Selecting pre-screening items for early intervention trials of XXXX --a case study.

@entity303 @entity73 @entity1274
Our goal was to review and extend statistical methods for discriminating between normal subjects and those with @entity303 or @entity73 . We compared six different methods to one constructed by expert opinion, in their brevity and predictive power. The methods include logistic regression and @entity1274 , with standard and least absolute shrinkage and selection operator (LASSO) variable selection, as well as decision trees with and without boosting. These methods were applied to the baseline data of a subgroup of subjects in a @entity303 study, using their screening interview items to predict their clinical diagnosis of normal or non-normal (cognitively impaired or demented). The derived models were then validated on a different subgroup of subjects in the same study who had the screening and clinical diagnosis two to five years later. Performance of different models was compared based on their sensitivity and specificity in the validation sample. Generally, the six statistical methods performed slightly to moderately better than the expert-opinion model. @entity1274 generally performed better than the logistic and decision tree models. LASSO improved the performance of logistic and neural network models, but it eliminated few input variables in the neural network. The single decision tree performed at least as well as the standard logistic model, and with fewer items, making it an attractive pre-screening tool. Using the boosting option for decision trees did not substantially improve the performance. We recommend that for each situation, different methods of classification should be attempted to obtain optimal results for a given purpose.
