Use of artificial networks in clinical trials: a pilot study to predict responsiveness to donepezil in Alzheimer's disease

Objectives: To evaluate the accuracy of artificial neural networks compared with discriminant analysis in classifying positive and negative response to the cholinesterase inhibitor donepezil in a group of Alzheimer's disease (AD) patients. Design: Convenience sample. Setting: Patients with mild to moderate AD consecutively admitted to a geriatric day hospital and treated with donepezil 5 mg/day. Participants: Sixty-one older patients of both sexes with AD. Measurements: Accuracy in detecting subjects sensitive (responders) or not (nonresponders) to 3-month therapy with ANNs. The criterion standard for evaluation of efficacy was the scores of Alzheimer's Disease Assessment Scale-Cognitive portion and Clinician's Interview Based Impression of Change-plus scales. Results: ANNs were more effective in discriminating between responders and nonresponders than other advanced statistical methods, particularly linear discriminant analysis. The total accuracy in predicting the outcome was 92.59%. Conclusions: ANNs appear to be a useful tool in detecting patient responsiveness to pharmacological treatment in AD.