A bayesian network meta-analysis on comparisons of enamel matrix derivatives, guided tissue regeneration and their combination therapies

Aims: Guided tissue regeneration (GTR) and enamel matrix derivatives (EMD) are two popular regenerative treatments for periodontal infrabony lesions. Both have been used in conjunction with other regenerative materials. We conducted a Bayesian network meta-analysis of randomized controlled trials on treatment effects of GTR, EMD and their combination therapies. Material and Methods: A systematic literature search was conducted using the Medline, EMBASE, LILACS and CENTRAL databases up to and including June 2011. Treatment outcomes were changes in probing pocket depth (PPD), clinical attachment level (CAL) and infrabony defect depth. Different types of bone grafts were treated as one group and so were barrier membranes. Results: A total of 53 studies were included in this review, and we found small differences between regenerative therapies which were non-significant statistically and clinically. GTR and GTR-related combination therapies achieved greater PPD reduction than EMD and EMD-related combination therapies. Combination therapies achieved slightly greater CAL gain than the use of EMD or GTR alone. GTR with BG achieved greatest defect fill. Conclusion: Combination therapies performed better than single therapies, but the additional benefits were small. Bayesian network meta-analysis is a promising technique to compare multiple treatments. Further analysis of methodological characteristics will be required prior to clinical recommendations.

Spirometric values in elderly asthmatic patients are not influenced by obesity

Background Studies have suggested that asthma in obese individuals differs from the classic asthma phenotype, presenting as a disease that is more difficult to control. Objective The objective of the present study was to determine whether obesity, age or a combination of the two are associated with worse spirometry parameters in patients with asthma. Methods This was an observational cross-sectional study involving patients over 18 years of age who had been diagnosed with asthma (allergic or nonallergic). We evaluated the results of their spirometric tests. The patients were classified in accordance with two criteria: body mass index (BMI) and age. Based on their BMIs, the patients were divided into three groups: normal weight, overweight and obese. Patients were also separated into two categories by age: 18-59 years of age; and >= 60 years of age. Results We evaluated 451 patients with asthma and their spirometry tests. In the present study, the pulmonary function parameters were negatively correlated with BMI and age (P < 0.05). We found that there was a statistically significant correlation between spirometric values and BMI among patients 18-59 years of age (P < 0.001), however, among patients over 60, we did not observe this negative association. Conclusions and Clinical Relevance The spirometric values decreased significantly in proportion to the increase of BMI and age in patients with asthma, especially among young adults. There was no negative correlation between BMI and FEV1 in the group >= 60 years of age, suggesting that perhaps the time of disease is a major factor in the loss of lung function than weight gain in the elderly.

Decreasing the number of false positives in sequence classification

Abstract Background A large number of probabilistic models used in sequence analysis assign non-zero probability values to most input sequences. To decide when a given probability is sufficient the most common way is bayesian binary classification, where the probability of the model characterizing the sequence family of interest is compared to that of an alternative probability model. We can use as alternative model a null model. This is the scoring technique used by sequence analysis tools such as HMMER, SAM and INFERNAL. The most prevalent null models are position-independent residue distributions that include: the uniform distribution, genomic distribution, family-specific distribution and the target sequence distribution. This paper presents a study to evaluate the impact of the choice of a null model in the final result of classifications. In particular, we are interested in minimizing the number of false predictions in a classification. This is a crucial issue to reduce costs of biological validation. Results For all the tests, the target null model presented the lowest number of false positives, when using random sequences as a test. The study was performed in DNA sequences using GC content as the measure of content bias, but the results should be valid also for protein sequences. To broaden the application of the results, the study was performed using randomly generated sequences. Previous studies were performed on aminoacid sequences, using only one probabilistic model (HMM) and on a specific benchmark, and lack more general conclusions about the performance of null models. Finally, a benchmark test with P. falciparum confirmed these results. Conclusions Of the evaluated models the best suited for classification are the uniform model and the target model. However, the use of the uniform model presents a GC bias that can cause more false positives for candidate sequences with extreme compositional bias, a characteristic not described in previous studies. In these cases the target model is more dependable for biological validation due to its higher specificity.

Clinical judgment to estimate pretest probability in the diagnosis of Cushing's syndrome under a Bayesian perspective

OBJECTIVE: To estimate the pretest probability of Cushing's syndrome (CS) diagnosis by a Bayesian approach using intuitive clinical judgment. MATERIALS AND METHODS: Physicians were requested, in seven endocrinology meetings, to answer three questions: "Based on your personal expertise, after obtaining clinical history and physical examination, without using laboratorial tests, what is your probability of diagnosing Cushing's Syndrome?"; "For how long have you been practicing Endocrinology?"; and "Where do you work?". A Bayesian beta regression, using the WinBugs software was employed. RESULTS: We obtained 294 questionnaires. The mean pretest probability of CS diagnosis was 51.6% (95%CI: 48.7-54.3). The probability was directly related to experience in endocrinology, but not with the place of work. CONCLUSION: Pretest probability of CS diagnosis was estimated using a Bayesian methodology. Although pretest likelihood can be context-dependent, experience based on years of practice may help the practitioner to diagnosis CS. Arq Bras Endocrinol Metab. 2012;56(9):633-7

Spatio-temporal patterns of tuberculosis incidence in Ribeirão Preto, State of São Paulo, southeast Brazil, and their relationship with social vulnerability: a Bayesian analysis

INTRODUCTION: The purpose of this ecological study was to evaluate the urban spatial and temporal distribution of tuberculosis (TB) in Ribeirão Preto, State of São Paulo, southeast Brazil, between 2006 and 2009 and to evaluate its relationship with factors of social vulnerability such as income and education level. METHODS: We evaluated data from TBWeb, an electronic notification system for TB cases. Measures of social vulnerability were obtained from the SEADE Foundation, and information about the number of inhabitants, education and income of the households were obtained from Brazilian Institute of Geography and Statistics. Statistical analyses were conducted by a Bayesian regression model assuming a Poisson distribution for the observed new cases of TB in each area. A conditional autoregressive structure was used for the spatial covariance structure. RESULTS: The Bayesian model confirmed the spatial heterogeneity of TB distribution in Ribeirão Preto, identifying areas with elevated risk and the effects of social vulnerability on the disease. We demonstrated that the rate of TB was correlated with the measures of income, education and social vulnerability. However, we observed areas with low vulnerability and high education and income, but with high estimated TB rates. CONCLUSIONS: The study identified areas with different risks for TB, given that the public health system deals with the characteristics of each region individually and prioritizes those that present a higher propensity to risk of TB. Complex relationships may exist between TB incidence and a wide range of environmental and intrinsic factors, which need to be studied in future research.

Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series

In this work we compared the estimates of the parameters of ARCH models using a complete Bayesian method and an empirical Bayesian method in which we adopted a non-informative prior distribution and informative prior distribution, respectively. We also considered a reparameterization of those models in order to map the space of the parameters into real space. This procedure permits choosing prior normal distributions for the transformed parameters. The posterior summaries were obtained using Monte Carlo Markov chain methods (MCMC). The methodology was evaluated by considering the Telebras series from the Brazilian financial market. The results show that the two methods are able to adjust ARCH models with different numbers of parameters. The empirical Bayesian method provided a more parsimonious model to the data and better adjustment than the complete Bayesian method.

One-repetition maximum test and isokinetic leg extension and flexion: correlations and predicted values

The most typical maximum tests for measuring leg muscle performance are the one-repetition maximum leg press test (1RMleg) and the isokinetic knee extension/flexion (IKEF) test. Nevertheless, their inter-correlations have not been well documented, mainly the predicted values of these evaluations. This correlational and regression analysis study involved 30 healthy young males aged 18-24y, who have performed both tests. Pearson's product moment correlation between 1RMleg and IKEF varied from 0.20 to 0.69 and the more exact predicted test was to 1RMleg (R2 = 0.71). The study showed correlations between 1RMleg and IKEF although these tests are different (isotonic vs. isokinetic) and provided further support for cross determination of 1RMleg and IKEF by linear and multiple linear regression analysis.