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Planned analyses cover a broad range of research questions, including building a predictor model for AM and on correlates of AM, PA and crash risk.
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Selecting the most relevant variables is usually suboptimal for building a predictor.
This reflects the fact that fitting a model to a single variable does not result in as much over-optimism (over-fitting) as building a multivariate model using a large number of predictors.
We performed also a logistic regression analysis with the diagnosis as dependent variable and the 4DSQ scores as independent variables, building a parsimonious model by stepwise backward elimination of non-significant "predictors" (p > 0.05).
When building a regression model for relating a response to a large number of predictors, researchers often try fitting a multiple linear regression model first.
Building a new model has been challenging.
PSM involved three steps towards building a logistic regression model to derive predictors of quitting.
Building a flexible multivariable model is challenging, especially if potential predictors are correlated (Sauerbrei et al, 2007).
In the context of building a model to predict clinical response to antiretrovirals and other anti-infectives, including maraviroc, the curse of dimensionality means that as the number of potential predictors increases it becomes harder to find the best model.
Two hypotheses emerged in building a model of TOR.
In contrast to traditional multivariate modeling, the random forest analysis can account for inter-relationships among all potential predictors including highly multilevel unordered categorical covariates in building a tree-based predictive model.
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