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Three general strategies for characterizing environmental exposures include questionnaires, environmental samples, and predictive models.
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The 10-item version of the cocaine craving questionnaire (CCQ-Brief) has not been validated in a mixed-gender sample, and predictive validity of the CCQ-Now and CCQ-Brief in terms of their relationship with cocaine relapse has not been demonstrated.
In model B, missing baseline systolic BP data were imputed, which involves calculating average results over a set of imputed datasets by using Gibbs sampler and predictive mean matching.
Quality coefficients below 20% for the accuracy error on mass fractions' prediction in unknown samples, and residual predictive deviations in general above 5, were obtained.
The number of samples, prior draws, and predictive samples were set to 1, meaning that both observed and predictive sample sizes equaled 500 for each character pair.
A ten-variable instrument was constructed by applying stepwise variable selection algorithms to the training samples, and its predictive performance was evaluated on the validation samples.
We will also evaluate mechanistically-based pharmacokinetic/pharmacodynamic biomarkers on tumor and blood samples and the predictive role of early (within 11 days after the beginning of SCRT) tumor metabolic changes measured by 18FDG-PET/TC in patients undergoing phase II trial.
This reduction in variance allows us to display the results in a more insightful manner and ensures further inferences on the relation between our factors (e.g., sample size) and predictive accuracy are not influenced by aberrant observations.
Prediction models tend to overfit the development sample, and the predictive validity of these variables should be tested in other, larger samples.
The analysis though was limited to the original sample, and the predictive response in novel data was not explicitly examined.
To gauge the dependency of system performance on the size of the training set, we progressively reduced the training data by iteratively discarding subsets from the training sample and refitting predictive models.
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