Using imputation to provide harmonized longitudinal measures of cognition across AIBL and ADNI

Shishegar, Rosita; Cox, Timothy; Rolls, David A.; Bourgeat, Pierrick; Doré, Vincent; Lamb, Fiona; Robertson, Joanne; Laws, Simon M.; Porter, Tenielle; Fripp, Jürgen; Tosun, Duygu; Maruff, Paul; Savage, Greg; Rowe, Christopher C.; Masters, Colin L.; Weiner, Michael W.; Villemagne, Victor L.; Burnham, Samantha

doi:10.1038/s41598-021-02827-6

Cited by 23 publications

(13 citation statements)

References 43 publications

(55 reference statements)

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“…Data heterogeneity is an inescapable aspect of large-scale, multinational studies, but previous experience shows that it can be addressed with appropriate harmonization. [136][137][138][139] The Consortium has additionally adopted flexibility of study designs, which will increase the richness of the datasets but further increase variability. The intent of the Consortium members is that by harmonizing data collection and measurements we will increase the likelihood of cross-comparisons and the interpretability of meta-analytic approaches.…”

Section: Discussionmentioning

confidence: 99%

Chronic neuropsychiatric sequelae of SARS‐CoV‐2: Protocol and methods from the Alzheimer's Association Global Consortium

Erausquin

Snyder

Brugha

et al. 2022

A&D Transl Res & Clin Interv

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Introduction: Coronavirus disease 2019 (COVID-19) has caused >3.5 million deaths worldwide and affected >160 million people. At least twice as many have been infected but remained asymptomatic or minimally symptomatic. COVID-19 includes central nervous system manifestations mediated by inflammation and cerebrovascular, anoxic, and/or viral neurotoxicity mechanisms. More than one third of patients with COVID-19 develop neurologic problems during the acute phase of the illness, including loss of sense of smell or taste, seizures, and stroke. Damage or functional changes to the brain may result in chronic sequelae. The risk of incident cognitive and neuropsychiatric complications appears independent from the severity of the original pulmonary illness. It behooves the scientific and medical community to attempt to understand the molecular and/or systemic factors linking COVID-19 to neurologic illness, both short and long term. Methods: This article describes what is known so far in terms of links among COVID-19, the brain, neurological symptoms, and Alzheimer's disease (AD) and related dementias. We focus on risk factors and possible molecular, inflammatory, and viral mechanisms underlying neurological injury. We also provide a comprehensive description of the Alzheimer's Association Consortium on Chronic Neuropsychiatric Sequelae 4 of 24 DE ERAUSQUIN ET AL.of SARS-CoV-2 infection (CNS SC2) harmonized methodology to address these questions using a worldwide network of researchers and institutions.Results: Successful harmonization of designs and methods was achieved through a consensus process initially fragmented by specific interest groups (epidemiology, clinical assessments, cognitive evaluation, biomarkers, and neuroimaging). Conclusions from subcommittees were presented to the whole group and discussed extensively.Presently data collection is ongoing at 19 sites in 12 countries representing Asia, Africa, the Americas, and Europe.Discussion: The Alzheimer's Association Global Consortium harmonized methodology is proposed as a model to study long-term neurocognitive sequelae of SARS-CoV-2 infection.

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Section: Discussionmentioning

confidence: 99%

Chronic neuropsychiatric sequelae of SARS‐CoV‐2: Protocol and methods from the Alzheimer's Association Global Consortium

Erausquin

Snyder

Brugha

et al. 2022

A&D Transl Res & Clin Interv

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“…For example, the data suffer from a “temporal bias” [ 62 ] that is caused by the fact that baseline visits are not distributed uniformly over latent disease stages. These shortcomings require further investigations, likely demanding novel methodological approaches that can address the selection and temporal biases in the data and possibly exploiting other cohorts, as in [ 63 ].…”

Section: Discussionmentioning

confidence: 99%

Machine learning based multi-modal prediction of future decline toward Alzheimer’s disease: An empirical study

Karaman

Mormino²,

Sabuncu³

2022

PLoS ONE

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Alzheimer’s disease (AD) is a neurodegenerative condition that progresses over decades. Early detection of individuals at high risk of future progression toward AD is likely to be of critical significance for the successful treatment and/or prevention of this devastating disease. In this paper, we present an empirical study to characterize how predictable an individual subjects’ future AD trajectory is, several years in advance, based on rich multi-modal data, and using modern deep learning methods. Crucially, the machine learning strategy we propose can handle different future time horizons and can be trained with heterogeneous data that exhibit missingness and non-uniform follow-up visit times. Our experiments demonstrate that our strategy yields predictions that are more accurate than a model trained on a single time horizon (e.g. 3 years), which is common practice in prior literature. We also provide a comparison between linear and nonlinear models, verifying the well-established insight that the latter can offer a boost in performance. Our results also confirm that predicting future decline for cognitively normal (CN) individuals is more challenging than for individuals with mild cognitive impairment (MCI). Intriguingly, however, we discover that prediction accuracy decreases with increasing time horizon for CN subjects, but the trend is in the opposite direction for MCI subjects. Additionally, we quantify the contribution of different data types in prediction, which yields novel insights into the utility of different biomarkers. We find that molecular biomarkers are not as helpful for CN individuals as they are for MCI individuals, whereas magnetic resonance imaging biomarkers (hippocampus volume, specifically) offer a significant boost in prediction accuracy for CN individuals. Finally, we show how our model’s prediction reveals the evolution of individual-level progression risk over a five-year time horizon. Our code is available at https://github.com/batuhankmkaraman/mlbasedad.

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“…While future data collection efforts may consider the role datasets could play in clinical trial design, existing data harmonization-imputation methods could be used to impute absent metrics. 67 Our linear modeling approach may not fully capture the shape of cohort-and subject-level AD progression, which likely impacts power results. However, our approach is describing a bestcase scenario for a trial: the model equation that contains the treatment effect matches the equation used to generate the data.…”

Section: Contributions Limitations and Future Workmentioning

confidence: 99%

Simulation-based power analysis could improve the design of clinical trials in Alzheimer’s disease

Andrews

Arnold

Bzdok

et al. 2022

Preprint

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Clinical trials of new treatments in different progressive diseases use power analysis to determine the sample size needed for a trial to obtain a statistically significant estimate for an anticipated treatment effect. In trials with parallel designs, the standard power analysis approach is based on a two-sample t-test. For example, the standard t-test approach was used in determining the sample size for the Phase 3 trials of aducanumab, the first drug approved by the United States Food and Drug Administration (FDA) to potentially slow cognitive decline in early-stage Alzheimer's disease. However, t-tests contain normality assumptions, and t-test-based power analyses do not implicitly factor in the uncertainty about anticipated treatment effects that arises due to inter-subject heterogeneity in disease progression. These limitations may lead to recommended sample sizes that are too small, potentially making a trial blind to a treatment effect that is truly present if the cohort's endpoints are not normally distributed and/or the anticipated treatment effect is overestimated. To address these issues, we present a novel power analysis method that (1) simulates clinical trials in a progressive disease using real-world data, (2) accounts for inter-subject heterogeneity in disease progression, and (3) does not depend on normality assumptions. As a showcase example, we used our method to calculate power for a range of sample sizes and treatment effects in simulated trials similar to the Phase 3 aducanumab trials EMERGE and ENGAGE. As expected, our results show that power increases with number of subjects and treatment effect (here defined as the cohort-level percent reduction in the rate of cognitive decline in treated subjects vs. controls). However, inclusion of realistic inter-subject heterogeneity in cognitive decline trajectories leads to increased sample size recommendations compared to a standard t-test power analysis. These results suggest that the sample sizes recommended by the t-test power analyses in the EMERGE and ENGAGE Statistical Analysis Plans were possibly too small to ensure a high probability of detecting the anticipated treatment effect. Insufficient sample sizes could partly explain the statistically significant effect of aducanumab being detected only in EMERGE. We also used our method to analyze power in simulated trials similar the Phase 3 lecanemab trial Clarity AD. Our results suggest that Clarity AD was adequately powered, and that power may be influenced by a trial's number of analysis visits and the characteristics of subgroups within a cohort. By using our simulation-based power analysis approach, clinical trials of treatments in Alzheimer's disease and potentially in other progressive diseases could obtain sample size recommendations that account for heterogeneity in disease progression and uncertainty in anticipated treatment effects. Our approach avoids the limitations of t-tests and thus could help ensure that clinical trials are more adequately powered to detect the treatment effects they seek to measure.

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Using imputation to provide harmonized longitudinal measures of cognition across AIBL and ADNI

Cited by 23 publications

References 43 publications

Chronic neuropsychiatric sequelae of SARS‐CoV‐2: Protocol and methods from the Alzheimer's Association Global Consortium

Chronic neuropsychiatric sequelae of SARS‐CoV‐2: Protocol and methods from the Alzheimer's Association Global Consortium

Machine learning based multi-modal prediction of future decline toward Alzheimer’s disease: An empirical study

Simulation-based power analysis could improve the design of clinical trials in Alzheimer’s disease

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