospective cohort study. SETTING: Outpatient oncology clinic at an academic tertiary care center. METHODS: Data were obtained from the UNC Health Registry/Cancer Survivorship Cohort. Barriers to care included self-reported delays in care and inability to obtain needed care due to cost. HRQOL was measured with validated questionnaires: general (PROMIS) and cancer specific (FACT-GP). RESULTS: The sample included 202 patients with head and neck squamous cell carcinoma with a mean age of 59.6 years (SD, 10.0). Eighty-two percent were male and 87% were White. Sixty-two patients (31%) reported at least 1 barrier to care. Significant predictors of a barrier to care in unadjusted analysis included age ≤60 years ( CONCLUSION: Delay- and affordability-related barriers are common among survivors of head and neck cancer and appear to be associated with significantly worse HRQOL outcomes. Certain sociodemographic groups appear to be more at risk of patient-reported barriers to care

The association of diabetes and obesity with prostate cancer aggressiveness among Black Americans and White Americans in a population-based study

Few studies have investigated the role of race in the association of diabetes and obesity with prostate cancer aggressiveness. Here we evaluate the independent association between diabetes and obesity with prostate cancer aggressiveness in White Americans and Black Americans

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Linear Mixed Effects Models under Inequality Constraints with Applications

<div><p>Constraints arise naturally in many scientific experiments/studies such as in, epidemiology, biology, toxicology, etc. and often researchers ignore such information when analyzing their data and use standard methods such as the analysis of variance (ANOVA). Such methods may not only result in a loss of power and efficiency in costs of experimentation but also may result poor interpretation of the data. In this paper we discuss constrained statistical inference in the context of linear mixed effects models that arise naturally in many applications, such as in repeated measurements designs, familial studies and others. We introduce a novel methodology that is broadly applicable for a variety of constraints on the parameters. Since in many applications sample sizes are small and/or the data are not necessarily normally distributed and furthermore error variances need not be homoscedastic (i.e. heterogeneity in the data) we use an empirical best linear unbiased predictor (EBLUP) type residual based bootstrap methodology for deriving critical values of the proposed test. Our simulation studies suggest that the proposed procedure maintains the desired nominal Type I error while competing well with other tests in terms of power. We illustrate the proposed methodology by re-analyzing a clinical trial data on blood mercury level. The methodology introduced in this paper can be easily extended to other settings such as nonlinear and generalized regression models.</p></div

Type I errors for log-normally distributed data.

<p>Type I errors for log-normally distributed data.</p

Power for heteroscedastic normally distributed data.

<p>Power for heteroscedastic normally distributed data.</p

Power for homoscedastic normally distributed data.

<p>Power for homoscedastic normally distributed data.</p

Flow chart for deriving Bootstrap data under the null hypothesis.

<p>Flow chart for deriving Bootstrap data under the null hypothesis.</p