Performance of balanced two-stage empirical predictors of realized cluster latent values from finite populations: A simulation study

Predictors of random effects are usually based on the popular mixed effects (ME) model developed under the assumption that the sample is obtained from a conceptual infinite population; such predictors are employed even when the actual population is finite. Two alternatives that incorporate the finite nature of the population are obtained from the superpopulation model proposed by Scott and Smith (1969. Estimation in multi-stage surveys. J. Amer. Statist. Assoc. 64, 830-840) or from the finite population mixed model recently proposed by Stanek and Singer (2004. Predicting random effects from finite population clustered samples with response error. J. Amer. Statist. Assoc. 99, 1119-1130). Predictors derived under the latter model with the additional assumptions that all variance components are known and that within-cluster variances are equal have smaller mean squared error (MSE) than the competitors based on either the ME or Scott and Smith`s models. As population variances are rarely known, we propose method of moment estimators to obtain empirical predictors and conduct a simulation study to evaluate their performance. The results suggest that the finite population mixed model empirical predictor is more stable than its competitors since, in terms of MSE, it is either the best or the second best and when second best, its performance lies within acceptable limits. When both cluster and unit intra-class correlation coefficients are very high (e.g., 0.95 or more), the performance of the empirical predictors derived under the three models is similar. (c) 2007 Elsevier B.V. All rights reserved.

An Information Theory framework for two-stage binary image operator design

The design of translation invariant and locally defined binary image operators over large windows is made difficult by decreased statistical precision and increased training time. We present a complete framework for the application of stacked design, a recently proposed technique to create two-stage operators that circumvents that difficulty. We propose a novel algorithm, based on Information Theory, to find groups of pixels that should be used together to predict the Output Value. We employ this algorithm to automate the process of creating a set of first-level operators that are later combined in a global operator. We also propose a principled way to guide this combination, by using feature selection and model comparison. Experimental results Show that the proposed framework leads to better results than single stage design. (C) 2009 Elsevier B.V. All rights reserved.

Predicting random effects with an expanded finite population mixed model

Prediction of random effects is an important problem with expanding applications. In the simplest context, the problem corresponds to prediction of the latent value (the mean) of a realized cluster selected via two-stage sampling. Recently, Stanek and Singer [Predicting random effects from finite population clustered samples with response error. J. Amer. Statist. Assoc. 99, 119-130] developed best linear unbiased predictors (BLUP) under a finite population mixed model that outperform BLUPs from mixed models and superpopulation models. Their setup, however, does not allow for unequally sized clusters. To overcome this drawback, we consider an expanded finite population mixed model based on a larger set of random variables that span a higher dimensional space than those typically applied to such problems. We show that BLUPs for linear combinations of the realized cluster means derived under such a model have considerably smaller mean squared error (MSE) than those obtained from mixed models, superpopulation models, and finite population mixed models. We motivate our general approach by an example developed for two-stage cluster sampling and show that it faithfully captures the stochastic aspects of sampling in the problem. We also consider simulation studies to illustrate the increased accuracy of the BLUP obtained under the expanded finite population mixed model. (C) 2007 Elsevier B.V. All rights reserved.

Sampling, WLS, and Mixed Models

Mixed models may be defined with or without reference to sampling, and can be used to predict realized random effects, as when estimating the latent values of study subjects measured with response error. When the model is specified without reference to sampling, a simple mixed model includes two random variables, one stemming from an exchangeable distribution of latent values of study subjects and the other, from the study subjects` response error distributions. Positive probabilities are assigned to both potentially realizable responses and artificial responses that are not potentially realizable, resulting in artificial latent values. In contrast, finite population mixed models represent the two-stage process of sampling subjects and measuring their responses, where positive probabilities are only assigned to potentially realizable responses. A comparison of the estimators over the same potentially realizable responses indicates that the optimal linear mixed model estimator (the usual best linear unbiased predictor, BLUP) is often (but not always) more accurate than the comparable finite population mixed model estimator (the FPMM BLUP). We examine a simple example and provide the basis for a broader discussion of the role of conditioning, sampling, and model assumptions in developing inference.

Impact of chronic disease on quality of life among the elderly in the state of Sao Paulo, Brazil: a population-based study

Objectives. To assess the impact of chronic disease and the number of diseases on the various aspects of health-related quality of life (HRQOL) among the elderly in Sao Paulo, Brazil. Methods. The SF-36 (R) Health Survey was used to assess the impact of the most prevalent chronic diseases on HRQOL. A cross-sectional and population-based study was carried out with two-stage stratified cluster sampling. Data were obtained from a multicenter health survey administered through household interviews in several municipalities in the state of Sao Paulo. The study evaluated seven diseases-arthritis, back-pain, depression/anxiety, diabetes, hypertension, osteoporosis, and stroke-and their effects on quality of life. Results. Among the 1958 elderly individuals (60 years of age or older), 13.6% reported not having any of the illnesses, whereas 45.7% presented three or more chronic conditions. The presence of any of the seven chronic illnesses studied had a significant effect on the scores Of nearly all the SF-36 (R) scales. HRQOL achieved lower scores when related to depression/anxiety, osteoporosis, and stroke. The higher the number of diseases, the greater the negative effect on the SF-36 (R) dimensions. The presence of three or more diseases significantly affected HRQOL in all areas. The bodily pain, general health, and vitality scales were the most affected by diseases. Conclusions. The study detected a high prevalence of chronic diseases among the elderly population and found that the degree of impact on HRQOL depends on the type of disease. The results highlight the importance of preventing and controlling chronic diseases in order to reduce the number of comorbidities and lessen their impact on HRQOL among the elderly.

Osteoconductivity of modified fluorcanasite glass-ceramics for bone tissue augmentation and repair

Modified fluorcanasite glasses were fabricated by either altering the molar ratios of Na(2)O and CaO or by adding P(2)O(5) to the parent stoichiometric glass compositions. Glasses were converted to glass-ceramics by a controlled two-stage heat treatment process. Rods (2 mm x 4 mm) were produced using the conventional lost-wax casting technique. Osteoconductive 45S5 bioglass was used as a reference material. Biocompatibility and osteoconductivity were investigated by implantation into healing defects (2 mm) in the midshaft of rabbit femora. Tissue response was investigated using conventional histology and scanning electron microscopy. Histological and histomorphometric evaluation of specimens after 12 weeks implantation showed significantly more bone contact with the surface of 45S5 bioglass implants when compared with other test materials. When the bone contact for each material was compared between experimental time points, the Glass-Ceramic 2 (CaO rich) group showed significant difference (p = 0.027) at 4 weeks, but no direct contact at 12 weeks. Histology and backscattered electron photomicrographs showed that modified fluorcanasite glass-ceramic implants had greater osteoconductivity than the parent stoichiometric composition. Of the new materials, fluorcanasite glass-ceramic implants modified by the addition of P(2)O(5) showed the greatest stimulation of new mineralized bone tissue formation adjacent to the implants after 4 and 12 weeks implantation. (C) 2010 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 94A: 760-768, 2010

Histological Outcomes on the Development of New Space-making Devices for Maxillary Sinus Floor Augmentation

Background: Previous studies have pointed out that the mere elevation of the maxillary sinus membrane promotes bone formation without the use of augmentation materials. Purpose: This experimental study aimed at evaluating if the two-stage procedure for sinus floor augmentation could benefit from the use of a space-making device in order to increase the bone volume to enable later implant installation with good primary stability. Materials and Methods: Six male tufted capuchin primates (Cebus apella) were subjected to extraction of the three premolars and the first molar on both sides of the maxilla to create an edentulous area. The sinuses were opened using the lateral bone-wall window technique, and the membrane was elevated. One resorbable space-making device was inserted in each maxillary sinus, and the bone window was returned in place. The animals were euthanatized after 6 months, and biopsy blocks containing the whole maxillary sinus and surrounding soft tissues were prepared for ground sections. Results: The histological examination of the specimens showed bone formation in contact with both the schneiderian membrane and the device in most cases even when the device was displaced. The process of bone formation indicates that this technique is potentially useful for two-stage sinus floor augmentation. The lack of stabilization of the device within the sinus demands further improvement of space-makers for predictable bone augmentation. Conclusions: It is concluded that (1) the device used in this study did not trigger any important inflammatory reaction; (2) when the sinus membrane was elevated, bone formation was a constant finding; and (3) an ideal space-making device should be stable and elevate the membrane to ensure a maintained connection between the membrane and the secluded space.

Production of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) P(3HB-co-3HV) with a broad range of 3HV content at high yields by Burkholderia sacchari IPT 189

The biosynthesis of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) from sucrose and propionic acid by Burkholderia sacchari IPT 189 was studied using a two-stage bioreactor process. In the first stage, this bacterium was cultivated in a balanced culture medium until sucrose exhaustion. In the second stage, a solution containing sucrose and propionic acid as carbon source was fed to the bioreactor at various sucrose/propionic acid (s/p) ratios at a constant specific flow rate. Copolymers with 3HV content ranging from 40 down to 6.5 (mol%) were obtained with 3HV yield from propionic acid (Y-3HV/prop) increasing from 1.10 to 1.34 g g(-1). Copolymer productivity of 1 g l(-1) h(-1) was obtained with polymer biomass content rising up to 60% by increasing a specific flow rate at a constant s/p ratio. Increasing values of 3HV content were obtained by varying the s/p ratios. A simulation of production costs considering Y-3HV/prop obtained in the present work indicated that a reduction of up to 73% can be reached, approximating US$ 1.00 per kg which is closer to the value to produce P3HB from sucrose (US$ 0.75 per kg).

On the cutting stock problem under stochastic demand

This paper addresses the one-dimensional cutting stock problem when demand is a random variable. The problem is formulated as a two-stage stochastic nonlinear program with recourse. The first stage decision variables are the number of objects to be cut according to a cutting pattern. The second stage decision variables are the number of holding or backordering items due to the decisions made in the first stage. The problem`s objective is to minimize the total expected cost incurred in both stages, due to waste and holding or backordering penalties. A Simplex-based method with column generation is proposed for solving a linear relaxation of the resulting optimization problem. The proposed method is evaluated by using two well-known measures of uncertainty effects in stochastic programming: the value of stochastic solution-VSS-and the expected value of perfect information-EVPI. The optimal two-stage solution is shown to be more effective than the alternative wait-and-see and expected value approaches, even under small variations in the parameters of the problem.

Comparing diagnostic tests with missing data

When missing data occur in studies designed to compare the accuracy of diagnostic tests, a common, though naive, practice is to base the comparison of sensitivity, specificity, as well as of positive and negative predictive values on some subset of the data that fits into methods implemented in standard statistical packages. Such methods are usually valid only under the strong missing completely at random (MCAR) assumption and may generate biased and less precise estimates. We review some models that use the dependence structure of the completely observed cases to incorporate the information of the partially categorized observations into the analysis and show how they may be fitted via a two-stage hybrid process involving maximum likelihood in the first stage and weighted least squares in the second. We indicate how computational subroutines written in R may be used to fit the proposed models and illustrate the different analysis strategies with observational data collected to compare the accuracy of three distinct non-invasive diagnostic methods for endometriosis. The results indicate that even when the MCAR assumption is plausible, the naive partial analyses should be avoided.