Introduction Numerous studies have collected Alzheimer's disease (AD) cohort data sets. To achieve reproducible, robust results in data‐driven approaches, an evaluation of the present data landscape is vital. Methods Previous efforts relied exclusively on metadata and literature. Here, we evaluate the data landscape by directly investigating nine patient‐level data sets generated in major clinical cohort studies. Results The investigated cohorts differ in key characteristics, such as demographics and distributions of AD biomarkers. Analyzing the ethnoracial diversity revealed a strong bias toward White/Caucasian individuals. We described and compared the measured data modalities. Finally, the available longitudinal data for important AD biomarkers was evaluated. All results are explorable through our web application ADataViewer (https://adata.scai.fraunhofer.de). Discussion Our evaluation exposed critical limitations in the AD data landscape that impede comparative approaches across multiple data sets. Comparison of our results to those gained by metadata‐based approaches highlights that thorough investigation of real patient‐level data is imperative to assess a data landscape.

Background Currently, Alzheimer’s disease (AD) cohort datasets are difficult to find and lack across-cohort interoperability, and the actual content of publicly available datasets often only becomes clear to third-party researchers once data access has been granted. These aspects severely hinder the advancement of AD research through emerging data-driven approaches such as machine learning and artificial intelligence and bias current data-driven findings towards the few commonly used, well-explored AD cohorts. To achieve robust and generalizable results, validation across multiple datasets is crucial. Methods We accessed and systematically investigated the content of 20 major AD cohort datasets at the data level. Both, a medical professional and a data specialist, manually curated and semantically harmonized the acquired datasets. Finally, we developed a platform that displays vital information about the available datasets. Results Here, we present ADataViewer, an interactive platform that facilitates the exploration of 20 cohort datasets with respect to longitudinal follow-up, demographics, ethnoracial diversity, measured modalities, and statistical properties of individual variables. It allows researchers to quickly identify AD cohorts that meet user-specified requirements for discovery and validation studies regarding available variables, sample sizes, and longitudinal follow-up. Additionally, we publish the underlying variable mapping catalog that harmonizes 1196 unique variables across the 20 cohorts and paves the way for interoperable AD datasets. Conclusions In conclusion, ADataViewer facilitates fast, robust data-driven research by transparently displaying cohort dataset content and supporting researchers in selecting datasets that are suited for their envisioned study. The platform is available at https://adata.scai.fraunhofer.de/.

Introduction Given study‐specific inclusion and exclusion criteria, Alzheimer's disease (AD) cohort studies effectively sample from different statistical distributions. This heterogeneity can propagate into cohort‐specific signals and subsequently bias data‐driven investigations of disease progression patterns. Methods We built multi‐state models for six independent AD cohort datasets to statistically compare disease progression patterns across them. Additionally, we propose a novel method for clustering cohorts with regard to their progression signals. Results We identified significant differences in progression patterns across cohorts. Models trained on cohort data learned cohort‐specific effects that bias their estimations. We demonstrated how six cohorts relate to each other regarding their disease progression. Discussion Heterogeneity in cohort datasets impedes the reproducibility of data‐driven results and validation of progression models generated on single cohorts. To ensure robust scientific insights, it is advisable to externally validate results in independent cohort datasets. The proposed clustering assesses the comparability of cohorts in an unbiased, data‐driven manner.