Avaliação dos fatores determinantes da estética do perfil facial

OBJETIVO: avaliar a influência da idade, do sexo, da relação oclusal sagital, do Padrão Facial e de 8 medidas do perfil facial sobre a estética do perfil. MÉTODOS: foram utilizadas tabelas de contingência, o Teste Qui-quadrado e o coeficiente de Cramér para avaliar a possível associação entre a nota dada por 32 avaliadores (14 ortodontistas, 12 leigos e 6 artistas) para a estética do perfil de 100 brasileiros - adultos, leucodermas, portadores de selamento labial passivo - e a idade, o sexo, a relação oclusal sagital, o Padrão Facial e as variáveis da análise facial numérica do perfil. RESULTADOS: não foi observada associação entre a idade, o sexo e a relação oclusal sagital e a estética do perfil facial. A associação foi observada entre a nota recebida para a estética do perfil e o Padrão Facial, o ângulo de convexidade facial e o ângulo do terço inferior da face. CONCLUSÃO: o Padrão Facial, definido na avaliação do perfil pela convexidade do perfil facial, e a projeção anterior do mento foram, entre os fatores avaliados, os determinantes para a estética do perfil facial.

Renal Cell Carcinoma: T1 and T2 Signal Intensity Characteristics of Papillary and Clear Cell Types Correlated with Pathology

OBJECTIVE. The objective of our study was to describe the T1 and T2 signal intensity characteristics of papillary renal cell carcinoma (RCC) and clear cell RCC with pathologic correlation. MATERIALS AND METHODS. Of 539 RCCs, 49 tumors (21 papillary RCCs and 28 clear cell RCCs) in 45 patients were examined with MRI. Two radiologists retrospectively and independently assessed each tumor`s T1 and T2 signal intensity qualitatively and quantitatively (i.e., the signal intensity [SI] ratio [tumor SI/renal cortex SI]). Of the 49 tumors, 37 (76%) were assessed for pathology features including tumor architecture and the presence of hemosiderin, ferritin, necrosis, and fibrosis. MRI findings and pathology features were correlated. Statistical methods included summary statistics and Wilcoxon`s rank sum test for signal intensity, contingency tables for assessing reader agreement, concordance rate between the two readers with 95% CIs, and Fisher`s exact test for independence, all stratified by RCC type. RESULTS. Papillary RCCs and clear cell RCCs had a similar appearance and signal intensity ratio on T1-weighted images. On T2-weighted images, most papillary RCCs were hypointense (reader 1, 13/21; reader 2, 14/21), with an average mean signal intensity ratio for both readers of 0.67 +/- 0.2, and none was hyperintense, whereas most clear cell RCCs were hyperintense (reader 1, 21/28; reader 2, 17/28), with an average mean signal intensity ratio for both readers of 1.41 +/- 0.4 (p < 0.05). A tumor T2 signal intensity ratio of <= 0.66 had a specificity of 100% and sensitivity of 54% for papillary RCC. Most T2 hypointense tumors exhibited predominant papillary architecture; most T2 hyperintense tumors had a predominant nested architecture (p < 0.05). CONCLUSION. On T2-weighted images, most papillary RCCs are hypointense and clear cell RCCs, hyperintense. The T2 hypointense appearance of papillary RCCs correlated with a predominant papillary architecture at pathology.

Inferential Implications of Over-Parametrization: A Case Study in Incomplete Categorical Data

P>In the context of either Bayesian or classical sensitivity analyses of over-parametrized models for incomplete categorical data, it is well known that prior-dependence on posterior inferences of nonidentifiable parameters or that too parsimonious over-parametrized models may lead to erroneous conclusions. Nevertheless, some authors either pay no attention to which parameters are nonidentifiable or do not appropriately account for possible prior-dependence. We review the literature on this topic and consider simple examples to emphasize that in both inferential frameworks, the subjective components can influence results in nontrivial ways, irrespectively of the sample size. Specifically, we show that prior distributions commonly regarded as slightly informative or noninformative may actually be too informative for nonidentifiable parameters, and that the choice of over-parametrized models may drastically impact the results, suggesting that a careful examination of their effects should be considered before drawing conclusions.Resume Que ce soit dans un cadre Bayesien ou classique, il est bien connu que la surparametrisation, dans les modeles pour donnees categorielles incompletes, peut conduire a des conclusions erronees. Cependant, certains auteurs persistent a negliger les problemes lies a la presence de parametres non identifies. Nous passons en revue la litterature dans ce domaine, et considerons quelques exemples surparametres simples dans lesquels les elements subjectifs influencent de facon non negligeable les resultats, independamment de la taille des echantillons. Plus precisement, nous montrons comment des a priori consideres comme peu ou non-informatifs peuvent se reveler extremement informatifs en ce qui concerne les parametres non identifies, et que le recours a des modeles surparametres peut avoir sur les conclusions finales un impact considerable. Ceci suggere un examen tres attentif de l`impact potentiel des a priori.

Missing data mechanisms and their implications on the analysis of categorical data

We review some issues related to the implications of different missing data mechanisms on statistical inference for contingency tables and consider simulation studies to compare the results obtained under such models to those where the units with missing data are disregarded. We confirm that although, in general, analyses under the correct missing at random and missing completely at random models are more efficient even for small sample sizes, there are exceptions where they may not improve the results obtained by ignoring the partially classified data. We show that under the missing not at random (MNAR) model, estimates on the boundary of the parameter space as well as lack of identifiability of the parameters of saturated models may be associated with undesirable asymptotic properties of maximum likelihood estimators and likelihood ratio tests; even in standard cases the bias of the estimators may be low only for very large samples. We also show that the probability of a boundary solution obtained under the correct MNAR model may be large even for large samples and that, consequently, we may not always conclude that a MNAR model is misspecified because the estimate is on the boundary of the parameter space.