Conditional symmetry model as a better alternative to Symmetry Model for rater agreement measure

In almost all life or social science researches, subjects are classified into categories by raters, interviewers or observers. Many approaches have been proposed by various authors for analyzing the data or the results obtained from these raters. Symmetry and conditional symmetry models are models designed for square tables like the one arising from the raters results. Conditional symmetry model which possessed an extra parameter for the off-diagonal cells is a special case to symmetry. In this research work, we examined the effect of the extra parameter introduced by conditional symmetry model over that of symmetry on structure of agreement as well as their fittings. Generalized linear model (GLM) approach was used to model the loglinear model forms of these models with empirical examples. We observed that conditional symmetry based on it extra parameter gave a tremendous improvement to the significant level of the test statistics over that of its symmetry model counterpart, hence conditional symmetry model is better for raters agreement modelling which require symmetric table

A review of agreement measure as a subset of association measure between raters

Agreement can be regarded as a special case of association and not the other way round. Virtually in all life or social science researches, subjects are being classified into categories by raters, interviewers or observers and both association and agreement measures can be obtained from the results of this researchers. The distinction between association and agreement for a given data is that, for two responses to be perfectly associated we require that we can predict the category of one response from the category of the other response, while for two response to agree, they must fall into the identical category. Which hence mean, once there is agreement between the two responses, association has already exist, however, strong association may exist between the two responses without any strong agreement. Many approaches have been proposed by various authors for measuring each of these measures. In this work, we present some up till date development on these measures statistics

Modelling Negative Binomial as a substitute model to Poisson for raters agreement on ordinal scales with sparse data

The Poisson distribution has been widely used for modelling rater agreement using loglinear models. Mostly in all life or social science researches, subjects are being classified into categories by rater, interviewers or observers and most of these tables indicate that the cell counts are mixtures of either too big values and two small values or zeroes which are sparse data. We refer to sparse as a situation when a large number of cell frequencies are very small. For these kinds of tables, there are tendencies for overdispersion in which the variance of the outcome or response exceeds the nominal variance, that is, when the response is greater than it should be under the given model or the true variance is bigger than the mean. In these types of situations assuming Poisson models means we are imposing the mean-variance equality restriction on the estimation. This implies that we will effectively be requiring the variance to be less than it really is, and also, as a result, we will underestimate the true variability in the data. Lastly, this will lead us to underestimating the standard errors, and so to overestimating the degree of precision in the coefficients. The Negative Binomial, which has a variance function, would be better for modelling rater agreement with sparse data in the table in order to allow the spread of the observations or counts. We observed that assuming Negative Binomial as the underline sampling plan is better for modelling rater agreement when there are sparse data in a limited number of example

Involvement of ras p2I in Neurotrophin-induced Response of Sensory, but Not Sympathetic Neurons

Little is known about the signal transduction mechanisms involved in the response to neurotrophins and other neurotrophic factors in neurons, beyond the activation of the tyrosine kinase activity of the neurotrophin receptors belonging to the trk family. We have previously shown that the introduction of the oncogene product ras p21 into the cytoplasm of chick embryonic neurons can reproduce the survival and neurite-outgrowth promoting effects of the neurotrophins nerve growth factor (NGF) and brain-derived neurotrophic factor (BDNF), and of ciliary neurotrophic factor (CNTF). To assess the potential signal- transducing role of endogenous ras p21, we introduced function-blocking anti-ras antibodies or their Fab fragments into cultured chick embryonic neurons. The BDNF-induced neurite outgrowth in E12 nodose ganglion neurons was reduced to below control levels, and the NGF- induced survival of E9 dorsal root ganglion (DRG) neurons was inhibited in a specific and dose-dependent fashion. Both effects could be reversed by saturating the epitope-binding sites with biologically inactive ras p21 before microinjection. Surprisingly, ras p21 did not promote the survival of NGF-dependent E12 chick sympathetic neurons, and the NGF-induced survival in these cells was not inhibited by the Fab-fragments. The survival effect of CNTF on ras-responsive ciliary neurons could not be blocked by anti-ras Fab fragments. These results indicate an involvement of ras p21 in the signal transduction of neurotrophic factors in sensory, but not sympathetic or ciliary neurons, pointing to the existence of different signaling pathways not only in CNTF-responsive, but also in neurotrophin-responsive neuronal populations

Ras p21 protein promotes survival and fiber outgrowth of cultured embryonic neurons

Although evidence obtained with the PC12 cell line has suggested a role for the ras oncogene proteins in the signal transduction of nerve growth factor-mediated fiber outgrowth, little is known about the signal transduction mechanisms involved in the neuronal response to neurotrophic factors in nontransformed cells. We report here that the oncogene protein T24-ras, when introduced into the cytoplasm of freshly dissociated chick embryonic neurons, promotes the in vitro survival and neurite outgrowth of nerve growth factor-responsive dorsal root ganglion neurons, brain-derived neurotrophic factor-responsive nodose ganglion neurons, and ciliary neuronotrophic factor-responsive ciliary ganglion neurons. The proto-oncogene product c-Ha-ras also promotes neuronal survival, albeit less strongly. No effect could be observed with truncated counterparts of T24-ras and c-Ha-ras lacking the 23 C-terminal amino acids including the membrane-anchoring, palmityl-accepting cysteine. These results suggest a generalized involvement of ras or ras-like proteins in the intracellular signal transduction pathway for neurotrophic factors

MAREG and WinMAREG

This paper describes a software tool for marginal regression methods. MAREG currently handles binary, categorical and continious data with several link functions. Although intended for the analysis of correlated data, uncorrelated data can be analysed. We supplies two different approaches for these problems-Maximum Likelihood and GEE methods. Handling of missing data is also provided. [ Published in: Computational Statistics and Data Analysis, 24, 235-241

Regression modelling with fixed effects - missing values and other problems

The paper considers new devices to predict the response variable using a convex target function weighting the response and its expectation. A MDEP-matrix superiority condition is given concerning BLUE, RLSE and mixed estimator where the latter is used in case of imputation for missing values. A small simulation study compares the alternative estimators. Finally the detection of non-MCAR processes in linear regression is discussed

WinMAREG Quick Start

This paper is a short introduction into the usage of WinMAREG. Two examples are used to illustrate the most common options and features of the software

C++ Utilities zur Implementierung statistischer Verfahren unter Berücksichtigung fehlender Werte

Die hier vorgestellten Erweiterungen der bereits bestehenden generischen Bibliothek zur linearen Algebra (Fieger, A., Heumann, C., Kastner, C., Watzka, K., 1997:(Discussion Paper 63) stellen Funktionen bereit, die bei der Implementierung statistischer Verfahren benötigt werden. Besondere Beachtung findet der Umgang mit fehlenden Daten

New features in MAREG 0.2.0

This paper describes changes in the software tool MAREG from version 0.0.7 (July 1997) to version 0.2.0 (June 1999). As these new features are not implemented in WinMAREG yet, they can only be used via editing the ini-files (*.cai). Handling and features of Version 0.0.7 are described in Fieger, Heumann and Kastner (1996), Fieger, Heumann and Kastner (1998) and Kastner, Fieger and Heumann (1997)