A multivariate frailty model for disease recurrences and survival

A multivariate frailty hazard model is developed for joint-modeling of three correlated time-to-event outcomes: (1) local recurrence, (2) distant recurrence, and (3) overall survival. The term frailty is introduced to model population heterogeneity. The dependence is modeled by conditioning on a shared frailty that is included in the three hazard functions. Independent variables can be included in the model as covariates. The Markov chain Monte Carlo methods are used to estimate the posterior distributions of model parameters. The algorithm used in present application is the hybrid Metropolis-Hastings algorithm, which simultaneously updates all parameters with evaluations of gradient of log posterior density. The performance of this approach is examined based on simulation studies using Exponential and Weibull distributions. We apply the proposed methods to a study of patients with soft tissue sarcoma, which motivated this research. Our results indicate that patients with chemotherapy had better overall survival with hazard ratio of 0.242 (95% CI: 0.094 - 0.564) and lower risk of distant recurrence with hazard ratio of 0.636 (95% CI: 0.487 - 0.860), but not significantly better in local recurrence with hazard ratio of 0.799 (95% CI: 0.575 - 1.054). The advantages and limitations of the proposed models, and future research directions are discussed. ^

Statistical and methodological challenges for disaster preparedness and medical needs assessment in Rio Grande Valley of Texas

In recent years, disaster preparedness through assessment of medical and special needs persons (MSNP) has taken a center place in public eye in effect of frequent natural disasters such as hurricanes, storm surge or tsunami due to climate change and increased human activity on our planet. Statistical methods complex survey design and analysis have equally gained significance as a consequence. However, there exist many challenges still, to infer such assessments over the target population for policy level advocacy and implementation. ^ Objective. This study discusses the use of some of the statistical methods for disaster preparedness and medical needs assessment to facilitate local and state governments for its policy level decision making and logistic support to avoid any loss of life and property in future calamities. ^ Methods. In order to obtain precise and unbiased estimates for Medical Special Needs Persons (MSNP) and disaster preparedness for evacuation in Rio Grande Valley (RGV) of Texas, a stratified and cluster-randomized multi-stage sampling design was implemented. US School of Public Health, Brownsville surveyed 3088 households in three counties namely Cameron, Hidalgo, and Willacy. Multiple statistical methods were implemented and estimates were obtained taking into count probability of selection and clustering effects. Statistical methods for data analysis discussed were Multivariate Linear Regression (MLR), Survey Linear Regression (Svy-Reg), Generalized Estimation Equation (GEE) and Multilevel Mixed Models (MLM) all with and without sampling weights. ^ Results. Estimated population for RGV was 1,146,796. There were 51.5% female, 90% Hispanic, 73% married, 56% unemployed and 37% with their personal transport. 40% people attained education up to elementary school, another 42% reaching high school and only 18% went to college. Median household income is less than $15,000/year. MSNP estimated to be 44,196 (3.98%) [95% CI: 39,029; 51,123]. All statistical models are in concordance with MSNP estimates ranging from 44,000 to 48,000. MSNP estimates for statistical methods are: MLR (47,707; 95% CI: 42,462; 52,999), MLR with weights (45,882; 95% CI: 39,792; 51,972), Bootstrap Regression (47,730; 95% CI: 41,629; 53,785), GEE (47,649; 95% CI: 41,629; 53,670), GEE with weights (45,076; 95% CI: 39,029; 51,123), Svy-Reg (44,196; 95% CI: 40,004; 48,390) and MLM (46,513; 95% CI: 39,869; 53,157). ^ Conclusion. RGV is a flood zone, most susceptible to hurricanes and other natural disasters. People in the region are mostly Hispanic, under-educated with least income levels in the U.S. In case of any disaster people in large are incapacitated with only 37% have their personal transport to take care of MSNP. Local and state government’s intervention in terms of planning, preparation and support for evacuation is necessary in any such disaster to avoid loss of precious human life. ^ Key words: Complex Surveys, statistical methods, multilevel models, cluster randomized, sampling weights, raking, survey regression, generalized estimation equations (GEE), random effects, Intracluster correlation coefficient (ICC).^

Multivariate methods for correlated effects sizes

Current statistical methods for estimation of parametric effect sizes from a series of experiments are generally restricted to univariate comparisons of standardized mean differences between two treatments. Multivariate methods are presented for the case in which effect size is a vector of standardized multivariate mean differences and the number of treatment groups is two or more. The proposed methods employ a vector of independent sample means for each response variable that leads to a covariance structure which depends only on correlations among the $p$ responses on each subject. Using weighted least squares theory and the assumption that the observations are from normally distributed populations, multivariate hypotheses analogous to common hypotheses used for testing effect sizes were formulated and tested for treatment effects which are correlated through a common control group, through multiple response variables observed on each subject, or both conditions.^ The asymptotic multivariate distribution for correlated effect sizes is obtained by extending univariate methods for estimating effect sizes which are correlated through common control groups. The joint distribution of vectors of effect sizes (from $p$ responses on each subject) from one treatment and one control group and from several treatment groups sharing a common control group are derived. Methods are given for estimation of linear combinations of effect sizes when certain homogeneity conditions are met, and for estimation of vectors of effect sizes and confidence intervals from $p$ responses on each subject. Computational illustrations are provided using data from studies of effects of electric field exposure on small laboratory animals. ^

THE USE OF MULTIVARIATE ANALYSIS OF COVARIANCE IN EVALUATING THE COMPARABILITY OF LABORATORY DETERMINATIONS IN COOPERATIVE STUDIES

The role of clinical chemistry has traditionally been to evaluate acutely ill or hospitalized patients. Traditional statistical methods have serious drawbacks in that they use univariate techniques. To demonstrate alternative methodology, a multivariate analysis of covariance model was developed and applied to the data from the Cooperative Study of Sickle Cell Disease.^ The purpose of developing the model for the laboratory data from the CSSCD was to evaluate the comparability of the results from the different clinics. Several variables were incorporated into the model in order to control for possible differences among the clinics that might confound any real laboratory differences.^ Differences for LDH, alkaline phosphatase and SGOT were identified which will necessitate adjustments by clinic whenever these data are used. In addition, aberrant clinic values for LDH, creatinine and BUN were also identified.^ The use of any statistical technique including multivariate analysis without thoughtful consideration may lead to spurious conclusions that may not be corrected for some time, if ever. However, the advantages of multivariate analysis far outweigh its potential problems. If its use increases as it should, the applicability to the analysis of laboratory data in prospective patient monitoring, quality control programs, and interpretation of data from cooperative studies could well have a major impact on the health and well being of a large number of individuals. ^

Comparing Campus Discipline Rates: A Multivariate Approach for Identifying Schools with Significantly Different than Expected Exclusionary Discipline Rates

Campus behavior management is important for ensuring classroom order and promoting positive academic outcomes. Previous studies have shown the importance of individual student and campus personnel characteristics and campus context for explaining campus discipline rates (e.g., rates of suspension and expulsion). Assessing campus discipline rates, while controlling for these individual and campus characteristics, is important for the monitoring, evaluation, and intervention role of policymakers as well as state and federal level education agencies. Systems or metrics exist that measure other student outcomes (i.e., academic performance) with controls for individual and campus characteristics, but none exist that monitor these differences for discipline rates across campuses. In this paper, we use a multivariate model to analyze a longitudinal, statewide dataset for all secondary students in Texas from 2000 to 2008 in order to examine how campus discipline rates differ across schools with statistically similar students, teachers, and campus characteristics. The findings are important for understanding that some schools with similar characteristics have significantly different exclusionary discipline rates, and they are important for informing policy and agency level decision-making. The methodology described can easily be used by monitoring agencies as well as local school districts.

Prevalence of Staphylococcus aureus colonization among high-risk HIV populations

Staphylococcus aureus is a common microorganism in humans, typically colonizing the nasopharynx, skin and other mucosal surfaces. It is among the most frequent causes of clinically-significant bacterial infections accounting for increased morbidity and mortality among individuals with HIV/AIDS. Evidence of higher colonization rates among high-risk HIV populations have been observed however, prevalence estimates have varied. Additionally, behavioral, biological, and/or environmental factors that may account for these high colonization rates are not understood. Previous literature on clinic-based surveys were subject to considerable biases. Additionally, representative samples of high-risk HIV populations were difficult to obtain due in part to an underrepresentation of individuals who may not regularly obtain health care. ^ The main objective of this project is to determine the prevalence of methicillin-sensitive S. aureus (MSSA) and methicillin-resistant (MRSA) nasal colonization in two populations: 1) men who have sex with men (MSM) and 2) injection drug users (IDU). Both of these populations are included in the third round of the National HIV Behavioral Surveillance System (NHBS) in Houston, Texas. ^ In the NHBS-MSM3 study, logistic regression was used to report odds ratios and 95% confidence intervals (CI). For the NHBS-IDU3 study, to account for the lack of independence between samples, the method of generalized estimating equations was utilized to report adjusted odds ratios and 95% CI. The NHBS-MSM3 study enrolled 202 participants with a MSSA colonization rate of 26.7% and MRSA rate of 3%. In the NHBS-IDU3 study, 18.4% were nasally colonized with MSSA and 5.7% were nasally colonized with MRSA. Among the NHBS-MSM3 population, high-risk sexual practices were associated with colonization. For the NHBS-IDU3 population, age, marital status, employment status, and the presence of scabs, were associated with colonization status when controlling for size of recruitment network. In multivariate GEE analyses, the use of antiretroviral medications and age remained significantly associated with S. aureus nasal colonization when controlling for size of recruitment network and gender. In both studies, a significantly higher than expected S. aureus and MRSA colonization rate was observed as compared to colonization rates described for the general population. However, these estimates were moderate in comparison to reported clinic-based MSM and IDU S. aureus colonization findings. This study validates substantial prevalence differences and biases that may exist with data collected from clinic-based MSM and IDU. The prevalence of MSSA and MRSA nasal colonization did not differ significantly with respect to HIV status among NHBS-MSM3/NHBS-IDU3 participants. Continued examination on the effects of S. aureus colonization and infection should be examined longitudinally to confirm additional community-based determinants in populations that are disproportionately affected.^