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Risk difference is an important measure of effect size in biostatistics, for both randomised and observational studies. The natural way to adjust risk differences for potential confounders is to use an additive binomial model, which is a binomial generalised linear model with an identity link...
Persistent link: https://www.econbiz.de/10010906924
In this article, we explain how to calculate adjusted risk ratios and risk differences when reporting results from logit, probit, and related nonlinear models. Building on Stata’s margins command, we create a new postestimation command, adjrr, that calculates adjusted risk ratios and adjusted...
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E-collaboration researchers usually employ P values for hypothesis testing, a common practice in a variety of other fields. This is also customary in many methodological contexts, such as analyses of path models with or without latent variables, as well as simpler tests that can be seen as...
Persistent link: https://www.econbiz.de/10012048790
Purpose – Quality function deployment (QFD) is a planning methodology to improve products, services and their associated processes by ensuring that the voice of the customer has been effectively deployed through specified and prioritised technical attributes (TAs). The purpose of this paper is...
Persistent link: https://www.econbiz.de/10014798386
Abstract This paper employs a Monte Carlo study to compare the performance of equal-tailed bootstrap percentile- t , symmetric bootstrap percentile- t , bootstrap percentile, and standard asymptotic confidence intervals in two distinct heteroscedastic regression models. Bootstrap confidence...
Persistent link: https://www.econbiz.de/10014612565
Abstract We propose a new approach to statistical inference on parameters that depend on population parameters in a non-standard way. As examples we consider a parameter that is interval identified and a parameter that is the maximum (or minimum) of population parameters. In both examples we...
Persistent link: https://www.econbiz.de/10014612567
Abstract The Fetal–Infant mortality rate (FIMR) is the basic surveillance statistic in perinatal periods of risk (PPOR) analyses. This paper presents a model for the FIMR as the ratio of two Poisson random variables. From this model, expressions for estimators of variance, standard error, and...
Persistent link: https://www.econbiz.de/10014590651