This timepoint was selected because it captures a midpoint in the postquit period and is a inhibitor Tofacitinib time when predictors still have strong predictive validity (prediction models tend to lose predictive validity as the prediction interval grows increasingly long). Items were entered individually as predictors based on the magnitude of their corresponding Wald statistics; items were entered until no remaining item in the collective pool yielded a statistically significant (p<.05) Wald statistic. Items were entered in this way to identify a small item set that not only would be predictive of relapse but also would consist of items that were nonoverlapping in accounting for likelihood of relapse.
Once the stepwise logistic regression procedure was completed, the process was repeated, now omitting from the collective pool of potential predictors those items that had been entered previously as predictors in the first logistic regression. The result of this second analysis is a second item set predictive of relapse, likely possessing substantial overlap with the first item set. This process was repeated a total of four times, each time omitting items that had been identified as significant predictors in all prior analyses. In the end, 26 items were identified as predictors of relapse across the five analyses. The purpose of iteratively repeating the analysis was to lend greater insight into the underlying characteristics of the items that made them important predictors of relapse. Items associated with common factors measured by other significant predictors were viewed as being more likely to yield significance in the cross-validation analysis.
The responses to the 26 items were next analyzed using exploratory factor analysis. A maximum likelihood solution was obtained and interpreted following Promax rotation. Several common factors emerged, most notably factors related to ��morning smoking,�� ��strength of cravings,�� ��environmental Carfilzomib smoking,�� and ��number of cigarettes smoked.�� The items for the final WI-PREPARE were selected based on two criteria: (a) measurement of a common factor and (b) magnitude of Wald statistic in logistic regression analysis. For each common factor, only the item with the highest factor loading was chosen as a representative of the factor.
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