{"doi":"10.3233/jad-215553","title":"Influence of Subject-Specific Effects in Longitudinal Modelling of Cognitive Decline in Alzheimer’s Disease","abstract":"BACKGROUND: Accurate 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. OBJECTIVE: This 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). METHODS: Prediction 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. RESULTS: All 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. CONCLUSION: Subject-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.","journal":"Journal of Alzheimer s Disease","year":2022,"id":306366,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9526,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":329407,"name":"Byron C. Jaeger","orcid":"0000-0001-7399-2299","position":1,"is_corresponding":false},{"id":388813,"name":"Jeff M. Szychowski","orcid":"0000-0002-1727-1662","position":2,"is_corresponding":false},{"id":1000812,"name":"Gary R. Cutter","orcid":null,"position":3,"is_corresponding":false},{"id":246148,"name":"Erik D. Roberson","orcid":"0000-0002-1810-9763","position":4,"is_corresponding":false},{"id":1000813,"name":"Richard E. Kennedy","orcid":null,"position":5,"is_corresponding":false},{"id":1000811,"name":"Charles F. Murchison","orcid":null,"position":0,"is_corresponding":true}],"reference_count":44,"raw_metadata":null,"created_at":"2026-07-19T00:32:48.984902Z","pmid":"35342087","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}