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Background: Alzheimer's disease and related dementia (ADRD) are characterized by multiple and progressive anatomo-clinical changes including accumulation of abnormal proteins in the brain, brain atrophy and severe cognitive impairment. Understanding the sequence and timing of these changes is of primary importance to gain insight into the disease natural history and ultimately allow earlier diagnosis. Yet, modeling changes over disease course from cohort data is challenging as the usual timescales (time since inclusion, chronological age) are inappropriate and time-to-clinical diagnosis is available on small subsamples of participants with short follow-up durations prior to diagnosis. One solution to circumvent this challenge is to define the disease time as a latent variable. Methods: We developed a multivariate mixed model approach that realigns individual trajectories into the latent disease time to describe disease progression. In contrast with the existing literature, our methodology exploits the clinical diagnosis information as a partially observed and approximate reference to guide the estimation of the latent disease time. The model estimation was carried out in the Bayesian Framework using Stan. We applied the methodology to the MEMENTO study, a French multicentric clinic-based cohort of 2186 participants with 5-year intensive follow-up. Repeated measures of 12 ADRD markers stemmed from cerebrospinal fluid (CSF), brain imaging and cognitive tests were analyzed. Results: The estimated latent disease time spanned over twenty years before the clinical diagnosis. Considering the profile of a woman aged 70 with a high level of education and APOE4 carrier (the main genetic risk factor for ADRD), CSF markers of tau proteins accumulation preceded markers of brain atrophy by 5 years and cognitive decline by 10 years. However we observed that individual characteristics could substantially modify the sequence and timing of these changes, in particular for CSF level of A β 42 . Conclusion: By leveraging the available clinical diagnosis timing information, our disease progression model does not only realign trajectories into the most homogeneous way. It accounts for the inherent residual inter-individual variability in dementia progression to describe the long-term anatomo-clinical degradations according to the years preceding clinical diagnosis, and to provide clinically meaningful information on the sequence of events. Trial registration: clinicaltrials.gov, NCT01926249. Registered on 16 August 2013. [ABSTRACT FROM AUTHOR]

Mixed models for repeated measures (MMRMs) are ubiquitous when analyzing outcomes of clinical trials. However, the linearity of the fixed‐effect structure in these models largely restrict their use to estimating treatment effects that are defined as linear combinations of effects on the outcome scale. In some situations, alternative quantifications of treatment effects may be more appropriate. In progressive diseases, for example, one may want to estimate if a drug has cumulative effects resulting in increasing efficacy over time or whether it slows the time progression of disease. This article introduces a class of nonlinear mixed‐effects models called progression models for repeated measures (PMRMs) that, based on a continuous‐time extension of the categorical‐time parametrization of MMRMs, enables estimation of novel types of treatment effects, including measures of slowing or delay of the time progression of disease. Compared to conventional estimates of treatment effects where the unit matches that of the outcome scale (eg, 2 points benefit on a cognitive scale), the time‐based treatment effects can offer better interpretability and clinical meaningfulness (eg, 6 months delay in progression of cognitive decline). The PMRM class includes conventionally used MMRMs and related models for longitudinal data analysis, as well as variants of previously proposed disease progression models as special cases. The potential of the PMRM framework is illustrated using both simulated and historical data from clinical trials in Alzheimer's disease with different types of artificially simulated treatment effects. Compared to conventional models it is shown that PMRMs can offer substantially increased power to detect disease‐modifying treatment effects where the benefit is increasing with treatment duration. [ABSTRACT FROM AUTHOR]

The prediction of Alzheimer's disease (AD) progression plays a very important role in the early intervention of patients and the improvement of life quality. Cognitive scales are commonly used to assess the patient's status. However, due to the complicated pathogenesis of AD and the individual differences in AD, the prediction of AD progression is challenging. This paper proposes a novel coupling model (P-E model) that takes into account the processes of physiological degradation and emotional state transition of AD patients. We conduct experiments on synthetic data to validate the effectiveness of the proposed P-E model. Next, we conduct experiments on 134 subjects with more than 10 follow-ups from the Alzheimer's Disease Neuroimaging Initiative. The prediction performance of the P-E model is significantly better than other state-of-the-art methods, which achieves the mean squared error of 7.137 ± 0.035. The experimental results show that the P-E model can well characterize the non-monotonic properties of AD cognitive data and can also have a good predictive ability for time series data with individual differences. [ABSTRACT FROM AUTHOR]

Mucopolysaccaridosis IIIA (MPS IIIA) is a rare genetic disease that afflicts children and leads to neurocognitive degeneration. We develop a Bayesian disease progression model (DPM) of MPS IIIA that characterizes the pattern of cognitive growth and decline in this disease. The DPM is a repeated measures model that incorporates a nonlinear developmental trajectory and shape‐invariant random effects. This approach quantifies the pattern of cognitive development in MPS IIIA and addresses differences in biological age, length of follow‐up, and clinical outcomes across natural history subjects. The DPM can be used in clinical trials to estimate the percent slowing in disease progression for treatment relative to natural history. Simulations demonstrate that the DPM provides substantial improvements in power relative to alternative analyses. [ABSTRACT FROM AUTHOR]

The use of Disease progression models (DPMs) in Drug Development has been widely adopted across therapeutic areas as a method for integrating previously obtained disease knowledge to elucidate the impact of novel therapeutics or vaccines on disease course, thus quantifying the potential clinical benefit at different stages of drug development programs. This paper provides a brief overview of DPMs and the evolution in data types, analytic methods, and applications that have occurred in their use by Quantitive Clinical Pharmacologists. It also provides examples of how these models have informed decisions and clinical trial design across several therapeutic areas and at various stages of development. It briefly describes potential new applications of DPMs utilizing emerging data sources, and utilizing new analytic techniques, and discuss new challenges faced such as requiring description of multiple endpoints, rapid model development, application of machine learning-based analytics, and use of high dimensional and real-world data. Considerations for the continued evolution future of DPMs to serve as community-maintained expert systems are also provided. [ABSTRACT FROM AUTHOR]

Background: People live a long time in pre-diabetes/early diabetes without a formal diagnosis or management. Heterogeneity of progression coupled with deficiencies in electronic health records related to incomplete data, discrete events, and irregular event intervals make identification of pre-diabetes and critical points of diabetes progression challenging.Methods: We utilized longitudinal electronic health records of 9298 patients with type 2 diabetes or prediabetes from 2005 to 2016 from a large regional healthcare delivery network in China. We optimized a generative Markov-Bayesian-based model to generate 5000 synthetic illness trajectories. The synthetic data were manually reviewed by endocrinologists.Results: We build an optimized generative progression model for type 2 diabetes using anchor information to reduce the number of parameters learning in the third layer of the model from [Formula: see text] to [Formula: see text], where [Formula: see text] is the number of clinical findings, [Formula: see text] is the number of complications, [Formula: see text] is the number of anchors. Based on this model, we infer the relationships between progression stages, the onset of complication categories, and the associated diagnoses during the whole progression of type 2 diabetes using electronic health records.Discussion: Our findings indicate that 55.3% of single complications and 31.8% of complication patterns could be predicted early and managed appropriately to potentially delay (as it is a progressive disease) or prevented (by lifestyle modifications that keep patient from developing/triggering diabetes in the first place).Conclusions: The full type 2 diabetes patient trajectories generated by the chronic disease progression model can counter a lack of real-world evidence of desired longitudinal timeframe while facilitating population health management. [ABSTRACT FROM AUTHOR]

Several CSF and blood biomarkers for genetic frontotemporal dementia have been proposed, including those reflecting neuroaxonal loss (neurofilament light chain and phosphorylated neurofilament heavy chain), synapse dysfunction [neuronal pentraxin 2 (NPTX2)], astrogliosis (glial fibrillary acidic protein) and complement activation (C1q, C3b). Determining the sequence in which biomarkers become abnormal over the course of disease could facilitate disease staging and help identify mutation carriers with prodromal or early-stage frontotemporal dementia, which is especially important as pharmaceutical trials emerge. We aimed to model the sequence of biomarker abnormalities in presymptomatic and symptomatic genetic frontotemporal dementia using cross-sectional data from the Genetic Frontotemporal dementia Initiative (GENFI), a longitudinal cohort study. Two-hundred and seventy-five presymptomatic and 127 symptomatic carriers of mutations in GRN, C9orf72 or MAPT, as well as 247 non-carriers, were selected from the GENFI cohort based on availability of one or more of the aforementioned biomarkers. Nine presymptomatic carriers developed symptoms within 18 months of sample collection ('converters'). Sequences of biomarker abnormalities were modelled for the entire group using discriminative event-based modelling (DEBM) and for each genetic subgroup using co-initialized DEBM. These models estimate probabilistic biomarker abnormalities in a data-driven way and do not rely on previous diagnostic information or biomarker cut-off points. Using cross-validation, subjects were subsequently assigned a disease stage based on their position along the disease progression timeline. CSF NPTX2 was the first biomarker to become abnormal, followed by blood and CSF neurofilament light chain, blood phosphorylated neurofilament heavy chain, blood glial fibrillary acidic protein and finally CSF C3b and C1q. Biomarker orderings did not differ significantly between genetic subgroups, but more uncertainty was noted in the C9orf72 and MAPT groups than for GRN. Estimated disease stages could distinguish symptomatic from presymptomatic carriers and non-carriers with areas under the curve of 0.84 (95% confidence interval 0.80-0.89) and 0.90 (0.86-0.94) respectively. The areas under the curve to distinguish converters from non-converting presymptomatic carriers was 0.85 (0.75-0.95). Our data-driven model of genetic frontotemporal dementia revealed that NPTX2 and neurofilament light chain are the earliest to change among the selected biomarkers. Further research should investigate their utility as candidate selection tools for pharmaceutical trials. The model's ability to accurately estimate individual disease stages could improve patient stratification and track the efficacy of therapeutic interventions. [ABSTRACT FROM AUTHOR]

Analyzing the progression of Alzheimer's disease (AD) is challenging due to lacking sensitivity in currently available measures. AD stages are typically defined based on cognitive cut-offs, but this results in heterogeneous patient groups. More accurate modeling of the continuous progression of the disease would enable more accurate patient prognosis. To address these issues, we propose a new multivariate continuous-time disease progression (MCDP) model. The model is formulated as a nonlinear mixed-effects model that aligns patients based on their predicted disease progression along a continuous latent disease timeline. The model is evaluated using long-term follow-up data from 2152 participants in the Alzheimer's Disease Neuroimaging Initiative. The MCDP model was used to simultaneously model three cognitive scales; the Alzheimer's Disease Assessment Scale-cognitive subscale, the Mini-Mental State Examination, and the Clinical Dementia Rating scale-sum of boxes. Compared with univariate modeling and previously proposed multivariate disease progression models, the MCDP model showed superior ability to predict future patient trajectories. Finally, based on the multivariate disease timeline estimated using the MCDP model, the sensitivity of the individual items of the cognitive scales along the different stages of disease was analyzed. The analysis showed that delayed memory recall items had the highest sensitivity in the early stages of disease, whereas language and attention items were sensitive later in disease. [ABSTRACT FROM AUTHOR]

This paper proposes a mixture of linear dynamical systems model for quantifying the heterogeneous progress of Parkinson’s disease from DaTscan Images. The model is fitted to longitudinal DaTscans from the Parkinson’s Progression Marker Initiative. Fitting is accomplished using robust Bayesian inference with collapsed Gibbs sampling. Bayesian inference reveals three image-based progression subtypes which differ in progression speeds as well as progression trajectories. The model reveals characteristic spatial progression patterns in the brain, each pattern associated with a time constant. These patterns can serve as disease progression markers. The subtypes also have different progression rates of clinical symptoms measured by MDS-UPDRS Part III scores. [ABSTRACT FROM AUTHOR]

Background: Centronuclear myopathies are severe rare congenital diseases. The clinical variability and genetic heterogeneity of these myopathies result in major challenges in clinical trial design. Alternative strategies to large placebo-controlled trials that have been used in other rare diseases (e.g., the use of surrogate markers or of historical controls) have limitations that Bayesian statistics may address. Here we present a Bayesian model that uses each patient's own natural history study data to predict progression in the absence of treatment. This prospective multicentre natural history evaluated 4-year follow-up data from 59 patients carrying mutations in the MTM1 or DNM2 genes.Methods: Our approach focused on evaluation of forced expiratory volume in 1 s (FEV1) in 6- to 18-year-old children. A patient was defined as a responder if an improvement was observed after treatment and the predictive probability of such improvement in absence of intervention was less than 0.01. An FEV1 response was considered clinically relevant if it corresponded to an increase of more than 8%.Results: The key endpoint of a clinical trial using this model is the rate of response. The power of the study is based on the posterior probability that the rate of response observed is greater than the rate of response that would be observed in the absence of treatment predicted based on the individual patient's previous natural history. In order to appropriately control for Type 1 error, the threshold probability by which the difference in response rates exceeds zero was adapted to 91%, ensuring a 5% overall Type 1 error rate for the trial.Conclusions: Bayesian statistical analysis of natural history data allowed us to reliably simulate the evolution of symptoms for individual patients over time and to probabilistically compare these simulated trajectories to actual observed post-treatment outcomes. The proposed model adequately predicted the natural evolution of patients over the duration of the study and will facilitate a sufficiently powerful trial design that can cope with the disease's rarity. Further research and ongoing dialog with regulatory authorities are needed to allow for more applications of Bayesian statistics in orphan disease research. [ABSTRACT FROM AUTHOR]

Background: Our objectives were to develop a disease progression model for cognitive decline in Alzheimer's disease (AD) and to determine whether disease progression of AD is related to the year of publication, add-on trial design, and geographical regions. Methods: Placebo-controlled randomized AD clinical trials were systemically searched in public databases. Longitudinal placebo response (mean change from baseline in the cognitive subscale of the Alzheimer's Disease Assessment Scale [ADAS-cog]) and the corresponding demographic information were extracted to establish a disease progression model. Covariate screening and subgroup analyses were performed to identify potential factors affecting the disease progression rate. Results: A total of 134 publications (140 trials) were included in this model-based meta-analysis. The typical disease progression rate was 5.82 points per year. The baseline ADAS-cog score was included in the final model using an inverse U-type function. Age was found to be negatively correlated with disease progression rate. After correcting the baseline ADAS-cog score and the age effect, no significant difference in the disease progression rate was found between trials published before and after 2008 and between trials using an add-on design and those that did not use an add-on design. However, a significant difference was found among different trial regions. Trials in East Asian countries showed the slowest decline rate and the largest placebo effect. Conclusions: Our model successfully quantified AD disease progression by integrating baseline ADAS-cog score and age as important predictors. These factors and geographic location should be considered when optimizing future trial designs and conducting indirect comparisons of clinical outcomes. [ABSTRACT FROM AUTHOR]

Abstract Current models of progression in neurodegenerative diseases use neuroimaging measures that are averaged across pre-defined regions of interest (ROIs). Such models are unable to recover fine details of atrophy patterns; they tend to impose an assumption of strong spatial correlation within each ROI and no correlation among ROIs. Such assumptions may be violated by the influence of underlying brain network connectivity on pathology propagation – a strong hypothesis e.g. in Alzheimer's Disease. Here we present DIVE: D ata-driven I nference of V ertexwise E volution. DIVE is an image-based disease progression model with single-vertex resolution, designed to reconstruct long-term patterns of brain pathology from short-term longitudinal data sets. DIVE clusters vertex-wise (i.e. point-wise) biomarker measurements on the cortical surface that have similar temporal dynamics across a patient population, and concurrently estimates an average trajectory of vertex measurements in each cluster. DIVE uniquely outputs a parcellation of the cortex into areas with common progression patterns, leading to a new signature for individual diseases. DIVE further estimates the disease stage and progression speed for every visit of every subject, potentially enhancing stratification for clinical trials or management. On simulated data, DIVE can recover ground truth clusters and their underlying trajectory, provided the average trajectories are sufficiently different between clusters. We demonstrate DIVE on data from two cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Dementia Research Centre (DRC), UK. The DRC cohort contains patients with Posterior Cortical Atrophy (PCA) as well as typical Alzheimer's disease (tAD). DIVE finds similar spatial patterns of atrophy for tAD subjects in the two independent datasets (ADNI and DRC), and further reveals distinct patterns of pathology in different diseases (tAD vs PCA) and for distinct types of biomarker data – cortical thickness from Magnetic Resonance Imaging (MRI) vs amyloid load from Positron Emission Tomography (PET). We demonstrate that DIVE stages have potential clinical relevance, despite being based only on imaging data, by showing that the stages correlate with cognitive test scores. Finally, DIVE can be used to estimate a fine-grained spatial distribution of pathology in the brain using any kind of voxelwise or vertexwise measures including Jacobian compression maps, fractional anisotropy (FA) maps from diffusion tensor imaging (DTI) or other PET measures. Graphical abstract Image 1 Highlights • DIVE estimates fine-grained spatial patterns of pathology in neurodegenerative diseases, not available with ROI-based methods. • Further estimates the temporal evolution of brain pathology and subject specific disease stages. • Forecasts the evolution of pathology at population- and subject-level. • Finds plausible patterns of pathology in four distinct datasets comprising different diseases and modalities. • DIVE, available online, can be applied to any registered voxelwise images (e.g.MRI, PET, DTI). [ABSTRACT FROM AUTHOR]

One Sentence Summary: A Bayesian repeated measures model based on quantitative muscle strength data from a prospective Natural History Study was developed to determine disease progression and design clinical trials for GNE myopathy, a rare and slowly progressive muscle disease. GNE myopathy is a rare muscle disease characterized by slowly progressive weakness and atrophy of skeletal muscles. To address the significant challenges of defining the natural history and designing clinical trials for GNE myopathy, we developed a Bayesian latent variable repeated measures model to determine disease progression. The model is based on longitudinal quantitative muscle strength data collected as part of a prospective Natural History Study. The GNE Myopathy Progression Model provides an understanding of disease progression that would have otherwise required a natural history of unfeasible duration. "Disease age," the model-generated measure of disease progression, highly correlates with a variety of clinical, functional and patient-reported outcomes. With the incorporation of a treatment effect parameter to the GNE Disease Progression Model, we describe a novel GNE Myopathy Disease Modification Analysis that significantly increases power and reduces the number of subjects required to test the effectiveness of novel therapies when compared to more traditional analysis methods. The GNE Myopathy Disease Progression Model and Disease Modification Analysis can be applied to muscle diseases with prospectively collected muscle strength data, and a variety of rare and slowly progressive diseases. [ABSTRACT FROM AUTHOR]

Clinical trial outcomes for Alzheimer's disease are typically analyzed by using the mixed model for repeated measures (MMRM) or similar models that compare an efficacy scale change from baseline between treatment arms with or without participants' disease stage as a covariate. The MMRM focuses on a single-point fixed follow-up duration regardless of the exposure for each participant. In contrast to these typical models, we have developed a novel semiparametric cognitive disease progression model (DPM) for autosomal dominant Alzheimer's disease based on the Dominantly Inherited Alzheimer Network (DIAN) observational study. This model includes 3 novel features, in which the DPM (1) aligns and compares participants by disease stage, (2) uses a proportional treatment effect similar to the concept of the Cox proportional hazard ratio, and (3) incorporates extended follow-up data from participants with different follow-up durations using all data until last participant visit. We present the DPM model developed by using the DIAN observational study data and demonstrate through simulation that the cognitive DPM used in hypothetical intervention clinical trials produces substantial gains in power compared with the MMRM. [ABSTRACT FROM AUTHOR]

Background: Disease progression models are important for understanding the critical steps during the development of diseases. The models are imbedded in a statistical framework to deal with random variations due to biology and the sampling process when observing only a finite population. Conditional probabilities are used to describe dependencies between events that characterise the critical steps in the disease process. Many different model classes have been proposed in the literature, from simple path models to complex Bayesian networks. A popular and easy to understand but yet flexible model class are oncogenetic trees. These have been applied to describe the accumulation of genetic aberrations in cancer and HIV data. However, the number of potentially relevant aberrations is often by far larger than the maximal number of events that can be used for reliably estimating the progression models. Still, there are only a few approaches to variable selection, which have not yet been investigated in detail. Results: We fill this gap and propose specifically for oncogenetic trees ten variable selection methods, some of these being completely new. We compare them in an extensive simulation study and on real data from cancer and HIV. It turns out that the preselection of events by clique identification algorithms performs best. Here, events are selected if they belong to the largest or the maximum weight subgraph in which all pairs of vertices are connected.a Conclusions: The variable selection method of identifying cliques finds both the important frequent events and those related to disease pathways. [ABSTRACT FROM AUTHOR]

Purpose: To describe the natural growth of vestibular schwannoma in patients with neurofibromatosis type 2 and to predict tumor volume evolution in patients treated with bevacizumab and everolimus. Methods: Clinical data, including longitudinal tumor volumes in patients treated by bevacizumab (n = 13), everolimus (n = 7) or both (n = 2), were analyzed by means of mathematical modeling techniques. Together with clinical data, data from the literature were also integrated to account for drugs mechanisms of action. Results: We developed a model of vestibular schwannoma growth that takes into account the effect of vascular endothelial growth factors and mammalian target of rapamycin complex 1 on tumor growth. Behaviors, such as tumor growth rebound following everolimus treatment stops, was correctly described with the model. Preliminary results indicate that the model can be used to predict, based on early tumor volume dynamic, tumor response to variation in treatment dose and regimen. Conclusion: The developed model successfully describes tumor volume growth before and during bevacizumab and/or everolimus treatment. It might constitute a rational tool to predict patients' response to these drugs, thus potentially improving management of this disease. [ABSTRACT FROM AUTHOR]

1. JHL45, a novel immune modulator against atopic dermatitis (AD), was synthesized from decursin isolated from Angelica gigas. The goal is to evaluate the lead compound using quantitative modeling approaches to novel anti-AD drug development. 2. We tested the anti-inflammatory effect of JHL45 by in vitro screening, characterized its in vitro pharmacokinetic (PK) properties. The dose-dependent efficacy of JHL45 was developed using a pharmacokinetics/pharmacodynamics/disease progression (PK/PD/DIS) model in NC/Nga mice. 3. JHL45 has drug-like properties and pharmacological effects when administered orally to treat atopic dermatitis. The developed PK/PD/DIS model described well the rapid metabolism of JHL45, double-peak phenomenon in the PK of decursinol and inhibition of IgE generation by compounds in NC/Nga mice. Also, a quantitative model was developed and used to elucidate the complex interactions between serum IgE concentration and atopic dermatitis symptoms. 4. Our findings indicate that JHL45 has good physicochemical properties and powerful pharmacological effects when administered orally for treatment of AD in rodents. [ABSTRACT FROM AUTHOR]

Our objective was to develop: 1) a longitudinal model to describe amyotrophic lateral sclerosis (ALS) disease progression using the revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R); and 2) a probabilistic model to estimate the presence of clusters of trajectories in ALS progression over 12 months of treatment. Three hundred and thirty-eight patients treated with placebo from the PRO-ACT database were included in the analyses. A non-linear Weibull model best described the ALS disease progression, and a stepwise logistic regression approach was used to select the variables predicting a slow or fast disease progression. Results identified two clusters of trajectories: 1) slow disease progressors (46% of patients with a change from baseline of 13%); 2) fast disease progressors (54% of patients with a change from baseline of 49%). ROC curve analysis estimated the optimal cut-off for classifying patients as slow or fast disease progressors given ALSFRS-R measurements at 2-4 weeks. Results showed that the degree of ALS disease progression quantified by the ALSFRS-R symptomatic change on placebo is highly heterogeneous. In conclusion, this finding indicates the potential interest of disease progression models for implementing a population enrichment strategy to control the level of heterogeneity in the patients included in new trials. [ABSTRACT FROM AUTHOR]