Simple Estimation of Incident HIV Infection Rates in Notification Cohorts Based on Window Periods of Algorithms for Evaluation of Line-Immunoassay Result Patterns

Background Tests for recent infections (TRIs) are important for HIV surveillance. We have shown that a patient's antibody pattern in a confirmatory line immunoassay (Inno-Lia) also yields information on time since infection. We have published algorithms which, with a certain sensitivity and specificity, distinguish between incident (< = 12 months) and older infection. In order to use these algorithms like other TRIs, i.e., based on their windows, we now determined their window periods. Methods We classified Inno-Lia results of 527 treatment-naïve patients with HIV-1 infection < = 12 months according to incidence by 25 algorithms. The time after which all infections were ruled older, i.e. the algorithm's window, was determined by linear regression of the proportion ruled incident in dependence of time since infection. Window-based incident infection rates (IIR) were determined utilizing the relationship ‘Prevalence = Incidence x Duration’ in four annual cohorts of HIV-1 notifications. Results were compared to performance-based IIR also derived from Inno-Lia results, but utilizing the relationship ‘incident = true incident + false incident’ and also to the IIR derived from the BED incidence assay. Results Window periods varied between 45.8 and 130.1 days and correlated well with the algorithms' diagnostic sensitivity (R2 = 0.962; P<0.0001). Among the 25 algorithms, the mean window-based IIR among the 748 notifications of 2005/06 was 0.457 compared to 0.453 obtained for performance-based IIR with a model not correcting for selection bias. Evaluation of BED results using a window of 153 days yielded an IIR of 0.669. Window-based IIR and performance-based IIR increased by 22.4% and respectively 30.6% in 2008, while 2009 and 2010 showed a return to baseline for both methods. Conclusions IIR estimations by window- and performance-based evaluations of Inno-Lia algorithm results were similar and can be used together to assess IIR changes between annual HIV notification cohorts.

Statnote 16: fitting a regression line to data

Fitting a linear regression to data provides much more information about the relationship between two variables than a simple correlation test. A goodness of fit test of the line should always be carried out. Hence, ‘r squared’ estimates the strength of the relationship between Y and X, ANOVA whether a statistically significant line is present, and the ‘t’ test whether the slope of the line is significantly different from zero. In addition, it is important to check whether the data fit the assumptions for regression analysis and, if not, whether a transformation of the Y and/or X variables is necessary.

Data methods in optometry. Part 6: fitting a regression line to data

1. Fitting a linear regression to data provides much more information about the relationship between two variables than a simple correlation test. A goodness of fit test of the line should always be carried out. Hence, r squared estimates the strength of the relationship between Y and X, ANOVA whether a statistically significant line is present, and the ‘t’ test whether the slope of the line is significantly different from zero. 2. Always check whether the data collected fit the assumptions for regression analysis and, if not, whether a transformation of the Y and/or X variables is necessary. 3. If the regression line is to be used for prediction, it is important to determine whether the prediction involves an individual y value or a mean. Care should be taken if predictions are made close to the extremities of the data and are subject to considerable error if x falls beyond the range of the data. Multiple predictions require correction of the P values. 3. If several individual regression lines have been calculated from a number of similar sets of data, consider whether they should be combined to form a single regression line. 4. If the data exhibit a degree of curvature, then fitting a higher-order polynomial curve may provide a better fit than a straight line. In this case, a test of whether the data depart significantly from a linear regression should be carried out.

RELIBILITY ANALYSIS OF SIMPLE SLOPES AND SOIL-STRUCTURES WITH LINEAR LIMIT STATES

The first objective of this research was to develop closed-form and numerical probabilistic methods of analysis that can be applied to otherwise conventional methods of unreinforced and geosynthetic reinforced slopes and walls. These probabilistic methods explicitly include random variability of soil and reinforcement, spatial variability of the soil, and cross-correlation between soil input parameters on probability of failure. The quantitative impact of simultaneously considering the influence of random and/or spatial variability in soil properties in combination with cross-correlation in soil properties is investigated for the first time in the research literature. Depending on the magnitude of these statistical descriptors, margins of safety based on conventional notions of safety may be very different from margins of safety expressed in terms of probability of failure (or reliability index). The thesis work also shows that intuitive notions of margin of safety using conventional factor of safety and probability of failure can be brought into alignment when cross-correlation between soil properties is considered in a rigorous manner. The second objective of this thesis work was to develop a general closed-form solution to compute the true probability of failure (or reliability index) of a simple linear limit state function with one load term and one resistance term expressed first in general probabilistic terms and then migrated to a LRFD format for the purpose of LRFD calibration. The formulation considers contributions to probability of failure due to model type, uncertainty in bias values, bias dependencies, uncertainty in estimates of nominal values for correlated and uncorrelated load and resistance terms, and average margin of safety expressed as the operational factor of safety (OFS). Bias is defined as the ratio of measured to predicted value. Parametric analyses were carried out to show that ignoring possible correlations between random variables can lead to conservative (safe) values of resistance factor in some cases and in other cases to non-conservative (unsafe) values. Example LRFD calibrations were carried out using different load and resistance models for the pullout internal stability limit state of steel strip and geosynthetic reinforced soil walls together with matching bias data reported in the literature.

Modelos de regressão lineares para estimativa de produtividade da soja no oeste do Paraná, utilizando dados espectrais

The main objective of this work was to evaluate the linear regression between spectral response and soybean yield in regional scale. In this study were monitored 36 municipalities from the west region of the states of Parana using five images of Landsat 5/TM during 2004/05 season. The spectral response was converted in physical values, apparent and surface reflectances, by radiometric transformation and atmospheric corrections and both used to calculate NDVI and GVI vegetation indices. Those ones were compared by multiple and simple regression with government official yield values (IBGE). Diagnostic processing method to identify influents values or collinearity was applied to the data too. The results showed that the mean surface reflectance value from all images was more correlated with yield than individual dates. Further, the multiple regressions using all dates and both vegetation indices gave better results than simple regression.

Fatores associados ao consumo de frutas, legumes e verduras em adultos da cidade de São Paulo

OBJETIVO: Descrever a freqüência de consumo de frutas, legumes e verduras por adultos e analisar os fatores associados ao seu consumo. MÉTODOS: Estudo transversal realizado entre outubro e dezembro de 2003 no município de São Paulo (SP). Foram realizadas entrevistas telefônicas em amostra probabilística da população adulta (>18 anos) residente em domicílios servidos por linhas fixas de telefone, totalizando 1.267 mulheres e 855 homens. A freqüência do consumo de frutas, legumes e verduras foi medida por meio de um roteiro com perguntas curtas e simples. Na avaliação dos fatores associados ao consumo, realizou-se análise de regressão linear multivariada e hierarquizada, com variáveis sociodemográficas no primeiro nível hierárquico, comportamentais no segundo e relacionadas ao padrão alimentar no terceiro nível. RESULTADOS: A freqüência de consumo de frutas, legumes e verduras foi maior entre as mulheres. Para ambos os sexos, verificou-se que a freqüência desse consumo aumentava de acordo com a idade e a escolaridade do indivíduo. Entre mulheres que relataram ter realizado dieta no ano anterior houve maior consumo de frutas, legumes e verduras. O consumo de alimentos que indicam um padrão de consumo não saudável como açúcares e gorduras se mostrou inversamente associado ao consumo de frutas, legumes e verduras em ambos os sexos. CONCLUSÕES: O consumo de frutas, legumes e verduras da população adulta residente em São Paulo foi maior entre as mulheres, sendo influenciado pela idade, escolaridade e dieta

Residuals for log-Burr XII regression models in survival analysis

In this paper, we compare three residuals to assess departures from the error assumptions as well as to detect outlying observations in log-Burr XII regression models with censored observations. These residuals can also be used for the log-logistic regression model, which is a special case of the log-Burr XII regression model. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and the empirical distribution of each residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to the modified martingale-type residual in log-Burr XII regression models with censored data.

Log-modified Weibull regression models with censored data: Sensitivity and residual analysis

This paper proposes a regression model considering the modified Weibull distribution. This distribution can be used to model bathtub-shaped failure rate functions. Assuming censored data, we consider maximum likelihood and Jackknife estimators for the parameters of the model. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and we also present some ways to perform global influence. Besides, for different parameter settings, sample sizes and censoring percentages, various simulations are performed and the empirical distribution of the modified deviance residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended for a martingale-type residual in log-modified Weibull regression models with censored data. Finally, we analyze a real data set under log-modified Weibull regression models. A diagnostic analysis and a model checking based on the modified deviance residual are performed to select appropriate models. (c) 2008 Elsevier B.V. All rights reserved.

Likelihood-based estimation of the regression model with scrambled responses

A significant problem in the collection of responses to potentially sensitive questions, such as relating to illegal, immoral or embarrassing activities, is non-sampling error due to refusal to respond or false responses. Eichhorn & Hayre (1983) suggested the use of scrambled responses to reduce this form of bias. This paper considers a linear regression model in which the dependent variable is unobserved but for which the sum or product with a scrambling random variable of known distribution, is known. The performance of two likelihood-based estimators is investigated, namely of a Bayesian estimator achieved through a Markov chain Monte Carlo (MCMC) sampling scheme, and a classical maximum-likelihood estimator. These two estimators and an estimator suggested by Singh, Joarder & King (1996) are compared. Monte Carlo results show that the Bayesian estimator outperforms the classical estimators in almost all cases, and the relative performance of the Bayesian estimator improves as the responses become more scrambled.

Birth weight and bone mass in young adults from Brazil

Background: Birth weight is positively associated with adult bone mass. However, it is not clear if its effect is already evident in early adulthood. Objective: To investigate the association between birth weight, adult body size, the interaction between them and bone mass in young adults. Methods: Bone densitometry by DXA was performed on 496 individuals (240 men) aged 23-24 years from the 1978/79 Ribeirao Preto (southern Brazil) birth cohort, who were born and still residing in the city in 2002. Birth weight and length as well as adult weight and height were directly measured and converted to z-scores. The influence of birth weight and length, and adult weight and height on bone area (BA), bone mineral content (BMC) and bone mineral density (BMD) at the lumbar spine, proximal femur and femoral neck were investigated through simple and multiple linear regression models. Adjustments were made for sex, skin color, gestational age, physical activity level, smoking status and dietary consumption of protein, calcium and alcohol. Interaction terms between birth weight and adult weight, and birth length and adult height were tested. Results: Men in the highest fertile of birth weight distribution had greater BA and BMC at all three bone sites when compared with their counterparts in the lowest tertiles (p<0.008). For BMD, this trend was observed only in the lumbar spine. Adult weight and height were positively associated with BA and BMC at all three bone sites (p<0.05). For BMD, these associations were seen for adult weight, but for adult height an association was observed only in the lumbar spine. Birth weight retained positive associations with proximal femur BA and BMC after adjustments for current weight and height. No interaction was observed between variables measuring prenatal growth and adult body size. Conclusion: Birth weight and postnatal growth are independent determinants of adult bone mass in a sample of Brazilian adults. This effect is already evident in early adulthood. (C) 2010 Elsevier Inc. All rights reserved.

Working memory correlates of three symptom clusters in schizophrenia

This study was designed to examine whether discrete working memory deficits underlie positive, negative and disorganised symptoms of schizophrenia. Symptom dimension ratings were assigned to 52 outpatients with schizophrenia (ICD-10 criteria), using items drawn from the Positive and Negative Syndrome Scale (PANSS). Linear regression and correlational analyses were conducted to examine whether symptom dimension scores were related to performance on several tests of working memory function. Severity of negative symptoms correlated with reduced production of words during a verbal fluency task, impaired ability to hold letter and number sequences on-line and manipulate them Simultaneously, reduced performance during a dual task, and compromised visuospatial working memory under distraction-free conditions. Severity of disorganisation symptoms correlated with impaired visuospatial working memory under conditions of distraction, failure of inhibition during a verbal fluency task, perseverative responding on a test of set-shifting ability, and impaired ability to judge the veracity of simple declarative statements. Severity of positive symptoms was uncorrelated with performance on any of the measures examined. The present study provides evidence that the positive, negative and disorganised symptom dimensions of the PANSS constitute independent clusters, associated with unique patterns of working memory impairment. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.

Análise de Resultados de Simulações não Estacionárias Aperiódicas e Cíclicas

Tese de Doutoramento, Matemática (Investigação Operacional), 23 de Setembro de 2006, Universidade dos Açores.

Avaliação de fatores determinantes e da taxa de incidência da condição DFD (Dark, Firm, Dry) na carne de bovinos abatidos no matadouro da ilha Terceira

Dissertação de Mestrado, Engenharia Zootécnica, 13 de Junho de 2014, Universidade dos Açores.

Factors associated to quality of life in active elderly

OBJECTIVE: To analyze whether quality of life in active, healthy elderly individuals is influenced by functional status and sociodemographic characteristics, as well as psychological parameters. METHODS: Study conducted in a sample of 120 active elderly subjects recruited from two open universities of the third age in the cities of São Paulo and São José dos Campos (Southeastern Brazil) between May 2005 and April 2006. Quality of life was measured using the abbreviated Brazilian version of the World Health Organization Quality of Live (WHOQOL-bref) questionnaire. Sociodemographic, clinical and functional variables were measured through crossculturally validated assessments by the Mini Mental State Examination, Geriatric Depression Scale, Functional Reach, One-Leg Balance Test, Timed Up and Go Test, Six-Minute Walk Test, Human Activity Profile and a complementary questionnaire. Simple descriptive analyses, Pearson's correlation coefficient, Student's t-test for non-related samples, analyses of variance, linear regression analyses and variance inflation factor were performed. The significance level for all statistical tests was set at 0.05. RESULTS: Linear regression analysis showed an independent correlation without colinearity between depressive symptoms measured by the Geriatric Depression Scale and four domains of the WHOQOL-bref. Not having a conjugal life implied greater perception in the social domain; developing leisure activities and having an income over five minimum wages implied greater perception in the environment domain. CONCLUSIONS: Functional status had no influence on the Quality of Life variable in the analysis models in active elderly. In contrast, psychological factors, as assessed by the Geriatric Depression Scale, and sociodemographic characteristics, such as marital status, income and leisure activities, had an impact on quality of life.

Multiple linear and principal component regressions for modelling ecotoxicity bioassay response

The ecotoxicological response of the living organisms in an aquatic system depends on the physical, chemical and bacteriological variables, as well as the interactions between them. An important challenge to scientists is to understand the interaction and behaviour of factors involved in a multidimensional process such as the ecotoxicological response.With this aim, multiple linear regression (MLR) and principal component regression were applied to the ecotoxicity bioassay response of Chlorella vulgaris and Vibrio fischeri in water collected at seven sites of Leça river during five monitoring campaigns (February, May, June, August and September of 2006). The river water characterization included the analysis of 22 physicochemical and 3 microbiological parameters. The model that best fitted the data was MLR, which shows: (i) a negative correlation with dissolved organic carbon, zinc and manganese, and a positive one with turbidity and arsenic, regarding C. vulgaris toxic response; (ii) a negative correlation with conductivity and turbidity and a positive one with phosphorus, hardness, iron, mercury, arsenic and faecal coliforms, concerning V. fischeri toxic response. This integrated assessment may allow the evaluation of the effect of future pollution abatement measures over the water quality of Leça River.