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Objective: To investigate the costs to society of Alzheimer disease (AD) care in a multinational, randomized, placebo-controlled trial of donepezil in patients with moderate to severe AD. Methods: A total of 290 patients with AD (screening standardized Mini-Mental State Examination score 5 to17) were randomized to receive either donepezil (n = 144; 5 mg/day for 28 days, followed by 10 mg/day as per clinician’s judgment) or placebo (n = 146) for 24 weeks. The authors collected data on patient and caregiver health resource utilization prospectively using the Canadian Utilization of Services Tracking questionnaire. Costs were calculated for patients and caregivers in each group based on resource utilization multiplied by the unit prices for each resource. A cost (the average Ontario minimum wage for 1998 [Can $6.85 per hour]) was assigned to unpaid time that caregivers spent assisting the patient with activities of daily living (ADL). Results: Patient and caregiver demographics at baseline were similar across the two groups. After adjusting for baseline total cost per patient, the mean total societal cost per patient for the 24-week period was donepezil, Can $9,904 (US $6,686) and placebo, Can $10,236 (US $6,910). This net cost saving of Can $332 (US $224) included the average 24-week cost of donepezil treatment. Most of the cost-saving with donepezil treatment was due to less use of residential care by patients, and caregivers spending less time assisting patients with ADL. Conclusion: This cost-consequence analysis reveals economic benefits of treatment of moderate to severe AD with donepezil.

Increasingly, clinical research is evaluated on the quality of its statistical analysis. Traditionally, statistical analyses in clinical research have been carried out from a ‘frequentist’ perspective. The presence of an alternative paradigm ‐ the Bayesian paradigm ‐ has been relatively unknown in clinical research until recently. There is currently a growing interest in the use of Bayesian statistics in health care research. This is due both to a growing realization of the limitations of frequentist methods and to the ability of Bayesian methods explicitly to incorporate prior expert knowledge and belief into the analyses. This is in contrast to frequentist methods, where prior experience and beliefs tend to be incorporated into the analyses in an ad hoc fashion. This paper outlines the frequentist and Bayesian paradigms. Acute myocardial infarction mortality data are then analysed from both a Bayesian and a frequentist perspective. In some analyses, the two methods are seen to produce comparable results; in others, they produce different results. It is noted that in this example, there are clinically relevant questions that are more easily addressed from a Bayesian perspective. Finally, areas in clinical research where Bayesian ideas are increasingly common are highlighted.