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Additive Risk versus Additive Relative Risk Models
Epidemiology, 1993The distinction between additive risk models and additive relative risk models is important when nonadditivity is used as a criterion for interdependence of causal effects (causal interaction). I show here that, in stratified studies, additive relative risk models do not provide the often-assumed correspondence between additivity and absence of causal ...
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2016
This chapter formulates and demonstrates generalized additive models (GAMs) for means of continuous outcomes treated as independent and normally distributed with constant variances as in linear regression and for logits (log odds) of means of dichotomous discrete outcomes with unit dispersions as in logistic regression.
George J. Knafl, Kai Ding
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This chapter formulates and demonstrates generalized additive models (GAMs) for means of continuous outcomes treated as independent and normally distributed with constant variances as in linear regression and for logits (log odds) of means of dichotomous discrete outcomes with unit dispersions as in logistic regression.
George J. Knafl, Kai Ding
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2010
This chapter returns to the problem of modelling the effect of continuous variables like age or engine power. Introducing the concept of penalized deviances leads to the use of cubic splines, a well-known tool in numerical analysis. Representing cubic splines in terms of so called B-splines makes it possible to formulate an estimation problem in terms ...
Esbjörn Ohlsson, Björn Johansson
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This chapter returns to the problem of modelling the effect of continuous variables like age or engine power. Introducing the concept of penalized deviances leads to the use of cubic splines, a well-known tool in numerical analysis. Representing cubic splines in terms of so called B-splines makes it possible to formulate an estimation problem in terms ...
Esbjörn Ohlsson, Björn Johansson
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2017
In this chapter, you will create models using additional modeling tools.
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In this chapter, you will create models using additional modeling tools.
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2003
The models fit in Chap. 2 have two limitations. First, the conditional distribution of the response, given the predictors, is assumed to be Gaussian. Second, only a single predictor is allowed to have a smooth nonlinear effect—the other predictors are modeled linearly.
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The models fit in Chap. 2 have two limitations. First, the conditional distribution of the response, given the predictors, is assumed to be Gaussian. Second, only a single predictor is allowed to have a smooth nonlinear effect—the other predictors are modeled linearly.
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2012
The issue concerning this chapter is that covariates need not enter a generalised linear model merely as linear terms. Quadratic and higher-order terms can sometimes be useful in explaining variation in the data. In this chapter nonlinearities are explored using several techniques; discretisation, polynomial regression, splines and generalised additive
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The issue concerning this chapter is that covariates need not enter a generalised linear model merely as linear terms. Quadratic and higher-order terms can sometimes be useful in explaining variation in the data. In this chapter nonlinearities are explored using several techniques; discretisation, polynomial regression, splines and generalised additive
openaire +1 more source