An updated Alzheimer's disease progression model: incorporating non-linearity, beta regression, and a third-level random effect in NONMEM

Employing zero-inflated beta distribution in an exposure-response analysis of TYK2/JAK1 inhibitor brepocitinib in patients with plaque psoriasis

Brepocitinib is an oral selective dual TYK2/JAK1 inhibitor and based on its cytokine inhibition profile is expected to provide therapeutic benefit in the treatment of plaque psoriasis. Efficacy data from a completed Phase 2a study in patients with moderate-to-severe plaque psoriasis were utilized to develop a population exposure-response model that can be employed to inform dose selection decisions for further clinical development. A modeling approach that employs the zero-inflated beta distribution was used to account for the bounded nature and distributional characteristics of the Psoriasis Area and Severity Index (PASI) score data. The developed exposure-response model provided an adequate description of the observed PASI scores across all the treatment arms tested and across both the induction and maintenance dosing periods of the study. In addition, the developed model exhibited a good predictive capacity with regard to the derived responder metrics (e.g., 75%/90%/100% improvement in PASI score [PASI75/90/100]). Clinical trial simulations indicated that the induction/maintenance dosing paradigm explored in this study does not offer any advantages from an efficacy perspective and that doses of 10, 30, and 60 mg once-daily may be suitable candidates for clinical evaluation in subsequent Phase 2b studies.

Onset of Alzheimer disease in apolipoprotein ɛ4 carriers is earlier in butyrylcholinesterase K variant carriers

BACKGROUND

The authors sought to examine the impact of the K-variant of butyrylcholinesterase (BCHE-K) carrier status on age-at-diagnosis of Alzheimer disease (AD) in APOE4 carriers.

METHODS

Patients aged 50-74 years with cerebrospinal fluid (CSF) biomarker-confirmed AD, were recruited to clinical trial (NCT03186989 since June 14, 2017). Baseline demographics, disease characteristics, and biomarkers were evaluated in 45 patients according to BCHE-K and APOE4 allelic status in this post-hoc study.

RESULTS

In APOE4 carriers (N = 33), the mean age-at-diagnosis of AD in BCHE-K carriers (n = 11) was 6.4 years earlier than in BCHE-K noncarriers (n = 22, P < .001, ANOVA). In APOE4 noncarriers (N = 12) there was no observed influence of BCHE-K. APOE4 carriers with BCHE-K also exhibited slightly higher amyloid and tau accumulations compared to BCHE-K noncarriers. A predominantly amyloid, limited tau, and limbic-amnestic phenotype was exemplified by APOE4 homozygotes with BCHE-K. In the overall population, multiple regression analyses demonstrated an association of amyloid accumulation with APOE4 carrier status (P < .029), larger total brain ventricle volume (P < .021), less synaptic injury (Ng, P < .001), and less tau pathophysiology (p-tau181, P < .005). In contrast, tau pathophysiology was associated with more neuroaxonal damage (NfL, P = .002), more synaptic injury (Ng, P < .001), and higher levels of glial activation (YKL-40, P = .01).

CONCLUSION

These findings have implications for the genetic architecture of prognosis in early AD, not the genetics of susceptibility to AD. In patients with early AD aged less than 75 years, the mean age-at-diagnosis of AD in APOE4 carriers was reduced by over 6 years in BCHE-K carriers versus noncarriers. The functional status of glia may explain many of the effects of APOE4 and BCHE-K on the early AD phenotype.

TRIAL REGISTRATION

NCT03186989 since June 14, 2017.

An updated overview of recent and ongoing deep brain stimulation (DBS) trials in patients with dementia: a systematic review

BACKGROUND

Dementia affects more than 55 million people worldwide. Several technologies have been developed to slow cognitive decline: deep brain stimulation (DBS) of network targets in Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) have been recently investigated.

OBJECTIVE

This study aimed to review the characteristics of the populations, protocols, and outcomes of patients with dementia enrolled in clinical trials investigating the feasibility and efficacy of DBS.

MATERIALS AND METHODS

A systematic search of all registered RCTs was performed on Clinicaltrials.gov and EudraCT, while a systematic literature review was conducted on PubMed, Scopus, Cochrane, and APA PsycInfo to identify published trials.

RESULTS

The literature search yielded 2122 records, and the clinical trial search 15 records. Overall, 17 studies were included. Two of 17 studies were open-label studies reporting no NCT/EUCT code and were analysed separately. Of 12 studies investigating the role of DBS in AD, we included 5 published RCTs, 2 unregistered open-label (OL) studies, 3 recruiting studies, and 2 unpublished trials with no evidence of completion. The overall risk of bias was assessed as moderate-high. Our review showed significant heterogeneity in the recruited populations regarding age, disease severity, informed consent availability, inclusion, and exclusion criteria. Notably, the standard mean of overall severe adverse events was moderately high (SAEs: 9.10 ± 7.10%).

CONCLUSION

The population investigated is small and heterogeneous, published results from clinical trials are under-represented, severe adverse events not negligible, and cognitive outcomes uncertain. Overall, the validity of these studies requires confirmation based on forthcoming higher-quality clinical trials.

Modeling Alzheimer's disease progression utilizing clinical trial and ADNI data to predict longitudinal trajectory of CDR-SB

There is strong interest in developing predictive models to better understand individual heterogeneity and disease progression in Alzheimer's disease (AD). We have built upon previous longitudinal AD progression models, using a nonlinear, mixed-effect modeling approach to predict Clinical Dementia Rating Scale - Sum of Boxes (CDR-SB) progression. Data from the Alzheimer's Disease Neuroimaging Initiative (observational study) and placebo arms from four interventional trials (N = 1093) were used for model building. The placebo arms from two additional interventional trials (N = 805) were used for external model validation. In this modeling framework, CDR-SB progression over the disease trajectory timescale was obtained for each participant by estimating disease onset time (DOT). Disease progression following DOT was described by both global progression rate (RATE) and individual progression rate (α). Baseline Mini-Mental State Examination and CDR-SB scores described the interindividual variabilities in DOT and α well. This model successfully predicted outcomes in the external validation datasets, supporting its suitability for prospective prediction and use in design of future trials. By predicting individual participants' disease progression trajectories using baseline characteristics and comparing these against the observed responses to new agents, the model can help assess treatment effects and support decision making for future trials.

Donanemab exposure and efficacy relationship using modeling in Alzheimer's disease

INTRODUCTION

Donanemab is an amyloid-targeting therapy that specifically targets brain amyloid plaques. The objective of these analyses was to characterize the relationship of donanemab exposure with plasma biomarkers and clinical efficacy through modeling.

METHODS

Data for the analyses were from participants with Alzheimer's disease from the phase 1 and TRAILBLAZER-ALZ studies. Indirect-response models were used to fit plasma phosphorylated tau 217 (p-tau217) and plasma glial fibrillated acidic protein (GFAP) data over time. Disease-progression models were developed using pharmacokinetic/pharmacodynamic modeling.

RESULTS

The plasma p-tau217 and plasma GFAP models adequately predicted the change over time, with donanemab resulting in decreased plasma p-tau217 and plasma GFAP concentrations. The disease-progression models confirmed that donanemab significantly reduced the rate of clinical decline. Simulations revealed that donanemab slowed disease progression irrespective of baseline tau positron emission tomography (PET) level within the evaluated population.

DISCUSSION

The disease-progression models show a clear treatment effect of donanemab on clinical efficacy regardless of baseline disease severity.

Characterization of exposure-Clinical Dementia Rating-Sum of Boxes relationship in subjects with early Alzheimer's disease from the aducanumab Phase 3 trials

Clinical Dementia Rating-Sum of Boxes (CDR-SB) assessments from two Phase 3 studies (ENGAGE and EMERGE) of aducanumab in subjects with early Alzheimer's disease (AD) were pooled to develop an exposure-response (ER) model. A linear model in the logit-transformed scaled CDR-SB best characterized the time profile for placebo- and aducanumab-treated subjects, with concentration as the exposure metric. The model allowed delineation of slow (4%), typical (86%), and fast (10%) progressing subpopulations in the data. The estimated drug effect on the disease progression rate was significant, 2.05 L/(g·year), with a 95% confidence interval (1.60, 2.50) that did not include zero. Following an evaluation of a series of ER model forms including differential drug and null effects either between the studies or among the three progression classes, the final ER model with a common (pooled) estimate of the drug effect between the studies and among the three progression classes was considered parsimonious. The final model provides supportive evidence that the two studies demonstrate a common intrinsic pharmacology. None of the identified covariates (Mini-Mental State Examination-BL score and Asian race) were clinically meaningful. Finally, simulations demonstrated that the intrinsic pharmacology remained consistent between the two Phase 3 studies.

Association of Amyloid Reduction After Donanemab Treatment With Tau Pathology and Clinical Outcomes: The TRAILBLAZER-ALZ Randomized Clinical Trial

Question

Is donanemab-induced amyloid reduction associated with slowing of tau pathology and clinical decline in individuals with Alzheimer disease?

Findings

In early symptomatic Alzheimer disease, donanemab induced a robust decrease in amyloid plaque levels by 24 weeks, with baseline plaque directly associated with magnitude of amyloid reduction and inversely associated with probability of complete clearance. In individuals with amyloid clearance, post hoc modeling suggests that amyloid levels would remain below the positivity threshold for almost 4 years without treatment; in treated patients, greater plaque clearance was associated with slower progression of tau positron emission tomography and slower clinical decline (in apolipoprotein E ε4 carriers only).

Meaning

Exploratory post hoc analyses of the Study of LY3002813 in Participants With Early Symptomatic Alzheimer’s Disease (TRAILBLAZER-ALZ) identified potential associations between amyloid lowering, tau pathology, and clinical outcomes.

Importance

β-amyloid plaques and neurofibrillary tau deposits biologically define Alzheimer disease.

Objective

To perform post hoc analyses of amyloid reduction after donanemab treatment and assess its association with tau pathology and clinical measures.

Design, Setting, and Participants

The Study of LY3002813 in Participants With Early Symptomatic Alzheimer’s Disease (TRAILBLAZER-ALZ) was a phase 2, placebo-controlled, randomized clinical trial conducted from December 18, 2017, to December 4, 2020, with a double-blind period of up to 76 weeks and a 48-week follow-up period. The study was conducted at 56 centers in the US and Canada. Enrolled were participants from 60 to 85 years of age with gradual and progressive change in memory function for 6 months or more, early symptomatic Alzheimer disease, elevated amyloid, and intermediate tau levels.

Interventions

Donanemab (an antibody specific for the N-terminal pyroglutamate β-amyloid epitope) dosing was every 4 weeks: 700 mg for the first 3 doses, then 1400 mg for up to 72 weeks. Blinded dose-reduction evaluations occurred at 24 and 52 weeks based on amyloid clearance.

Main Outcomes and Measures

Change in amyloid, tau, and clinical decline after donanemab treatment.

Results

The primary study randomized 272 participants (mean [SD] age, 75.2 [5.5] years; 145 female participants [53.3%]). The trial excluded 1683 of 1955 individuals screened. The rate of donanemab-induced amyloid reduction at 24 weeks was moderately correlated with the amount of baseline amyloid (Spearman correlation coefficient r, −0.54; 95% CI, −0.66 to −0.39; P < .001). Modeling provides a hypothesis that amyloid would not reaccumulate to the 24.1-centiloid threshold for 3.9 years (95% prediction interval, 1.9-8.3 years) after discontinuing donanemab treatment. Donanemab slowed tau accumulation in a region-dependent manner as measured using neocortical and regional standardized uptake value ratios with cerebellar gray reference region. A disease-progression model found a significant association between percentage amyloid reduction and change on the integrated Alzheimer Disease Rating Scale only in apolipoprotein E (APOE) ε4 carriers (95% CI, 24%-59%; P < .001).

Conclusions and Relevance

Results of post hoc analyses for donanemab-treated participants suggest that baseline amyloid levels were directly associated with the magnitude of amyloid reduction and inversely associated with the probability of achieving complete amyloid clearance. The donanemab-induced slowing of tau was more pronounced in those with complete amyloid clearance and in brain regions identified later in the pathologic sequence. Data from other trials will be important to confirm aforementioned observations, particularly treatment response by APOE ε4 status.

Trial Registration

ClinicalTrials.gov Identifier: NCT03367403

Role of Disease Progression Models in Drug Development

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.

Influence of Subject-Specific Effects in Longitudinal Modelling of Cognitive Decline in Alzheimer's Disease

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.

An Item Response Theory-Informed Strategy to Model Total Score Data from Composite Scales

Composite scale data is widely used in many therapeutic areas and consists of several categorical questions/items that are usually summarized into a total score (TS). Such data is discrete and bounded by nature. The gold standard to analyse composite scale data is item response theory (IRT) models. However, IRT models require item-level data while sometimes only TS is available. This work investigates models for TS. When an IRT model exists, it can be used to derive the information as well as expected mean and variability of TS at any point, which can inform TS-analyses. We propose a new method: IRT-informed functions of expected values and standard deviation in TS-analyses. The most common models for TS-analyses are continuous variable (CV) models, while bounded integer (BI) models offer an alternative that respects scale boundaries and the nature of TS data. We investigate the method in CV and BI models on both simulated and real data. Both CV and BI models were improved in fit by IRT-informed disease progression, which allows modellers to precisely and accurately find the corresponding latent variable parameters, and IRT-informed SD, which allows deviations from homoscedasticity. The methodology provides a formal way to link IRT models and TS models, and to compare the relative information of different model types. Also, joint analyses of item-level data and TS data are made possible. Thus, IRT-informed functions can facilitate total score analysis and allow a quantitative analysis of relative merits of different analysis methods.

Comparison of Precision and Accuracy of Five Methods to Analyse Total Score Data

Total score (TS) data is generated from composite scales consisting of several questions/items, such as the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The analysis method that most fully uses the information gathered is item response theory (IRT) models, but these are complex and require item-level data which may not be available. Therefore, the TS is commonly analysed with standard continuous variable (CV) models, which do not respect the bounded nature of data. Bounded integer (BI) models do respect the data nature but are not as extensively researched. Mixed models for repeated measures (MMRM) are an alternative that requires few assumptions and handles dropout without bias. If an IRT model exists, the expected mean and standard deviation of TS can be computed through IRT-informed functions-which allows CV and BI models to estimate parameters on the IRT scale. The fit, performance on external data and parameter precision (when applicable) of CV, BI and MMRM to analyse simulated TS data from the MDS-UPDRS motor subscale are investigated in this work. All models provided accurate predictions and residuals without trends, but the fit of CV and BI models was improved by IRT-informed functions. The IRT-informed BI model had more precise parameter estimates than the IRT-informed CV model. The IRT-informed models also had the best performance on external data, while the MMRM model was worst. In conclusion, (1) IRT-informed functions improve TS analyses and (2) IRT-informed BI models had more precise IRT parameter estimates than IRT-informed CV models.

Modelling Decline in Cognition to Decline in Function in Alzheimer's Disease

OBJECTIVES

The study aimed to evaluate and quantify the temporal link between cognitive and functional decline, and assess the impact of the apolipoprotein E4 (APOE-e4) genotype on Alzheimer's disease (AD) progression.

METHODS

A nonlinear mixed-effects Emax model was developed using longitudinal data from 659 patients with dementia due to AD sourced from the Alzheimer's disease neuroimaging initiative (ADNI) database. A cognitive decline model was first built using a cognitive subscale of the AD assessment scale (delayed word recall) as the endpoint, followed by a functional decline model, using the functional assessment questionnaire (FAQ) as the endpoint. Individual and population cognitive decline from the first model drove a functional decline in the second model. The impact of the APOE-e4 genotype status on the dynamics of AD progression was evaluated using the model.

RESULTS

Mixed-effects Emax models adequately quantified population average and individual disease trajectories. The model captured a higher initial cognitive impairment and final functional impairment in APOE-e4 carriers than non-carriers. The age at cognitive decline and diagnosis of dementia due to AD was significantly lower in APOE-e4 carriers than that of non-carriers. The average [standard deviation] time shift between cognitive and functional decline, i.e. the time span between half of the maximum cognitive decline and half of the maximum functional decline, was estimated as 1.5 [1.6] years.

CONCLUSION

The present analysis quantifies the temporal link between a cognitive and functional decline in AD progression at the population and individual level, and provides information about the potential benefits of pre-clinical AD treatments on both cognition and function.

Differential response to cytotoxic therapy explains treatment dynamics of acute myeloid leukaemia patients: insights from a mathematical modelling approach

Disease response and durability of remission are very heterogeneous in patients with acute myeloid leukaemia (AML). There is increasing evidence that the individual risk of early relapse can be predicted based on the initial treatment response. However, it is unclear how such a correlation is linked to functional aspects of AML progression and treatment. We suggest a mathematical model in which leukaemia-initiating cells and normal/healthy haematopoietic stem and progenitor cells reversibly change between an active state characterized by proliferation and chemosensitivity and a quiescent state, in which the cells do not divide, but are also insensitive to chemotherapy. Applying this model to 275 molecular time courses of nucleophosmin 1-mutated patients, we conclude that the differential chemosensitivity of the leukaemia-initiating cells together with the cells' intrinsic proliferative capacity is sufficient to reproduce both, early relapse as well as long-lasting remission. We can, furthermore, show that the model parameters associated with individual chemosensitivity and proliferative advantage of the leukaemic cells are closely linked to the patients' time to relapse, while a reliable prediction based on early response only is not possible based on the currently available data. Although we demonstrate with our approach, that the complete response data is sufficient to quantify the aggressiveness of the disease, further investigations are necessary to study how an intensive early sampling strategy may prospectively improve risk assessment and help to optimize individual treatments.

Development of a Disease Progression Model for Leucine-Rich Repeat Kinase 2 in Parkinson's Disease to Inform Clinical Trial Designs

A quantitative assessment of Parkinson's disease (PD) progression is critical for optimizing clinical trials design. Disease progression model was developed using pooled data from the Progression Marker Initiative study and the Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation in Parkinson's Disease study. Age, gender, concomitant medication, and study arms were predictors of baseline. A mutation in the leucine-rich repeat kinase 2 (LRRK2) encoding gene was associated with the disease progression rate. The progression rate in subjects with PD who carried LRRK2 mutation was slightly slower (~0.170 points/month) than that in PD subjects without the mutation (~0.222 points/month). For a nonenriched placebo-controlled clinical trial, approximately 70 subjects/arm would be required to detect a drug effect of 50% reduction in the progression rate with 80% probability, whereas 85, 93, and 100 subjects/arm would be required for an enriched clinical trial with 30%, 50%, and 70% subjects with LRRK2 mutations, respectively.

Hippocampal Neuroimaging-Informed Clinical Trial Enrichment Tool for Amnestic Mild Cognitive Impairment Using Open Data

Our goal was to assess the enrichment utility of hippocampal volume (HV) as an enrichment biomarker in amnestic mild cognitive impairment (aMCI) clinical trials, and, hence, develop an HV neuroimaging-informed clinical trial enrichment tool. Modeling of integrated longitudinal patient-level data came from open-access natural history studies in patients diagnosed with aMCI-the Alzheimer's Disease Neuroimaging Initiative (ADNI)-1 and ADNI-2-and indicated that a decrease of 1 cm³ with respect to the analysis dataset median baseline intracranial volume-adjusted HV (ICV-HV; ~ 5 cm³ ) is associated with > 50% increase in disease progression rate as measured by the Clinical Dementia Rating Scale-Sum of Boxes. Clinical trial simulations showed that the inclusion of aMCI subjects with baseline ICV-HV below the 84th or 50th percentile allowed an approximate reduction in trial size of at least 26% and 55%, respectively. This clinical trial enrichment tool can help design more efficient and informative clinical trials.

Misidentification Subtype of Alzheimer's Disease Psychosis Predicts a Faster Cognitive Decline

The presence of psychosis is associated with a more rapid decline in Alzheimer's disease (AD), but the impact of paranoid (persecutory delusions) and misidentification (misperceptions and/or hallucinations) subtypes of psychosis on the speed of decline in AD is still unclear. We analyzed data on Alzheimer's Disease Neuroimaging Initiative 2 participants with late mild cognitive impairment or AD, and we described individual trajectories of Alzheimer's Disease Assessment Scale-Cognitive Subscale scores using a semimechanistic logistic model with a mixed effects-based approach, which accounted for dropout and adjusted for baseline Mini Mental State Examination scores. The covariate model included psychosis subtypes, age, gender, education, medications, and Apolipoprotein E epsilon 4 (Apo-e ε4 genotype). We found that the Alzheimer's Disease Assessment Scale-Cognitive Subscale rate of increase was doubled in misidentification (β_{r,misid_subtype}  = 0.63, P = 0.031) and mixed (both subtypes; β_{r,mixed_subtype}  = 0.70, P = 0.003) when compared with nonpsychotic (or paranoid) patients, suggesting that the misidentification subtype may represent a distinct AD sub-phenotype associated with an accelerated pathological process.

Opening the debate on deep brain stimulation for Alzheimer disease - a critical evaluation of rationale, shortcomings, and ethical justification

BACKGROUND

Deep brain stimulation (DBS) as investigational intervention for symptomatic relief from Alzheimer disease (AD) has generated big expectations. Our aim is to discuss the ethical justification of this research agenda by examining the underlying research rationale as well as potential methodological pitfalls. The shortcomings we address are of high ethical importance because only scientifically valid research has the potential to be ethical.

METHOD

We performed a systematic search on MEDLINE and EMBASE. We included 166 publications about DBS for AD into the analysis of research rationale, risks and ethical aspects. Fifty-eight patients were reported in peer-reviewed journals with very mixed results. A grey literature search revealed hints for 75 yet to be published, potentially enrolled patients.

RESULTS

The results of our systematic review indicate methodological shortcomings in the literature that are both scientific and ethical in nature. According to our analysis, research with human subjects was performed before decisive preclinical research was published examining the specific research question at stake. We also raise the concern that conclusions on the potential safety and efficacy have been reported in the literature that seem premature given the design of the feasibility studies from which they were drawn. In addition, some publications report that DBS for AD was performed without written informed consent from some patients, but from surrogates only. Furthermore, registered ongoing trials plan to enroll severely demented patients. We provide reasons that this would violate Art. 28 of the Declaration of Helsinki, because DBS for AD involves more than minimal risks and burdens, and because its efficacy and safety are not yet empirically established to be likely.

CONCLUSION

Based on our empirical analysis, we argue that clinical research on interventions of risk class III (Food and Drug Administration and European Medicines Agency) should not be exploratory but grounded on sound, preclinically tested, and disease-specific a posteriori hypotheses. This also applies to DBS for dementia as long as therapeutic benefits are uncertain, and especially when research subjects with cognitive deficits are involved, who may foreseeably progress to full incapacity to provide informed consent during the required follow-up period.

Dopamine Transporter Neuroimaging as an Enrichment Biomarker in Early Parkinson's Disease Clinical Trials: A Disease Progression Modeling Analysis

Given the recognition that disease‐modifying therapies should focus on earlier Parkinson's disease stages, trial enrollment based purely on clinical criteria poses significant challenges. The goal herein was to determine the utility of dopamine transporter neuroimaging as an enrichment biomarker in early motor Parkinson's disease clinical trials. Patient‐level longitudinal data of 672 subjects with early‐stage Parkinson's disease in the Parkinson's Progression Markers Initiative (PPMI) observational study and the Parkinson Research Examination of CEP‐1347 Trial (PRECEPT) clinical trial were utilized in a linear mixed‐effects model analysis. The rate of worsening in the motor scores between subjects with or without a scan without evidence of dopamine transporter deficit was different both statistically and clinically. The average difference in the change from baseline of motor scores at 24 months between biomarker statuses was –3.16 (90% confidence interval [CI] = –0.96 to –5.42) points. Dopamine transporter imaging could identify subjects with a steeper worsening of the motor scores, allowing trial enrichment and 24% reduction of sample size.

Disease Progression Modeling: Key Concepts and Recent Developments

Disease modeling involves the use of mathematical functions to describe quantitatively the time course of disease progression. In order to characterize the natural progression of disease, these models generally incorporate longitudinal data for some biomarker(s) of disease severity or can incorporate more direct measures of disease severity. Disease models are also often linked to pharmacokinetic-pharmacodynamic models so that the influence of drug treatment on disease progression can be quantified and evaluated. Regulatory agencies have embraced disease progression models as powerful tools that can be used to improve drug development productivity. This article provides a brief overview of key concepts in disease progression modeling followed by illustrative examples from models for Alzheimer's disease. Finally, recent novel applications in which disease progression models have been linked to cost-effectiveness analysis and genomic analysis are described.

A review and comparison of Bayesian and likelihood-based inferences in beta regression and zero-or-one-inflated beta regression

Beta regression is an increasingly popular statistical technique in medical research for modeling of outcomes that assume values in (0, 1), such as proportions and patient reported outcomes. When outcomes take values in the intervals [0,1), (0,1], or [0,1], zero-or-one-inflated beta (zoib) regression can be used. We provide a thorough review on beta regression and zoib regression in the modeling, inferential, and computational aspects via the likelihood-based and Bayesian approaches. We demonstrate the statistical and practical importance of correctly modeling the inflation at zero/one rather than ad hoc replacing them with values close to zero/one via simulation studies; the latter approach can lead to biased estimates and invalid inferences. We show via simulation studies that the likelihood-based approach is computationally faster in general than MCMC algorithms used in the Bayesian inferences, but runs the risk of non-convergence, large biases, and sensitivity to starting values in the optimization algorithm especially with clustered/correlated data, data with sparse inflation at zero and one, and data that warrant regularization of the likelihood. The disadvantages of the regular likelihood-based approach make the Bayesian approach an attractive alternative in these cases. Software packages and tools for fitting beta and zoib regressions in both the likelihood-based and Bayesian frameworks are also reviewed.

Alzheimer's disease assessment scale-cognitive 11-item progression model in mild-to-moderate Alzheimer's disease trials of bapineuzumab

Introduction

The objective of this study was to estimate longitudinal changes in disease progression (measured by Alzheimer's disease assessment scale-cognitive 11-item [ADAS-cog/11] scale) after bapineuzumab treatment and to identify covariates (demographics or baseline characteristics) contributing to the variability in disease progression rate and baseline disease status.

Methods

A population-based disease progression model was developed using pooled placebo and bapineuzumab data from two phase-3 studies in APOE ε4 noncarrier and carrier Alzheimer's disease (AD) patients.

Results

A beta regression model with the Richard's function as the structural component best described ADAS-cog/11 disease progression for mild-to-moderate AD population. This analysis confirmed no effect of bapineuzumab exposure on ADAS-cog/11 progression rate, consistent with the lack of clinical efficacy observed in the statistical analysis of ADAS-cog/11 data in both studies. Assessment of covariates affecting baseline severity revealed that men had a 6% lower baseline ADAS-cog/11 score than women; patients who took two AD concomitant medications had a 19% higher (worse) baseline score; APOE ε4 noncarriers had a 5% lower baseline score; and patients who had AD for a longer duration had a higher baseline score. Furthermore, shorter AD duration, younger age, APOE ε4 carrier status, and use of two AD concomitant medications were associated with faster disease progression rates. Patients who had an ADAS-cog/11 score progression rate that was not statistically significantly different from 0 typically took no AD concomitant medications.

Discussion

The beta regression model is a sensible modeling approach to characterize cognitive decline in AD patients. The influence of bapineuzumab exposure on disease progression measured by ADAS-cog/11 was not significant.

Trial Registration

ClinicalTrials.gov identifier: NCT00575055 and NCT00574132.