Several economists agree to say that the need for adjustment was essential for African countries over the decade of the 80’s. The econometric analysis of a sample of 28 sub-Saharan African countries, from variables r...Several economists agree to say that the need for adjustment was essential for African countries over the decade of the 80’s. The econometric analysis of a sample of 28 sub-Saharan African countries, from variables regarded as “representatives” for the adjustment objectives, proves that this assertion cannot be completely rejected.展开更多
Clinicians need to predict the number of involved nodes in breast cancer patients in order to ascertain severity, prognosis, and design subsequent treatment. The distribution of involved nodes often displays over-disp...Clinicians need to predict the number of involved nodes in breast cancer patients in order to ascertain severity, prognosis, and design subsequent treatment. The distribution of involved nodes often displays over-dispersion—a larger variability than expected. Until now, the negative binomial model has been used to describe this distribution assuming that over-dispersion is only due to unobserved heterogeneity. The distribution of involved nodes contains a large proportion of excess zeros (negative nodes), which can lead to over-dispersion. In this situation, alternative models may better account for over-dispersion due to excess zeros. This study examines data from 1152 patients who underwent axillary dissections in a tertiary hospital in India during January 1993-January 2005. We fit and compare various count models to test model abilities to predict the number of involved nodes. We also argue for using zero inflated models in such populations where all the excess zeros come from those who have at some risk of the outcome of interest. The negative binomial regression model fits the data better than the Poisson, zero hurdle/inflated Poisson regression models. However, zero hurdle/inflated negative binomial regression models predicted the number of involved nodes much more accurately than the negative binomial model. This suggests that the number of involved nodes displays excess variability not only due to unobserved heterogeneity but also due to excess negative nodes in the data set. In this analysis, only skin changes and primary site were associated with negative nodes whereas parity, skin changes, primary site and size of tumor were associated with a greater number of involved nodes. In case of near equal performances, the zero inflated negative binomial model should be preferred over the hurdle model in describing the nodal frequency because it provides an estimate of negative nodes that are at “high-risk” of nodal involvement.展开更多
The objective of this study is to determine the role that obesity plays in how often Canadians visit their family doctors or general practitioners. Doctor visits are analyzed using mixtures of ordered probability mode...The objective of this study is to determine the role that obesity plays in how often Canadians visit their family doctors or general practitioners. Doctor visits are analyzed using mixtures of ordered probability models applied to sample survey data from the 2010 Canadian Community Health Survey. This procedure is shown to be superior in terms of likelihood criteria to the more usual one involving count models of doctor visits. The main result is that obesity is one of the leading causes of doctor visits. Obesity has become more important in the demand for physician services than smoking for all Canadians. Other factors including diabetes, the individual’s level of education, position in the income distribution, and drinking behavior are also important. The application of latent class’s ordered probability models by age-group and gender leads to results which are different from what others have found. While obesity is shown to be a serious problem in Canada, it has not yet reached the stage which some researchers have described as critical.展开更多
文摘Several economists agree to say that the need for adjustment was essential for African countries over the decade of the 80’s. The econometric analysis of a sample of 28 sub-Saharan African countries, from variables regarded as “representatives” for the adjustment objectives, proves that this assertion cannot be completely rejected.
文摘Clinicians need to predict the number of involved nodes in breast cancer patients in order to ascertain severity, prognosis, and design subsequent treatment. The distribution of involved nodes often displays over-dispersion—a larger variability than expected. Until now, the negative binomial model has been used to describe this distribution assuming that over-dispersion is only due to unobserved heterogeneity. The distribution of involved nodes contains a large proportion of excess zeros (negative nodes), which can lead to over-dispersion. In this situation, alternative models may better account for over-dispersion due to excess zeros. This study examines data from 1152 patients who underwent axillary dissections in a tertiary hospital in India during January 1993-January 2005. We fit and compare various count models to test model abilities to predict the number of involved nodes. We also argue for using zero inflated models in such populations where all the excess zeros come from those who have at some risk of the outcome of interest. The negative binomial regression model fits the data better than the Poisson, zero hurdle/inflated Poisson regression models. However, zero hurdle/inflated negative binomial regression models predicted the number of involved nodes much more accurately than the negative binomial model. This suggests that the number of involved nodes displays excess variability not only due to unobserved heterogeneity but also due to excess negative nodes in the data set. In this analysis, only skin changes and primary site were associated with negative nodes whereas parity, skin changes, primary site and size of tumor were associated with a greater number of involved nodes. In case of near equal performances, the zero inflated negative binomial model should be preferred over the hurdle model in describing the nodal frequency because it provides an estimate of negative nodes that are at “high-risk” of nodal involvement.
文摘The objective of this study is to determine the role that obesity plays in how often Canadians visit their family doctors or general practitioners. Doctor visits are analyzed using mixtures of ordered probability models applied to sample survey data from the 2010 Canadian Community Health Survey. This procedure is shown to be superior in terms of likelihood criteria to the more usual one involving count models of doctor visits. The main result is that obesity is one of the leading causes of doctor visits. Obesity has become more important in the demand for physician services than smoking for all Canadians. Other factors including diabetes, the individual’s level of education, position in the income distribution, and drinking behavior are also important. The application of latent class’s ordered probability models by age-group and gender leads to results which are different from what others have found. While obesity is shown to be a serious problem in Canada, it has not yet reached the stage which some researchers have described as critical.
基金National Nature Science Foundation of China(11475003)Science and Technology Major Project of Anhui Province (18030901021)Anhui Provincial Department of Education outstanding top-notch talent-funded projects (gxbjZD26)。
