Generalized estimating equations approach for spatial lattice data: A case study in adoption of improved maize varieties in Mozambique.

Generalized estimating equations (GEE) are extension of generalized linear models (GLM) widely applied in longitudinal data analysis. GEE are also applied in spatial data analysis using geostatistics methods. In this paper, we advocate application of GEE for spatial lattice data by modeling the spatial working correlation matrix using the Moran's index and the spatial weight matrix. We present theoretical developments and results for simulated and actual data as well. For the former case, 1,000 samples of a random variable (response variable) defined in (0, 1) interval were generated using different values of the Moran's index. In addition, 1,000 samples of a binary and a continuous variable were also randomly generated as covariates. In each sample, three structures of spatial working correlation matrices were used while modeling: The independent, autoregressive, and the Toeplitz structure. Two measures were used to evaluate the performance of each of the spatial working correlation structures: the asymptotic relative efficiency and the working correlation selection criterions. The results showed that both measures indicated that the autoregressive spatial working correlation matrix proposed in this paper presents the best performance in general. For the actual data case, the proportion of small farmers who used improved maize varieties was considered as the response variable and a set of nine variables were used as covariates. Two structures of spatial working correlation matrices were used and the results showed consistence with those obtained in the simulation study.

Uso dos métodos Mantel-Haenszel para a detecção do funcionamento diferencial dos itens e software relacionado / Using Mantel-Haenszel methods for detecting differential item functioning

As estatísticas englobadas sob a denominação métodos Mantel-Haenszel (MH), por suas simplicidades, baixo custo computacional e bons resultados, são os métodos mais empregadas para detectar o funcionamento diferencial dos itens (DIF). Os métodos MH podem ser usados para detectar o DIF tanto em itens dicotômicos como em itens politômicos, permitindo comparar dois ou mais grupos simultaneamente, e aplicam-se tanto no âmbito da Teoria Clássica dos Testes (TCT) como da Teoria da Resposta ao Item (TRI). Este artigo fornece uma visão completa e integrada dos métodos MH e apresenta um programa que permite aplicar essas estatísticas no estudo do DIF. O programa é gratuito e está disponível em Espanhol, Inglês e Português.(AU)

Statistics comprised under Mantel-Haenszel (MH) methods designation constitute one of the most popular and low cost differential item functioning (DIF) detection methods. Mantel-Haenszel methods permit DIF assessment of dichotomous and polytomous items in multiple groups simultaneously, and they can be applied both under Classical Test Theory and Item Response Theory. This paper provides a framework for integrating the different MH statistics used in DIF research, and describes the software that has been developed to provide an easy-to-use program for conducting DIF analyses using the statistics. The program is free of charge and it is available in the following languages: Spanish, English and Portuguese.(AU)

Uso dos métodos Mantel-Haenszel para a detecção do funcionamento diferencial dos itens e software relacionado / Using Mantel-Haenszel methods for detecting differential item functioning

As estatísticas englobadas sob a denominação métodos Mantel-Haenszel (MH), por suas simplicidades, baixo custo computacional e bons resultados, são os métodos mais empregadas para detectar o funcionamento diferencial dos itens (DIF). Os métodos MH podem ser usados para detectar o DIF tanto em itens dicotômicos como em itens politômicos, permitindo comparar dois ou mais grupos simultaneamente, e aplicam-se tanto no âmbito da Teoria Clássica dos Testes (TCT) como da Teoria da Resposta ao Item (TRI). Este artigo fornece uma visão completa e integrada dos métodos MH e apresenta um programa que permite aplicar essas estatísticas no estudo do DIF. O programa é gratuito e está disponível em Espanhol, Inglês e Português.

Statistics comprised under Mantel-Haenszel (MH) methods designation constitute one of the most popular and low cost differential item functioning (DIF) detection methods. Mantel-Haenszel methods permit DIF assessment of dichotomous and polytomous items in multiple groups simultaneously, and they can be applied both under Classical Test Theory and Item Response Theory. This paper provides a framework for integrating the different MH statistics used in DIF research, and describes the software that has been developed to provide an easy-to-use program for conducting DIF analyses using the statistics. The program is free of charge and it is available in the following languages: Spanish, English and Portuguese.

State-space algorithms for estimating spike rate functions.

The accurate characterization of spike firing rates including the determination of when changes in activity occur is a fundamental issue in the analysis of neurophysiological data. Here we describe a state-space model for estimating the spike rate function that provides a maximum likelihood estimate of the spike rate, model goodness-of-fit assessments, as well as confidence intervals for the spike rate function and any other associated quantities of interest. Using simulated spike data, we first compare the performance of the state-space approach with that of Bayesian adaptive regression splines (BARS) and a simple cubic spline smoothing algorithm. We show that the state-space model is computationally efficient and comparable with other spline approaches. Our results suggest both a theoretically sound and practical approach for estimating spike rate functions that is applicable to a wide range of neurophysiological data.

Interobserver agreement of Gleason score and modified Gleason score in needle biopsy and in surgical specimen of prostate cancer.

INTRODUCTION: Gleason score, which has a high interobserver variability, is used to classify prostate cancer. The most recent consensus valued the tertiary Gleason pattern and recommended its use in the final score of needle biopsies (modified Gleason score). This pattern is considered to be of high prognostic value in surgical specimens. This study emphasized the evaluation of the modified score agreement in needle biopsies and in surgical specimen, as well as the interobserver variability of this score. MATERIALS AND METHODS: Three pathologists evaluated the slides of needle biopsies and surgical specimens of 110 patients, reporting primary, secondary and tertiary Gleason patterns and after that, traditional and modified Gleason scores were calculated. Kappa test (K) assessed the interobserver agreement and the agreement between the traditional and modified scores of the biopsy and of the surgical specimen. RESULTS: Interobserver agreement in the biopsy was K = 0.36 and K = 0.35, and in the surgical specimen it was K = 0.46 and K = 0.36, for the traditional and modified scores, respectively. The tertiary Gleason grade was found in 8%, 0% and 2% of the biopsies and in 8%, 0% and 13% of the surgical specimens, according to observers 1, 2 and 3, respectively. When evaluating the agreement of the traditional and modified Gleason scores in needle biopsy with both scores of the surgical specimen, a similar agreement was found through Kappa. CONCLUSION: Contrary to what was expected, the modified Gleason score was not superior in the agreement between the biopsy score and the specimen, or in interobserver reproducibility, in this study.

Interobserver agreement of gleason score and modified gleason score in needle biopsy and in surgical specimen of prostate cancer

INTRODUCTION: Gleason score, which has a high interobserver variability, is used to classify prostate cancer. The most recent consensus valued the tertiary Gleason pattern and recommended its use in the final score of needle biopsies (modified Gleason score). This pattern is considered to be of high prognostic value in surgical specimens. This study emphasized the evaluation of the modified score agreement in needle biopsies and in surgical specimen, as well as the interobserver variability of this score MATERIALS AND METHODS: Three pathologists evaluated the slides of needle biopsies and surgical specimens of 110 patients, reporting primary, secondary and tertiary Gleason patterns and after that, traditional and modified Gleason scores were calculated. Kappa test (K) assessed the interobserver agreement and the agreement between the traditional and modified scores of the biopsy and of the surgical specimen RESULTS: Interobserver agreement in the biopsy was K = 0.36 and K = 0.35, and in the surgical specimen it was K = 0.46 and K = 0.36, for the traditional and modified scores, respectively. The tertiary Gleason grade was found in 8 percent, 0 percent and 2 percent of the biopsies and in 8 percent, 0 percent and 13 percent of the surgical specimens, according to observers 1, 2 and 3, respectively. When evaluating the agreement of the traditional and modified Gleason scores in needle biopsy with both scores of the surgical specimen, a similar agreement was found through Kappa CONCLUSION: Contrary to what was expected, the modified Gleason score was not superior in the agreement between the biopsy score and the specimen, or in interobserver reproducibility, in this study.