The generalized log-gamma mixture model with covariates: local influence and residual analysis

In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death, failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data. The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction. Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate model.

Norwegian long-distance reflexives: Lectal variation and speaker attitudes

10 lectal variables were examined with respect to Norwegian speakers' acceptance of long-distance reflexives (LDR), using a questionnaire to elicit grammaticality judgements on 50 potential LDR sentences. A sample of 180 speakers completed the questionnaire. The data was analysed using a general linear model univariate model, and Spearman's correlation. In this sample the results showed that dialect and level of education had significant effects on speakers' acceptance of long-distance reflexives, while sex, age, being a native speaker, having both native-speaker parents, living in the city or the country, and the speaker's attitudes to the two Norwegian writing languages had no influence on speakers' acceptance of long-distance reflexives. It is suggested that the influence of Danish on Norwegian writing and on the southern dialects may be the cause of the observed variation with respect to LDR in Norwegian.

The effects of Australian tall poppy attitudes on American value based leadership theory

A survey study of twenty-two Australian CEOs and their subordinates assessed relationships between Australian leader motives, Australian value based leader behaviour, subordinate tall poppy attitudes and subordinate commitment, effectiveness, motivation and satisfaction (CEMS). On the whole, the results showed general support for value based leadership processes. Subsequent regression analyses of the second main component of Value Based Leadership Theory, value based leader behaviour, revealed that the collectivistic, inspirational, integrity and visionary behaviour sub-scales of the construct were positively related with subordinate CEMS. Although the hypothesis that subordinate tall poppy attitudes would moderate value based leadership processes was not clearly supported, subsequent regression analyses found that subordinate tall poppy attitudes were negatively related with perceptions of value based leader behaviour and CEMS. These findings suggest complex relationships between the three constructs, and the proposed model for the Australian context is accordingly amended. Overall, the research supports the need to consider cultural-specific attitudes in management development.

On modifications to the long-term survival mixture model in the presence of competing risks

A mixture model for long-term survivors has been adopted in various fields such as biostatistics and criminology where some individuals may never experience the type of failure under study. It is directly applicable in situations where the only information available from follow-up on individuals who will never experience this type of failure is in the form of censored observations. In this paper, we consider a modification to the model so that it still applies in the case where during the follow-up period it becomes known that an individual will never experience failure from the cause of interest. Unless a model allows for this additional information, a consistent survival analysis will not be obtained. A partial maximum likelihood (ML) approach is proposed that preserves the simplicity of the long-term survival mixture model and provides consistent estimators of the quantities of interest. Some simulation experiments are performed to assess the efficiency of the partial ML approach relative to the full ML approach for survival in the presence of competing risks.

A model of specification-based testing of interactive systems

In this paper we present a model of specification-based testing of interactive systems. This model provides the basis for a framework to guide such testing. Interactive systems are traditionally decomposed into a functionality component and a user interface component; this distinction is termed dialogue separation and is the underlying basis for conceptual and architectural models of such systems. Correctness involves both proper behaviour of the user interface and proper computation by the underlying functionality. Specification-based testing is one method used to increase confidence in correctness, but it has had limited application to interactive system development to date.

Estimating HIV incidence rates from age prevalence data in epidemic situations

We present a method of estimating HIV incidence rates in epidemic situations from data on age-specific prevalence and changes in the overall prevalence over time. The method is applied to women attending antenatal clinics in Hlabisa, a rural district of KwaZulu/Natal, South Africa, where transmission of HIV is overwhelmingly through heterosexual contact. A model which gives age-specific prevalence rates in the presence of a progressing epidemic is fitted to prevalence data for 1998 using maximum likelihood methods and used to derive the age-specific incidence. Error estimates are obtained using a Monte Carlo procedure. Although the method is quite general some simplifying assumptions are made concerning the form of the risk function and sensitivity analyses are performed to explore the importance of these assumptions. The analysis shows that in 1998 the annual incidence of infection per susceptible woman increased from 5.4 per cent (3.3-8.5 per cent; here and elsewhere ranges give 95 per cent confidence limits) at age 15 years to 24.5 per cent (20.6-29.1 per cent) at age 22 years and declined to 1.3 per cent (0.5-2.9 per cent) at age 50 years; standardized to a uniform age distribution, the overall incidence per susceptible woman aged 15 to 59 was 11.4 per cent (10.0-13.1 per cent); per women in the population it was 8.4 per cent (7.3-9.5 per cent). Standardized to the age distribution of the female population the average incidence per woman was 9.6 per cent (8.4-11.0 per cent); standardized to the age distribution of women attending antenatal clinics, it was 11.3 per cent (9.8-13.3 per cent). The estimated incidence depends on the values used for the epidemic growth rate and the AIDS related mortality. To ensure that, for this population, errors in these two parameters change the age specific estimates of the annual incidence by less than the standard deviation of the estimates of the age specific incidence, the AIDS related mortality should be known to within +/-50 per cent and the epidemic growth rate to within +/-25 per cent, both of which conditions are met. In the absence of cohort studies to measure the incidence of HIV infection directly, useful estimates of the age-specific incidence can be obtained from cross-sectional, age-specific prevalence data and repeat cross-sectional data on the overall prevalence of HIV infection. Several assumptions were made because of the lack of data but sensitivity analyses show that they are unlikely to affect the overall estimates significantly. These estimates are important in assessing the magnitude of the public health problem, for designing vaccine trials and for evaluating the impact of interventions. Copyright (C) 2001 John Wiley & Sons, Ltd.

A model of seller holdout

We model a buyer who wishes to combine objects owned by two separate sellers in order to realize higher value. Sellers are able to avoid entering into negotiations with the buyer, so that the order in which they negotiate is endogenous. Holdout occurs if at least one of the sellers is not present in the first round of negotiations. We demonstrate that complementarity of the buyer's technology is a necessary condition for equilibrium holdout. Moreover, a rise in complementarity leads to an increased likelihood of holdout, and an increased efficiency loss. Applications include patents, the land assembly problem, and mergers.

Relation of Aortic Valve Calcium Detected by Cardiac Computed Tomography to All-Cause Mortality

Aortic valve calcium (AVC) can be quantified on the same computed tomographic scan as coronary artery calcium (CAC). Although CAC is an established predictor of cardiovascular events, limited evidence is available for an independent predictive value for AVC. We studied a cohort of 8,401 asymptomatic subjects (mean age 53 10 years, 69% men), who were free of known coronary heart disease and were undergoing electron beam computed tomography for assessment of subclinical atherosclerosis. The patients were followed for a median of 5 years (range 1 to 7) for the occurrence of mortality from any cause. Multivariate Cox regression models were developed to predict all-cause mortality according to the presence of AVC. A total of 517 patients (6%) had AVC on electron beam computed tomography. During follow-up, 124 patients died (1.5%), for an overall survival rate of 96.1% and 98.7% for those with and without AVC, respectively (hazard ratio 3.39, 95% confidence interval 2.09 to 5.49). After adjustment for age, gender, hypertension, dyslipidemia, diabetes mellitus, smoking, and a family history of premature coronary heart disease, AVC remained a significant predictor of mortality (hazard ratio 1.82, 95% confidence interval 1.11 to 2.98). Likelihood ratio chi-square statistics demonstrated that the addition of AVC contributed significantly to the prediction of mortality in a model adjusted for traditional risk factors (chi-square = 5.03, p = 0.03) as well as traditional risk factors plus the presence of CAC (chi-square = 3.58, p = 0.05). In conclusion, AVC was associated with increased all-cause mortality, independent of the traditional risk factors and the presence of CAC. (C) 2010 Published by Elsevier Inc. (Am J Cardiol 2010;106:1787-1791)

Strategies for genetic model specification in the screening of genome-wide meta-analysis signals for further replication

Background Meta-analysis is increasingly being employed as a screening procedure in large-scale association studies to select promising variants for follow-up studies. However, standard methods for meta-analysis require the assumption of an underlying genetic model, which is typically unknown a priori. This drawback can introduce model misspecifications, causing power to be suboptimal, or the evaluation of multiple genetic models, which augments the number of false-positive associations, ultimately leading to waste of resources with fruitless replication studies. We used simulated meta-analyses of large genetic association studies to investigate naive strategies of genetic model specification to optimize screenings of genome-wide meta-analysis signals for further replication. Methods Different methods, meta-analytical models and strategies were compared in terms of power and type-I error. Simulations were carried out for a binary trait in a wide range of true genetic models, genome-wide thresholds, minor allele frequencies (MAFs), odds ratios and between-study heterogeneity (tau(2)). Results Among the investigated strategies, a simple Bonferroni-corrected approach that fits both multiplicative and recessive models was found to be optimal in most examined scenarios, reducing the likelihood of false discoveries and enhancing power in scenarios with small MAFs either in the presence or in absence of heterogeneity. Nonetheless, this strategy is sensitive to tau(2) whenever the susceptibility allele is common (MAF epsilon 30%), resulting in an increased number of false-positive associations compared with an analysis that considers only the multiplicative model. Conclusion Invoking a simple Bonferroni adjustment and testing for both multiplicative and recessive models is fast and an optimal strategy in large meta-analysis-based screenings. However, care must be taken when examined variants are common, where specification of a multiplicative model alone may be preferable.

Derivation and external validation of a simple prediction model for the diagnosis of type 2 Diabetes Mellitus in the Brazilian urban population

A risk score model was developed based in a population of 1,224 individuals from the general population without known diabetes aging 35 years or more from an urban Brazilian population sample in order to select individuals who should be screened in subsequent testing and improve the efficacy of public health assurance. External validation was performed in a second, independent, population from a different city ascertained through a similar epidemiological protocol. The risk score was developed by multiple logistic regression and model performance and cutoff values were derived from a receiver operating characteristic curve. Model`s capacity of predicting fasting blood glucose levels was tested analyzing data from a 5-year follow-up protocol conducted in the general population. Items independently and significantly associated with diabetes were age, BMI and known hypertension. Sensitivity, specificity and proportion of further testing necessary for the best cutoff value were 75.9, 66.9 and 37.2%, respectively. External validation confirmed the model`s adequacy (AUC equal to 0.72). Finally, model score was also capable of predicting fasting blood glucose progression in non-diabetic individuals in a 5-year follow-up period. In conclusion, this simple diabetes risk score was able to identify individuals with an increased likelihood of having diabetes and it can be used to stratify subpopulations in which performing of subsequent tests is necessary and probably cost-effective.

Self-Esteem, Confidence, and Relationships in Brazilian Men with Erectile Dysfunction Receiving Sildenafil Citrate: A Randomized, Parallel-Group, Double-Blind, Placebo-Controlled Study in Brazil

Psychosocial manifestations of erectile dysfunction (ED) differ across cultures. Understanding the treatment response to ED medications within cultural groups can aid in resource allocation and in developing treatment strategies. Evaluate the effect of sildenafil treatment on self-esteem, confidence, and sexual relationship satisfaction in Brazilian men with ED. The Self-Esteem and Relationship (SEAR) questionnaire, a validated, 14-question instrument developed to specifically address self-esteem and relationship issues within the context of ED. Men aged 18 years or older with a clinical diagnosis of ED (<= 21 on the Sexual Health Inventory for Men) and in a stable relationship with a partner during the study were eligible. The primary end point was a change from baseline in the self-esteem subscale of the SEAR questionnaire. Thirteen Brazilian sites participated in a randomized, double-blind, placebo-controlled trial of sildenafil treatment for ED. Patients were randomized to receive either 50 mg of sildenafil (adjustable to 25 mg or 100 mg based on patient response) or matching placebo approximately 1 hour before anticipated sexual activity but not more than once a day. At the end of double-blind treatment, 63 and 66 patients in the placebo and sildenafil groups, respectively, from 13 Brazilian sites were assessed for efficacy. Brazilian patients receiving sildenafil had significantly greater improvements in their scores on the SEAR self-esteem subscale (42.9 [95% confidence interval 35.7-50.0]) compared with placebo (21.1 [95% confidence interval 13.7-28.6]; P < 0.0001). Effect sizes ranged from 0.91 to 1.25 for individual SEAR components. The psychosocial parameters in Brazilian men with ED assessed by the SEAR questionnaire showed significant improvements in self-esteem, confidence, and relationships after treatment with sildenafil. Glina S, Damiao R, Abdo C, Afif-Abdo J, Tseng L-J, and Stecher V. Self-esteem, confidence, and relationships in Brazilian men with erectile dysfunction receiving sildenafil citrate: A randomized, parallel-group, double-blind, placebo-controlled study in Brazil. J Sex Med 2009;6:268-275.

Histologic Study of Acute Vocal Fold Wound Healing After Corticosteroid Injection in a Rabbit Model

Objectives: Injectable corticosteroids have been used in phonosurgery to prevent scarring of the vocal fold because of their effects of wound healing, and to ensure better voice quality. We histologically evaluated the effects of dexamethasone sodium phosphate infiltration on acute vocal fold wound healing in rabbits 3 and 7 days after surgically induced injury by quantification of the inflammatory reaction and collagen deposition. Methods: A standardized surgical incision was made in the vocal folds of 12 rabbits, and 0.1 mL dexamethasone sodium phosphate (4 mg/mL) was injected into the left vocal fold. The right vocal fold was not injected and served as the control. The larynges were collected 3 and 7 days after surgery. For histologic analysis, the vocal folds were stained with hematoxylin-eosin for quantification of the inflammatory response and with picrosirius red for qunatification of collagen depostion. Results: There was no quantitative difference in the inflammatory response between vocal folds injected with the corticosteroid and control vocal folds. However, the rate of collage deposition was significantly lower in the corticosteroid-treated group at 3 and 7 days after injury (p = 0.002). Conclusions: The present results suggest that dexamethasone reduces collagen depostion during acute vocal fold wound healing.

Depersonalization and personality in panic disorder

Background: Prevalence and clinical correlates of depersonalization symptoms have been associated with panic disorder. Personality traits might increase the likelihood of experiencing depersonalization symptoms or depersonalization disorder in panic patients. Aims: The objectives of this study are to establish the prevalence of depersonalization symptoms during the panic attack and in depersonalization disorder and to examine the personality factors associated with the presence of depersonalization in patients with panic disorder. Methods: The sample comprised 104 consecutive adult outpatients with panic disorder, diagnosed according to the Semistructured Clinical Interview for DSM-IV (Axis I/II disorders). Participants were assessed with the Cambridge Depersonalization Scales, the Temperament and Character Inventory, and the Panic and Agoraphobia Scale. Results: Forty-eight percent of the sample had depersonalization symptoms during the panic attack, whereas 20% of patients had a depersonalization disorder. Women presented more depersonalization disorders than did men (P = .036). Patients with panic disorder with depersonalization disorder had a more severe panic disorder (P = .002). Logistic regression analysis showed that self-transcendence trait (odds ratio, 1.089; 95% confidence interval, 1.021-1.162; P = .010) and severity of panic (odds ratio, 1.056; 95% confidence interval, 1.005-1.110; P = .032) were independently associated with depersonalization disorder. Conclusions: A high prevalence of depersonalization symptoms and depersonalization disorder was confirmed in patients with panic disorder, supporting a dosage effect model for understanding depersonalization pathology. Self-transcendence trait and severity of panic disorder were reported as risk factors for depersonalization disorder. (C) 2011 Elsevier Inc. All rights reserved.

A Comparison of Bayesian and Sampling Theory Inferences in a Probit Model

HE PROBIT MODEL IS A POPULAR DEVICE for explaining binary choice decisions in econometrics. It has been used to describe choices such as labor force participation, travel mode, home ownership, and type of education. These and many more examples can be found in papers by Amemiya (1981) and Maddala (1983). Given the contribution of economics towards explaining such choices, and given the nature of data that are collected, prior information on the relationship between a choice probability and several explanatory variables frequently exists. Bayesian inference is a convenient vehicle for including such prior information. Given the increasing popularity of Bayesian inference it is useful to ask whether inferences from a probit model are sensitive to a choice between Bayesian and sampling theory techniques. Of interest is the sensitivity of inference on coefficients, probabilities, and elasticities. We consider these issues in a model designed to explain choice between fixed and variable interest rate mortgages. Two Bayesian priors are employed: a uniform prior on the coefficients, designed to be noninformative for the coefficients, and an inequality restricted prior on the signs of the coefficients. We often know, a priori, whether increasing the value of a particular explanatory variable will have a positive or negative effect on a choice probability. This knowledge can be captured by using a prior probability density function (pdf) that is truncated to be positive or negative. Thus, three sets of results are compared:those from maximum likelihood (ML) estimation, those from Bayesian estimation with an unrestricted uniform prior on the coefficients, and those from Bayesian estimation with a uniform prior truncated to accommodate inequality restrictions on the coefficients.

Long-term survivor mixture model with random effects: application to a multi-centre clinical trial of carcinoma

A mixture model incorporating long-term survivors has been adopted in the field of biostatistics where some individuals may never experience the failure event under study. The surviving fractions may be considered as cured. In most applications, the survival times are assumed to be independent. However, when the survival data are obtained from a multi-centre clinical trial, it is conceived that the environ mental conditions and facilities shared within clinic affects the proportion cured as well as the failure risk for the uncured individuals. It necessitates a long-term survivor mixture model with random effects. In this paper, the long-term survivor mixture model is extended for the analysis of multivariate failure time data using the generalized linear mixed model (GLMM) approach. The proposed model is applied to analyse a numerical data set from a multi-centre clinical trial of carcinoma as an illustration. Some simulation experiments are performed to assess the applicability of the model based on the average biases of the estimates formed. Copyright (C) 2001 John Wiley & Sons, Ltd.