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OBJECTIVE: This paper puts forward a proposal for a modelling approach to the estimation of long term cost savings from the treatment of Alzheimer's disease (AD). DESIGN: In the proposed modelling approach, disease progression is defined in terms of intervals in the Mini-Mental State Exam (MMSE) scale. Clinical trial data are then used to determine the time at which a particular patient moved into a more severe stage of the disease. By comparing these durations across treatment groups, survival analysis is used to measure the impact of treatment in delaying the onset of a more costly stage of the disease. SETTING: Patients with varying severity of AD. PATIENTS AND PARTICIPANTS: The model uses clinical trial data on 1333 patients recruited internationally in 2 studies from 67 centres. INTERVENTIONS: The aim of these clinical studies was to evaluate the safety and efficacy of 2 non-overlapping dose ranges of rivastigmine relative to placebo over a 26-week treatment period in patients with probable AD. MAIN OUTCOME MEASURES AND RESULTS: The results indicate that the average cost savings with high-dose rivastigmine at the end of the trial period are quite low (approximately 29 Pounds per patient; 1997 values), but by extrapolating to a projected lifetime of 3 years, they rise to approximately 1100 Pounds per patient. The largest long term cost savings from treatment are obtained from treating those in the mild category (i.e. MMSE > 20). However, if the time horizon over which savings are estimated is short (i.e. if life expectancy is below 2 years), more costs are saved by prioritising patients with moderate AD (i.e. MMSE between 20 and 11). CONCLUSIONS: The model is a possible approach for estimating cost savings with treatment of AD, given the lack of long term data on resource use and drug efficacy. Caution should be used when extrapolating the results beyond the original study parameters.

Original publication




Journal article



Publication Date





165 - 174


Alzheimer Disease, Clinical Trials as Topic, Cost Savings, Humans, Models, Economic