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. ^

Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^

The prevalence of periodontal disease in Mexican American vs European American elderly in three distinct socio-economic communities in San Antonio, Texas

Purpose of the Study: This study evaluated the prevalence of periodontal disease between Mexican American elderly and European American elderly residing in three socio-economically distinct neighborhoods in San Antonio, Texas. ^ Study Group: Subjects for the original protocol were participants of the Oral Health: San Antonio Longitudinal Study of Aging (OH: SALSA), which began with National Institutes of Health (NIH) funding in 1993 (M.J. Saunders, PI). The cohort in the study was the individuals who had been enrolled in Phases I and III of the San Antonio Heart Study (SAHS). This SAHS/SALSA sample is a community-based probability sample of Mexican American and European American residents from three socio-economically distinct San Antonio neighborhoods: low-income barrio, middle-income transitional, and upper-income suburban. The OH: SALSA cohort was established between July 1993 and May 1998 by sampling two subsets of the San Antonio Heart Study (SAHS) cohort. These subsets included the San Antonio Longitudinal Study of Aging (SALSA) cohort, comprised of the oldest members of the SAHS (age 65+ yrs. old), and a younger set of controls (age 35-64 yrs. old) sampled from the remainder of the SAHS cohort. ^ Methods: The study used simple descriptive statistics to describe the sociodemographic characteristics and periodontal disease indicators of the OH: SALSA participants. Means and standard deviations were used to summarize continuous measures. Proportions were used to summarize categorical measures. Simple m x n chi square statistics was used to compare ethnic differences. A multivariable ordered logit regression was used to estimate the prevalence of periodontal disease and test ethnic group and neighborhood differences in the prevalence of periodontal disease. A multivariable model adjustment for socio-economic status (income and education), gender, and age (treated as confounders) was applied. ^ Summary: In the unadjusted and adjusted model, Mexican American elderly demonstrated the greatest prevalence for periodontitis, p < 0.05. Mexican American elderly in barrio neighborhoods demonstrated the greatest prevalence for severe periodontitis, with unadjusted prevalence rates of 31.7%, 22.3%, and 22.4% for Mexican American elderly barrio, transitional, and suburban neighborhoods, respectively. Also, Mexican American elderly had adjusted prevalence rates of 29.4%, 23.7%, and 20.4% for barrio, transitional, and suburban neighborhoods, respectively. ^ Conclusion: This study indicates that the prevalence of periodontal disease is an important oral health issue among the Mexican American elderly. The results suggest that the socioeconomic status of the residential neighborhood increased the risk for severe periodontal disease among the Mexican American elderly when compared to European American elderly. A viable approach to recognizing oral health disparities in our growing population of Mexican American elderly is imperative for the provision of special care programs that will help increase the quality of care in this minority population.^