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Table 1 shows the models we fitted, their number of parameters, AIC/BIC relative to the minimum, and the first moment that diverges according to (14). Figure 2 shows the results of performing likelihood ratio tests.
To facilitate quantitative comparison of the causal inference model with other models, we fitted the parameters individually to each subject's data using a maximum-likelihood procedure: we maximized the probability of the data under the model.
To estimate patient-specific parameters in individual models, we fitted the model by the least-squares method to the pertinent "training set" that included all pre-treatment and several initial in-treatment PSA values for each patient; the number of training data points could differ among the patients.
[ 33, 36] The baseline RMDQ score significantly explained 2% of the residual variance in transition rating in the regression models we fitted.
On the basis of these models, we fitted the corresponding logistic models in which the cumulative variables were replaced by the indicator variables of their quartiles.
To improve the statistical properties of the regression models, we fitted all models to the natural logarithm of the reported risk estimates (Berlin et al. 1993).
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The health modeling stage of the two-stage approach included the health models we fit and the measurement error correction methods we employed.
For those company's models, we fit their cars into the rankings based on the highest base prices of which we were aware.
The spline models we fit to each wing allow the instantaneous calculation of any function of wing shape.
In what follows, we describe each class of annotation variable, its source and the parameterization we use for it in the models we fit.
In both non-DV and DV models, we fit the upper quantile and lower quantile trend model at τ upper = 0.85 and τ lower = 0.15 respectively.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com