scholarly journals Recruitment and Retention of Participant and Study Partner Dyads in Two Multinational Alzheimer’s Disease Registration Trials

2020 ◽  
Author(s):  
Olivia M Bernstein ◽  
Joshua D. Grill ◽  
Daniel L. Gillen
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Random Forest ◽  
Random Forest Model ◽  
P Value ◽  
Confounding Factors ◽  
Forest Model ◽  
Partner Type ◽  
Study Partner ◽  
The Relationship

Abstract Background: Early study exit is detrimental to statistical power and increases the risk for bias in Alzheimer’s disease clinical trials. Previous analyses in early phase academic trials demonstrated associations between rates of trial incompletion and participants’ study partner type, with participants enrolling with non-spouse study partners being at greater risk.Methods: We conducted secondary analyses of two multinational phase III trials of semagacestat, an oral gamma secretase inhibitor, for mild-to-moderate AD dementia. Cox’s proportional hazards regression model was used to estimate the relationship between study partner type and the risk of early exit from the trial after adjustment for a priori identified potential confounding factors. Additionally, we used a random forest model to identify top predictors of dropout.Results: Among participants with spousal, adult child, and other study partners, respectively, 35%, 38%, and 36% dropped out or died prior to protocol-defined study completion, respectively. In unadjusted models, the risk of trial incompletion differed by study partner type (unadjusted p-value=0.027 for test of differences by partner type), but in models adjusting for potential confounding factors the differences were not statistically significant (p-value=0.928). In exploratory modeling, participant age was identified as the primary characteristic to explain the relationship between study partner type and the risk of failing to complete the trial. Participant age was also the strongest predictor of trial incompletion in the random forest model.Conclusions: After adjustment for age, no qualitative differences in the risk of incompletion were observed when comparing participants with different study partner types in these trials. Differences between our findings and the findings of previous studies may be explained by differences in trial phase, size, geographic regions, or the composition of academic and non-academic sites.

scholarly journals Recruitment and retention of participant and study partner dyads in two multinational Alzheimer’s disease registration trials

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Olivia M. Bernstein ◽  
Joshua D. Grill ◽  
Daniel L. Gillen
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Random Forest ◽  
Random Forest Model ◽  
Phase Iii ◽  
Confounding Factors ◽  
Forest Model ◽  
Partner Type ◽  
Study Partner ◽  
The Relationship

Abstract Background Early study exit is detrimental to statistical power and increases the risk for bias in Alzheimer’s disease clinical trials. Previous analyses in early phase academic trials demonstrated associations between rates of trial incompletion and participants’ study partner type, with participants enrolling with non-spouse study partners being at greater risk. Methods We conducted secondary analyses of two multinational phase III trials of semagacestat, an oral gamma secretase inhibitor, for mild-to-moderate AD dementia. Cox’s proportional hazards regression model was used to estimate the relationship between study partner type and the risk of early exit from the trial after adjustment for a priori identified potential confounding factors. Additionally, we used a random forest model to identify top predictors of dropout. Results Among participants with spousal, adult child, and other study partners, respectively, 35%, 38%, and 36% dropped out or died prior to protocol-defined study completion, respectively. In unadjusted models, the risk of trial incompletion differed by study partner type (unadjusted p value = 0.027 for test of differences by partner type), but in models adjusting for potential confounding factors, the differences were not statistically significant (p value = 0.928). In exploratory modeling, participant age was identified as the primary characteristic to explain the relationship between study partner type and the risk of failing to complete the trial. Participant age was also the strongest predictor of trial incompletion in the random forest model. Conclusions After adjustment for age, no differences in the risk of incompletion were observed when comparing participants with different study partner types in these trials. Differences between our findings and the findings of previous studies may be explained by differences in trial phase, size, geographic regions, or the composition of academic and non-academic sites.

scholarly journals Random forest model for feature-based Alzheimer’s disease conversion prediction from early mild cognitive impairment subjects

2020 ◽  
Author(s):  
Matthew Velazquez ◽  
Yugyung Lee ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Random Forest ◽  
Clinical Features ◽  
Random Forest Model ◽  
Clinical Feature ◽  
Forest Model ◽  
Early Mild Cognitive Impairment

AbstractAlzheimer’s Disease (AD) conversion prediction from the mild cognitive impairment (MCI) stage has been a difficult challenge. This study focuses on providing an individualized MCI to AD conversion prediction using a balanced random forest model that leverages clinical data. In order to do this, 383 Early Mild Cognitive Impairment (EMCI) patients were gathered from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Of these patients, 49 would eventually convert to AD (EMCI_C), whereas the remaining 335 did not convert (EMCI_NC). All of these patients were split into training and testing data sets with 95 patients reserved for testing. Nine clinical features were selected, comprised of a mix of demographic, brain volume, and cognitive testing variables. Oversampling was then performed in order to balance the initially imbalanced classes. Our results showed that a random forest model was effective (93.6% accuracy) at predicting the conversion of EMCI patients to AD based on these clinical features. Additionally, we assessed the importance of each clinical feature at both the individual and model level for interpretation of the prediction itself. Our model could impact the clinical environment as a tool to predict the conversion to AD from a prodromal stage or to identify ideal candidates for clinical trials.

scholarly journals Random forest model for feature-based Alzheimer’s disease conversion prediction from early mild cognitive impairment subjects

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0244773
Author(s):  
Matthew Velazquez ◽  
Yugyung Lee ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Random Forest ◽  
Clinical Features ◽  
Random Forest Model ◽  
Clinical Feature ◽  
Forest Model ◽  
Early Mild Cognitive Impairment

Alzheimer’s Disease (AD) conversion prediction from the mild cognitive impairment (MCI) stage has been a difficult challenge. This study focuses on providing an individualized MCI to AD conversion prediction using a balanced random forest model that leverages clinical data. In order to do this, 383 Early Mild Cognitive Impairment (EMCI) patients were gathered from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Of these patients, 49 would eventually convert to AD (EMCI_C), whereas the remaining 334 did not convert (EMCI_NC). All of these patients were split randomly into training and testing data sets with 95 patients reserved for testing. Nine clinical features were selected, comprised of a mix of demographic, brain volume, and cognitive testing variables. Oversampling was then performed in order to balance the initially imbalanced classes prior to training the model with 1000 estimators. Our results showed that a random forest model was effective (93.6% accuracy) at predicting the conversion of EMCI patients to AD based on these clinical features. Additionally, we focus on explainability by assessing the importance of each clinical feature. Our model could impact the clinical environment as a tool to predict the conversion to AD from a prodromal stage or to identify ideal candidates for clinical trials.

scholarly journals Does Study Partner Type Impact the Rate of Alzheimer's Disease Progression?

2013 ◽  
Vol 38 (3) ◽  
pp. 507-514 ◽  
Author(s):  
Joshua D. Grill ◽  
Yan Zhou ◽  
Jason Karlawish ◽  
David Elashoff
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Partner Type ◽  
Study Partner

scholarly journals Participant and study partner prediction and identification of cognitive impairment in preclinical Alzheimer’s disease: study partner vs. participant accuracy

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Mary M. Ryan ◽  
◽  
Joshua D. Grill ◽  
Daniel L. Gillen
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Random Forest ◽  
Cognitive Change ◽  
Disease Assessment ◽  
Preclinical Alzheimer’S Disease ◽  
Baseline Study ◽  
Forest Models ◽  
Random Forest Models ◽  
Study Partner

Abstract Background Preclinical Alzheimer’s disease (AD) clinical trials require participants to enroll with a study partner, a person who can attend visits and report changes in the participant’s cognitive ability. Whether study partners, compared to participants themselves, provide added information about participant cognition in preclinical AD trials is an open question. We tested the hypothesis that study partners provide meaningful information related to participant cognition cross-sectionally and longitudinally, and assessed whether amyloid status modified observed effects. Methods We assessed participant and study partner Everyday Cognition (ECog) scores and participant Alzheimer’s Disease Assessment Scale 13-item cognitive subscale (ADAS13) data from 335 cognitively normal participant-partner dyads in the AD Neuroimaging Initiative. We used random forest and linear mixed effects (LME) models to predict ADAS13 scores as a function of participant and/or study partner ECog scores over time. LME models were adjusted for potential confounding factors, including APOE4 status, amyloid status, baseline age, years of education, and sex. Random forest models were split into the above factors, as well as race/ethnicity and other available neuropsychological battery test scores. Results In random forest models predicting ADAS13 12 months from baseline, we observed no difference in the estimated mean variable importance (eMVI) associated with baseline study partner ECog compared to the baseline participant ECog (eMVI = 0.15, 95%CB 0.13, 0.16 for partner; eMVI = 0.15, 95%CB 0.14, 0.16 for participant). In models predicting ADAS13 48 months after baseline, the eMVI associated with baseline study partner ECog was slightly lower than that associated with baseline participant ECog (eMVI = 0.21, 95%CB 0.20, 0.22 for partner; eMVI = 0.24, 95%CB 0.22, 0.25 for participant). In cross-sectional models, study partner eMVI was twice as large as participant eMVI at 12 months (eMVI = 0.20, 95%CB 0.19, 0.21 for partner; eMVI = 0.09, 95%CB 0.09, 0.10 for participant) and three times as large at 48 months (eMVI = 0.38, 95%CB 0.36, 0.39 for partner; eMVI = 0.13, 95%CB 0.12, 0.14 for participant). We did not observe qualitative differences by amyloid status. Conclusions While baseline participant reports reasonably predict subsequent cognitive change, informants perform better at cross-sectionally recognizing cognitive status as observation time grows. The study partner requirement may be essential to ensure trial data integrity, especially in longer trials.

Investigating the relationship between train speed and ground vibrations using random forest machine learning models

2021 ◽  
Vol 263 (3) ◽  
pp. 3595-3606
Author(s):  
F.L.H. Klein Schaarsberg ◽  
A.C. de Niet ◽  
H. Zandberg ◽  
Gerrit Jan Dijkgraaf
Keyword(s):  
Random Forest ◽  
Regression Models ◽  
Random Forest Model ◽  
Mean Deviation ◽  
Ground Vibrations ◽  
Effective Measure ◽  
Forest Model ◽  
Random Decision Forest ◽  
Train Speed ◽  
The Relationship

In the Netherlands, concerned citizens have proposed reducing train speed as an effective measure to mitigate annoyance caused by railway-induced vibrations. In the present study the relationship between train speed and other influencing parameters (e.g. axle load, wheel roughness), and ground vibrations was investigated using measurements, at different locations, of ground vibrations caused by the passage of regular freight trains and a test train at different speeds. Measurements have been analysed using multivariate regression models and a random decision forest model. The prevailing uncertainties have also been measured using normalized mean deviation between the model predicted value and the actual value. A comparison of results demonstrates that a 'trained and tested' random forest model has certain predictive advantages: i) mean deviation between predicted and actual value is found to be the lowest with random forest model; ii) the random forest model considers all available parameters in the dataset, thus simulating the real situation more closely. However, the model is very location-specific and must therefore be used with caution. In general it is observed that a decrease in train speed results in the reduction of measured vibration levels.

scholarly journals Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Yuantian Sun ◽  
Guichen Li ◽  
Junfei Zhang ◽  
Deyu Qian
Keyword(s):  
Random Forest ◽  
Coarse Aggregate ◽  
Construction Material ◽  
Random Forest Model ◽  
Significant Variable ◽  
Rubberized Concrete ◽  
Forest Model ◽  
Environmental Friendly ◽  
Key Variables ◽  
The Relationship

Rubberized concrete (RC) has attracted more attention these years as it is an economical and environmental-friendly construction material. Normally, the uniaxial compressive strength (UCS) of RC needs to be evaluated before application. In this study, an evolutionary random forest model (BRF) combining random forest (RF) and beetle antennae search (BAS) algorithms was proposed, which can be used for establishing the relationship between UCS of RC and its key variables. A total number of 138 cases were collected from the literature to develop and validate the BRF model. The results showed that the BAS can tune the RF effectively, and therefore, the hyperparameters of RF were obtained. The proposed BRF model can accurately predict the UCS of RC with a high correlation coefficient (0.96). Furthermore, the variable importance was determined, and the results showed that the age of RC is the most significant variable, followed by water-cement ratio, fine rubber aggregate, coarse rubber aggregate, and coarse aggregate. This study provides a new method to access the strength of RC and can efficiently guide the design of RC in practice.

MiR-335-5p Inhibits β-Amyloid (Aβ) Accumulation to Attenuate Cognitive Deficits Through Targeting c-jun-N-terminal Kinase 3 in Alzheimer’s Disease

2020 ◽  
Vol 17 (1) ◽  
pp. 93-101 ◽  
Author(s):  
Dan Wang ◽  
Zhifu Fei ◽  
Song Luo ◽  
Hai Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transgenic Mice ◽  
Western Blot ◽  
Mrna Expression ◽  
Cognitive Deficits ◽  
Protein Levels ◽  
Amyloid Aβ ◽  
Β Amyloid ◽  
The Relationship

Objectives: Alzheimer's disease (AD), also known as senile dementia, is a common neurodegenerative disease characterized by progressive cognitive impairment and personality changes. Numerous evidences have suggested that microRNAs (miRNAs) are involved in the pathogenesis and development of AD. However, the exact role of miR-335-5p in the progression of AD is still not clearly clarified. Methods: The protein and mRNA levels were measured by western blot and RNA extraction and quantitative real-time PCR (qRT-PCR), respectively. The relationship between miR-335-5p and c-jun-N-terminal kinase 3 (JNK3) was confirmed by dual-luciferase reporter assay. SH-SY5Y cells were transfected with APP mutant gene to establish the in vitro AD cell model. Flow cytometry and western blot were performed to evaluate cell apoptosis. The APP/PS1 transgenic mice were used as an in vivo AD model. Morris water maze test was performed to assess the effect of miR- 335-5p on the cognitive deficits in APP/PS1 transgenic mice. Results: The JNK3 mRNA expression and protein levels of JNK3 and β-Amyloid (Aβ) were significantly up-regulated, and the mRNA expression of miR-335-5p was down-regulated in the brain tissues of AD patients. The expression levels of miR-335-5p and JNK3 were significantly inversely correlated. Further, the dual Luciferase assay verified the relationship between miR-335- 5p and JNK3. Overexpression of miR-335-5p significantly decreased the protein levels of JNK3 and Aβ and inhibited apoptosis in SH-SY5Y/APPswe cells, whereas the inhibition of miR-335-5p obtained the opposite results. Moreover, the overexpression of miR-335-5p remarkably improved the cognitive abilities of APP/PS1 mice. Conclusion: The results revealed that the increased JNK3 expression, negatively regulated by miR-335-5p, may be a potential mechanism that contributes to Aβ accumulation and AD progression, indicating a novel approach for AD treatment.

A Biomarker Combining Imaging and Neuropsychological Assessment for Tracking Early Alzheimer's Disease in Clinical Trials

2018 ◽  
Vol 15 (5) ◽  
pp. 429-442 ◽  
Author(s):  
Nishant Verma ◽  
S. Natasha Beretvas ◽  
Belen Pascual ◽  
Joseph C. Masdeu ◽  
Mia K. Markey ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Cognitive Impairment ◽  
Latent Variable ◽  
Brain Regions ◽  
Disease Assessment ◽  
Latent Variable Analysis ◽  
The Relationship ◽  
Selection Of

Background: Combining optimized cognitive (Alzheimer's Disease Assessment Scale- Cognitive subscale, ADAS-Cog) and atrophy markers of Alzheimer's disease for tracking progression in clinical trials may provide greater sensitivity than currently used methods, which have yielded negative results in multiple recent trials. Furthermore, it is critical to clarify the relationship among the subcomponents yielded by cognitive and imaging testing, to address the symptomatic and anatomical variability of Alzheimer's disease. Method: Using latent variable analysis, we thoroughly investigated the relationship between cognitive impairment, as assessed on the ADAS-Cog, and cerebral atrophy. A biomarker was developed for Alzheimer's clinical trials that combines cognitive and atrophy markers. Results: Atrophy within specific brain regions was found to be closely related with impairment in cognitive domains of memory, language, and praxis. The proposed biomarker showed significantly better sensitivity in tracking progression of cognitive impairment than the ADAS-Cog in simulated trials and a real world problem. The biomarker also improved the selection of MCI patients (78.8±4.9% specificity at 80% sensitivity) that will evolve to Alzheimer's disease for clinical trials. Conclusion: The proposed biomarker provides a boost to the efficacy of clinical trials focused in the mild cognitive impairment (MCI) stage by significantly improving the sensitivity to detect treatment effects and improving the selection of MCI patients that will evolve to Alzheimer’s disease.

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