Structural equation modeling of the medical outcome study: Short Form 36 (SF-36)

The factorial validity of the SF-36 was evaluated using confirmatory factor analysis (CFA) methods, structural equation modeling (SEM), and multigroup structural equation modeling (MSEM). First, the measurement and structural model of the hypothesized SF-36 was explicated. Second, the model was tested for the validity of a second-order factorial structure, upon evidence of model misfit, determined the best-fitting model, and tested the validity of the best-fitting model on a second random sample from the same population. Third, the best-fitting model was tested for invariance of the factorial structure across race, age, and educational subgroups using MSEM.^ The findings support the second-order factorial structure of the SF-36 as proposed by Ware and Sherbourne (1992). However, the results suggest that: (a) Mental Health and Physical Health covary; (b) general mental health cross-loads onto Physical Health; (c) general health perception loads onto Mental Health instead of Physical Health; (d) many of the error terms are correlated; and (e) the physical function scale is not reliable across these two samples. This hierarchical factor pattern was replicated across both samples of health care workers, suggesting that the post hoc model fitting was not data specific. Subgroup analysis suggests that the physical function scale is not reliable across the "age" or "education" subgroups and that the general mental health scale path from Mental Health is not reliable across the "white/nonwhite" or "education" subgroups.^ The importance of this study is in the use of SEM and MSEM in evaluating sample data from the use of the SF-36. These methods are uniquely suited to the analysis of latent variable structures and are widely used in other fields. The use of latent variable models for self reported outcome measures has become widespread, and should now be applied to medical outcomes research. Invariance testing is superior to mean scores or summary scores when evaluating differences between groups. From a practical, as well as, psychometric perspective, it seems imperative that construct validity research related to the SF-36 establish whether this same hierarchical structure and invariance holds for other populations.^ This project is presented as three articles to be submitted for publication. ^

The effects of population density on the spread of disease

The objective of this study is to identify the relationship between population density and the initial stages of the spread of disease in a local population. This study proposes to concentrate on the question of how population density affects the distribution of the susceptible individuals in a local population and thus affects the spread of the disease, measles. Population density is measured by the average of the number of contacts with susceptible individuals by each individual in the population during a fixed-length time period. The term “contact with susceptible individuals” means sufficient contact between two people for the disease to pass from an infectious person to a susceptible person. The fixed-length time period is taken to be the average length of time an infected person is infectious without symptoms of the disease. For this study of measles, the time period will be seven days. ^ While much attention has been given to modeling the entire epidemic process of measles, attempts have not been made to study the characteristics of contact rates required to initiate an epidemic. This study explores the relationship between population density, given a specific herd immunity rate in the population, and initial rate of the spread of the disease by considering the underlying distribution of contacts with susceptibles by the individuals in the population. ^ This study does not seek to model an entire measles epidemic, but to model the above stated relationship for the local population within which the first infective person is introduced. This study describes the mathematical relationship between population density parameters and contact distribution parameters. ^ The results are displayed in graphs that show the effects of different population densities on the spread of disease. The results support the idea that the number of new infectives is strongly related to the distribution of susceptible contacts. The results also show large differences in the epidemic measures between populations with densities equal to four versus three. ^

A coalescent analysis for modeling the mutation process in colorectal cancer

Colorectal cancer is the forth most common diagnosed cancer in the United States. Every year about a hundred forty-seven thousand people will be diagnosed with colorectal cancer and fifty-six thousand people lose their lives due to this disease. Most of the hereditary nonpolyposis colorectal cancer (HNPCC) and 12% of the sporadic colorectal cancer show microsatellite instability. Colorectal cancer is a multistep progressive disease. It starts from a mutation in a normal colorectal cell and grows into a clone of cells that further accumulates mutations and finally develops into a malignant tumor. In terms of molecular evolution, the process of colorectal tumor progression represents the acquisition of sequential mutations. ^ Clinical studies use biomarkers such as microsatellite or single nucleotide polymorphisms (SNPs) to study mutation frequencies in colorectal cancer. Microsatellite data obtained from single genome equivalent PCR or small pool PCR can be used to infer tumor progression. Since tumor progression is similar to population evolution, we used an approach known as coalescent, which is well established in population genetics, to analyze this type of data. Coalescent theory has been known to infer the sample's evolutionary path through the analysis of microsatellite data. ^ The simulation results indicate that the constant population size pattern and the rapid tumor growth pattern have different genetic polymorphic patterns. The simulation results were compared with experimental data collected from HNPCC patients. The preliminary result shows the mutation rate in 6 HNPCC patients range from 0.001 to 0.01. The patients' polymorphic patterns are similar to the constant population size pattern which implies the tumor progression is through multilineage persistence instead of clonal sequential evolution. The results should be further verified using a larger dataset. ^

Body mass index trajectories and predictors among 3rd to 12th graders using growth curve mixture modeling the Child and Adolescent Trial for Cardiovascular Health (CATCH) study

This dissertation examined body mass index (BMI) growth trajectories and the effects of gender, ethnicity, dietary intake, and physical activity (PA) on BMI growth trajectories among 3rd to 12th graders (9-18 years of age). Growth curve model analysis was performed using data from The Child and Adolescent Trial for Cardiovascular Health (CATCH) study. The study population included 2909 students who were followed up from grades 3-12. The main outcome was BMI at grades 3, 4, 5, 8, and 12. ^ The results revealed that BMI growth differed across two distinct developmental periods of childhood and adolescence. Rate of BMI growth was faster in middle childhood (9-11 years old or 3rd - 5th grades) than in adolescence (11-18 years old or 5th - 12th grades). Students with higher BMI at 3rd grade (baseline) had faster rates of BMI growth. Three groups of students with distinct BMI growth trajectories were identified: high, average, and low. ^ Black and Hispanic children were more likely to be in the groups with higher baseline BMI and faster rates of BMI growth over time. The effects of gender or ethnicity on BMI growth differed across the three groups. The effects of ethnicity on BMI growth were weakened as the children aged. The effects of gender on BMI growth were attenuated in the groups with a large proportion of black and Hispanic children, i.e., “high” or “average” BMI trajectory group. After controlling for gender, ethnicity, and age at baseline, in the “high BMI trajectory”, rate of yearly BMI growth in middle childhood increased 0.102 for every 500 Kcals increase (p=0.049). No significant effects of percentage of energy from total fat and saturated fat on BMI growth were found. Baseline BMI increased 0.041 for every 30 minutes increased in moderate-to-vigorous PA (MVPA) in the “low BMI trajectory”, while Baseline BMI decreased 0.345 for every 30 minutes increased in vigorous PA (VPA) in the “high BMI trajectory”. ^ Childhood overweight and obesity interventions should start at the earliest possible ages, prior to 3rd grade and continue through grade school. Interventions should focus on all children, but specifically black and Hispanic children, who are more likely to be highest at-risk. Promoting VPA earlier in childhood is important for preventing overweight and obesity among children and adolescents. Interventions should target total energy intake, rather than only percentage of energy from total fat or saturated fat. ^

Racial/ethnic disparities in colorectal cancer screening and survival in a large nationwide population-based cohort

Background. Colorectal cancer (CRC) is the third most commonly diagnosed cancer (excluding skin cancer) in both men and women in the United States, with an estimated 148,810 new cases and 49,960 deaths in 2008 (1). Racial/ethnic disparities have been reported across the CRC care continuum. Studies have documented racial/ethnic disparities in CRC screening (2-9), but only a few studies have looked at these differences in CRC screening over time (9-11). No studies have compared these trends in a population with CRC and without cancer. Additionally, although there is evidence suggesting that hospital factors (e.g. teaching hospital status and NCI designation) are associated with CRC survival (12-16), no studies have sought to explain the racial/ethnic differences in survival by looking at differences in socio-demographics, tumor characteristics, screening, co-morbidities, treatment, as well as hospital characteristics. ^ Objectives and Methods. The overall goals of this dissertation were to describe the patterns and trends of racial/ethnic disparities in CRC screening (i.e. fecal occult blood test (FOBT), sigmoidoscopy (SIG) and colonoscopy (COL)) and to determine if racial/ethnic disparities in CRC survival are explained by differences in socio-demographic, tumor characteristics, screening, co-morbidities, treatment, and hospital factors. These goals were accomplished in a two-paper format.^ In Paper 1, "Racial/Ethnic Disparities and Trends in Colorectal Cancer Screening in Medicare Beneficiaries with Colorectal Cancer and without Cancer in SEER Areas, 1992-2002", the study population consisted of 50,186 Medicare beneficiaries diagnosed with CRC from 1992 to 2002 and 62,917 Medicare beneficiaries without cancer during the same time period. Both cohorts were aged 67 to 89 years and resided in 16 Surveillance, Epidemiology and End Results (SEER) regions of the United States. Screening procedures between 6 months and 3 years prior to the date of diagnosis for CRC patients and prior to the index date for persons without cancer were identified in Medicare claims. The crude and age-gender-adjusted percentages and odds ratios of receiving FOBT, SIG, or COL were calculated. Multivariable logistic regression was used to assess race/ethnicity on the odds of receiving CRC screening over time.^ Paper 2, "Racial/Ethnic Disparities in Colorectal Cancer Survival: To what extent are racial/ethnic disparities in survival explained by racial differences in socio-demographics, screening, co-morbidities, treatment, tumor or hospital characteristics", included a cohort of 50,186 Medicare beneficiaries diagnosed with CRC from 1992 to 2002 and residing in 16 SEER regions of the United States which were identified in the SEER-Medicare linked database. Survival was estimated using the Kaplan-Meier method. Cox proportional hazard modeling was used to estimate hazard ratios (HR) of mortality and 95% confidence intervals (95% CI).^ Results. The screening analysis demonstrated racial/ethnic disparities in screening over time among the cohort without cancer. From 1992 to 1995, Blacks and Hispanics were less likely than Whites to receive FOBT (OR=0.75, 95% CI: 0.65-0.87; OR=0.50, 95% CI: 0.34-0.72, respectively) but their odds of screening increased from 2000 to 2002 (OR=0.79, 95% CI: 0.72-0.85; OR=0.67, 95% CI: 0.54-0.75, respectively). Blacks and Hispanics were less likely than Whites to receive SIG from 1992 to 1995 (OR=0.75, 95% CI: 0.57-0.98; OR=0.29, 95% CI: 0.12-0.71, respectively), but their odds of screening increased from 2000 to 2002 (OR=0.79, 95% CI: 0.68-0.93; OR=0.50, 95% CI: 0.35-0.72, respectively).^ The survival analysis showed that Blacks had worse CRC-specific survival than Whites (HR: 1.33, 95% CI: 1.23-1.44), but this was reduced for stages I-III disease after full adjustment for socio-demographic, tumor characteristics, screening, co-morbidities, treatment and hospital characteristics (aHR=1.24, 95% CI: 1.14-1.35). Socioeconomic status, tumor characteristics, treatment and co-morbidities contributed to the reduction in hazard ratios between Blacks and Whites with stage I-III disease. Asians had better survival than Whites before (HR: 0.73, 95% CI: 0.64-0.82) and after (aHR: 0.80, 95% CI: 0.70-0.92) adjusting for all predictors for stage I-III disease. For stage IV, both Asians and Hispanics had better survival than Whites, and after full adjustment, survival improved (aHR=0.73, 95% CI: 0.63-0.84; aHR=0.74, 95% CI: 0.61-0.92, respectively).^ Conclusion. Screening disparities remain between Blacks and Whites, and Hispanics and Whites, but have decreased in recent years. Future studies should explore other factors that may contribute to screening disparities, such as physician recommendations and language/cultural barriers in this and younger populations.^ There were substantial racial/ethnic differences in CRC survival among older Whites, Blacks, Asians and Hispanics. Co-morbidities, SES, tumor characteristics, treatment and other predictor variables contributed to, but did not fully explain the CRC survival differences between Blacks and Whites. Future research should examine the role of quality of care, particularly the benefit of treatment and post-treatment surveillance, in racial disparities in survival.^

Modeling the survival of T-cell lymphocytes using compound branching processes

Radiotherapy has been a method of choice in cancer treatment for a number of years. Mathematical modeling is an important tool in studying the survival behavior of any cell as well as its radiosensitivity. One particular cell under investigation is the normal T-cell, the radiosensitivity of which may be indicative to the patient's tolerance to radiation doses.^ The model derived is a compound branching process with a random initial population of T-cells that is assumed to have compound distribution. T-cells in any generation are assumed to double or die at random lengths of time. This population is assumed to undergo a random number of generations within a period of time. The model is then used to obtain an estimate for the survival probability of T-cells for the data under investigation. This estimate is derived iteratively by applying the likelihood principle. Further assessment of the validity of the model is performed by simulating a number of subjects under this model.^ This study shows that there is a great deal of variation in T-cells survival from one individual to another. These variations can be observed under normal conditions as well as under radiotherapy. The findings are in agreement with a recent study and show that genetic diversity plays a role in determining the survival of T-cells. ^

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.^

Quality of life and functional status in a low -income, multiethnic HIV -positive population

The introduction of new medical treatments in recent years, commonly referred to as highly active antiretroviral therapy, has greatly increased the survival of patients with HIV/AIDS. As patients with HIV/AIDS continue to live longer, other important health-related outcomes, such as quality of life (QOL), should be thoroughly studied. There is also evidence that racial/ethnic minorities are disproportionately affected by HIV/AIDS, but potential health disparities among individuals already infected with HIV/AIDS have not been adequately examined in ethnically diverse populations. The purpose of this dissertation was to: (1) examine the impact of both demographic and behavioral variables on functional status and overall QOL among a population of ethnically diverse and economically disadvantaged HIV/AIDS patients; (2) examine the psychometric properties of a functional status measure—the Household and Leisure Time Activities questionnaire (HLTA); and (3) assess a proximal-distal theoretical framework for QOL using a full structural equation model in a population of patients with HIV/AIDS. Analyses were performed using data collected in the fall of 2000 from the project, Health and Work-Related Quality of Life and Health Risk Behaviors in a Multiethnic HIV-positive Population . Investigators from The University of Texas M.D. Anderson Cancer Center, The University of Texas-Houston Medical School, and The University of Texas School of Public Health conducted this project. The study site was the Thomas Street Clinic (TSC), a comprehensive HIV/AIDS care facility funded by the Harris County Hospital District (HCHD). TSC provides HIV/AIDS care to a diverse population of approximately 4000 medically indigent residents of Harris County. A systematic, consecutive sampling procedure yielded a sample size of 348 patients. Findings suggested that overall QOL, work-role functioning, household functioning, and leisure time functioning were impaired in this patient population. Results from the psychometric evaluation indicated that the HLTA was a reliable and valid measure of household and leisure time functioning status in a low-income multiethnic HIV-positive population. Finally, structural equation modeling of the proximal-distal QOL model suggested that this model was not a viable representation of the relationship between the study variables in this patient population. ^