New Alzheimer’s Microsimulation Model Translates Slowed Cognitive Decline into Years of Independence

Researchers in the Netherlands have built a microsimulation model of Alzheimer’s disease that, for the first time, is based entirely on patients with biomarker-confirmed disease and explicitly links the slow erosion of cognition to the loss of functional independence. The model, developed from the Amsterdam Dementia Cohort and published in the European Journal of Epidemiology, was designed to answer a question that the current generation of amyloid-targeting therapies (ATTs) has left open: what do the modest cognitive benefits measured in eighteen-month clinical trials actually mean for the years a patient can live independently? When the team simulated a therapy that slows decline on the Mini-Mental State Examination (MMSE) by 30 percent during mild cognitive impairment (MCI) and mild dementia, the model predicted an extra 0.4 years in the MCI stage, an extra 1.0 year in mild dementia, and a delay of 1.1 years before a patient moves into a care home.

The need for such a tool stems from a gap between what trials measure and what patients, clinicians and payers care about. The randomised trials behind the first ATTs to reach the market reported slowing of decline in continuous cognitive and functional outcomes over less than two years of follow-up. Decisions about reimbursement, however, hinge on long-term effects and cost-effectiveness, which no trial of that length can deliver. Disease trajectory models fitted to real-world data can extend trial results into the future, and simulation studies can explore alternative treatment scenarios, such as treating all patients with amyloid-positive MCI and mild dementia versus only those with MCI, or assuming a persistent treatment effect versus one that wanes over time. The new model was built specifically to serve as a platform for these extrapolations and for cost-effectiveness analyses of current and future therapies.

Existing simulation models, the researchers argue, carry three important limitations for evaluating ATTs. First, none of the models they reviewed, identified through systematic reviews up to August 2022 plus seven later publications, used data exclusively from patients with biomarker-confirmed Alzheimer’s disease at the time of diagnosis, even though biomarker confirmation is the eligibility requirement for the new therapies. Because clinical differentiation of the neurodegenerative diseases causing dementia is uncertain, diagnoses based on symptoms alone involve substantial misclassification, and differing rates of cognitive decline across diseases can introduce spectrum bias into estimated treatment effects. Second, published models stitched together data from multiple sources with differing age, sex and severity profiles, undermining internal validity because transition probabilities derived from incompatible populations may not combine coherently. Third, most models represented the disease as a small set of discrete stages, implicitly assuming that cognitive and functional benefits from therapy are equivalent, even though cognition and functional independence are only moderately correlated.

To address these weaknesses, the team estimated all model parameters from a single target population: amyloid-positive patients seen at the Amsterdam Dementia Cohort memory clinic who would in principle be eligible for ATT. The dementia group comprised 757 of 777 eligible participants (97 percent) who could be successfully linked to national registries, with a mean age of 65 years and 55 percent female; the MCI group comprised 388 participants with a mean age of 66 years and 44 percent female. Every participant underwent a standardised one-day diagnostic work-up including medical history, neurological and neuropsychological testing, MRI and lumbar puncture, with diagnoses of MCI or dementia due to Alzheimer’s disease made in a multidisciplinary meeting according to core clinical NIA-AA criteria plus biomarker confirmation of amyloid pathology. Institutionalisation dates, available from 2015 to 2022 under the Dutch Long-Term Care Act, came from Statistics Netherlands, and dates of death came from municipal personal records.

The statistical core of the model consists of two joint models, which combine a longitudinal marker with a time-to-event analysis. The marker was the MMSE, modelled as a quadratic function of time using linear mixed models, and the modelled MMSE values entered the survival component as time-varying predictors, so that an individual’s cognitive trajectory directly modified the risk of transitioning to the next disease stage. The MCI-to-dementia joint model included baseline age, sex, cerebrospinal fluid Aβ1-42 and pTau, and MRI-rated medial temporal lobe atrophy as predictors of the rate of cognitive decline; the dementia-to-institutionalisation-or-death model included age and sex. Remaining transitions, including death within an institution, were described with Weibull hazard models. Missing biomarker and MRI data were handled with multiple imputation by chained equations. Death during the MCI stage was informed by Dutch age- and sex-specific life tables rather than a competing risk model, because the median interval between loss to follow-up and death among MCI participants without a registered dementia diagnosis was 7.9 years, suggesting most had transitioned to dementia at an unknown earlier timepoint.

The joint models revealed how tightly cognition governs clinical progression. Each one-point lower MMSE score was associated with a hazard ratio of 1.28 (95 percent confidence interval 1.18 to 1.40) for conversion from MCI to dementia. In the dementia stage, each point lower MMSE carried a hazard ratio of 1.19 (95 percent CI 1.07 to 1.32) for institutionalisation and 1.12 (95 percent CI 1.06 to 1.19) for death outside an institution. Cerebrospinal fluid pTau and medial temporal atrophy significantly predicted the rate of MMSE decline in the MCI model, while higher age was associated with a slower rate of decline in the dementia model. Validation showed that observed and predicted cumulative incidence curves overlapped for all stage transitions, indicating the statistical models captured the time spent in each stage accurately.

Simulating the natural disease course for 1,000 patients starting with MCI, the model produced a mean of 3.3 years in the MCI stage, during which 57 of the simulated patients died before converting to dementia. The community dementia stage lasted a mean of 5.0 years, which the researchers subdivided post hoc by MMSE thresholds into 2.3 years of mild dementia (MMSE above 20), 1.6 years of moderate dementia (MMSE 20 to 10) and 1.1 years of severe dementia (MMSE below 10). Within the dementia stage, 256 simulated patients died without ever being institutionalised, while 686 entered a care home before death. Institutional life lasted a mean of 1.7 years, and overall mean survival from MCI to death was 10.0 years.

Applying the ATT effect changed that trajectory in a pattern the authors consider clinically meaningful. With MMSE decline slowed by 30 percent during MCI and mild dementia, mean time in the MCI stage increased by 0.35 years and time in mild dementia increased by 0.99 years, while time with moderate dementia decreased by 0.29 years and time with severe dementia and in an institution remained approximately unchanged. Overall survival from MCI diagnosis to death rose by 0.97 years, meaning patients spent roughly sixteen additional months in what the researchers describe as reasonably good cognitive health. Age mattered considerably: total survival fell from 10.8 years for a cohort starting at age 60 to 8.0 years at age 80, and the gains in MCI and mild dementia shrank from 0.41 and 1.07 years at age 60 to 0.17 and 0.72 years at age 80, largely because older patients spend less time in moderate and severe community dementia and in institutions.

The predicted stage durations differ from most existing Alzheimer’s simulation models, including those in the recent IPECAD model-comparison study, with a slightly shorter MCI phase and a longer dementia phase. The authors attribute this partly to their data source: a relatively young, amyloid-positive tertiary memory clinic population, whereas other models drew on older cohorts with higher mortality risk, such as NACC, or self-referral cohorts with more worried-well participants, such as ADNI. The amyloid-positive selection may explain the shorter MCI duration, while the younger cohort’s low mortality risk allows more time with dementia and a longer window over which treatment benefits can accumulate. The modest effect on MCI duration also reflects the model’s assumption, drawn from the trials, that ATTs slow the rate of cognitive decline rather than raising the absolute cognitive score, so benefits take time to accumulate before translating into preserved independence. Models assuming an instantaneous 30 percent reduction in transition risk showed larger MCI gains.

The researchers highlight four strengths: exclusive use of biomarker-confirmed diagnoses at baseline, estimation of nearly all transitions from a single cohort for high internal validity, inclusion of gradual cognitive change during the MCI stage so that future studies can test different treatment start and stop thresholds, and Dutch estimates of institutional length of stay, which no cost-effectiveness analysis of the past decade based on European data despite institutionalisation being a major cost and quality-of-life driver. Limitations remain. The MMSE does not capture all cognitive domains and has limited sensitivity in early disease, though the authors note that cognition in general is only moderately correlated with function, so a different measure would be unlikely to change the results. The memory clinic population is younger than the average person with incident dementia, restricting generalisability beyond memory clinics, participants were not prospectively selected against the appropriate-use criteria for anti-amyloid therapies, and the uniform 30 percent slowing was extrapolated beyond the trials’ eighteen-month follow-up, leaving uncertainty about waning effects and heterogeneity by age and sex. The team plans to use the model for cost-effectiveness analyses of the new therapies under varying assumptions about treatment duration and persistence.

Subject of Research: Microsimulation modelling of Alzheimer's disease trajectories to evaluate amyloid-targeting therapies

Article Title: Developing a microsimulation model of Alzheimer’s disease for the evaluation of disease modifying therapies

Article References: van der Veere, P. J., Broulíková, H. M., Hoogland, J., Handels, R., Vijverberg, E. G. B., van Harten, A. C., Teunissen, C. E., Barkhof, F., van de Giessen, E., van der Flier, W. M., & Berkhof, J. (2026). Developing a microsimulation model of Alzheimer’s disease for the evaluation of disease modifying therapies. European Journal of Epidemiology. https://doi.org/10.1007/s10654-026-01470-7

Image Credits: AI Generated

DOI: 10.1007/s10654-026-01470-7

Keywords: Alzheimer's disease, microsimulation, amyloid-targeting therapies, disease modelling, cognitive decline, functional independence, institutionalisation, joint models, MMSE, cost-effectiveness, biomarkers, memory clinic