BACKGROUND Recent communications have argued that often it may not be appropriate to analyse cross-sectional studies of prevalent outcomes with logistic regression models. The purpose of this communication is to compare three methods that have been proposed for application to cross sectional studies: (1) a multiplicative generalized linear model, which we will call the log-binomial model, (2) a...

We applaud Drs. Spiegelman and Hertzmark’s idea of using SAS procedure PROC GENMOD to estimate the risk ratio or difference (1). However, we have reservations about 1) the claim that there is no good justification for fitting the logistic regression and estimating the odds ratio when the odds ratio is not a good approximation of the risk ratio, and 2) using Poisson regression (PROC GENMOD) to e...

In cohort studies binary outcomes are very often analyzed by logistic regression. However, it is well-known that when the goal is to estimate a risk ratio, the logistic regression is inappropriate if the outcome is common. In these cases, a log-binomial regression model is preferable. On the other hand, the estimation of the regression coefficients of the log-binomial model is difficult due to ...

background: we designed an algorithmic model based on the logistic regression analysis and a non-algorithmic model based on the artificial neural network (ann). materials and methods: the ability of these models was compared together in clinical application to differentiate malignant from benign breast tumors in a study group of 161 patients' records. each patient’s record consisted of 6 s...

INTRODUCTION Count data are often collected in chronic disease research, and sometimes these data have a skewed distribution. The number of unhealthy days reported in the Behavioral Risk Factor Surveillance System (BRFSS) is an example of such data: most respondents report zero days. Studies have either categorized the Healthy Days measure or used linear regression models. We used alternative r...

The number of the unique individuals in the population is of great importance in evaluating the disclosure risk of a microdata set. We approach this problem by considering some basic superpopulation models including the gamma-Poisson model of Bethlehem et al. (1990). We introduce Dirichlet-multinomial model which is closely related but more basic than the gamma-Poisson model, in the sense that ...

Many dynamic revenue management models divide the sale period into a finite number of periods T and assume, invoking a fine-enough grid of time, that each period sees at most one booking request. These Poisson-type assumptions restrict the variability of the demand in the model, but researchers and practitioners were willing to overlook this for the benefit of tractability of the models. In thi...