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.