ArticleJournal of Alzheimer's disease : JAD2022
Influence of Subject-Specific Effects in Longitudinal Modelling of Cognitive Decline in Alzheimer's Disease.
Article in Journal of Alzheimer's disease : JAD, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundAccurate longitudinal modelling of cognitive decline is a major goal of Alzheimer's disease and related dementia (ADRD) research. However, the impact of subject-specific effects is not well characterized and may have implications for data generation and prediction.
objectiveThis study seeks to address the impact of subject-specific effects, which are a less well-characterized aspect of ADRD cognitive decline, as measured by the Alzheimer's Disease Assessment Scale's Cognitive Subscale (ADAS-Cog).
methodsPrediction errors and biases for the ADAS-Cog subscale were evaluated when using only population-level effects, robust imputation of subject-specific effects using model covariances, and directly known individual-level effects fit during modelling as a natural control. Evaluated models included pre-specified parameterizations for clinical trial simulation, analogous mixed-effects regression models parameterized directly, and random forest ensemble models. Assessment used a meta-database of Alzheimer's disease studies with validation in simulated synthetic cohorts.
resultsAll models observed increases in variance under imputation leading to increased prediction error. Bias decreased with imputation except under the pre-specified parameterization, which increased in the meta-database, but was attenuated under simulation. Known fitted subject effects gave the best prediction results.
conclusionSubject-specific effects were found to have a profound impact on predicting ADAS-Cog. Reductions in bias suggest imputing random effects assists in calculating results on average, as when simulating clinical trials. However, reduction in error emphasizes population-level effects when attempting to predict outcomes for individuals. Forecasting future observations greatly benefits from using known subject-specific effects.
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