Proof of concept demonstration of optimal composite MRI endpoints for clinical trials

Optimal Weighting of Preclinical Alzheimer's Cognitive Composite (PACC) Scales to Improve their Performance as Outcome Measures for Alzheimer's Disease Clinical Trials

Introduction

Cognitive composite scales constructed by combining existing neuropsychometric tests are seeing wide application as endpoints for clinical trials and cohort studies of Alzheimer's disease (AD) predementia conditions. Preclinical Alzheimer's Cognitive Composite (PACC) scales are composite scores calculated as the sum of the component test scores weighted by the reciprocal of their standard deviations at the baseline visit. Reciprocal standard deviation is an arbitrary weighting in this context, and may be an inefficient utilization of the data contained in the component measures. Mathematically derived optimal composite weighting is a promising alternative.

Methods

Sample size projections using standard power calculation formulas were used to describe the relative performance of component measures and their composites when used as endpoints for clinical trials. Power calculations were informed by (n=1,333) amnestic mild cognitive impaired participants in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set.

Results

A composite constructed using PACC reciprocal standard deviation weighting was both less sensitive to change than one of its component measures and less sensitive to change than its optimally weighted counterpart. In standard sample size calculations informed by NACC data, a clinical trial using the PACC weighting would require 38% more subjects than a composite calculated using optimal weighting.

Discussion

These findings illustrate how reciprocal standard deviation weighting can result in inefficient cognitive composites, and underscore the importance of component weights to the performance of composite scales. In the future, optimal weighting parameters informed by accumulating clinical trial data may improve the efficiency of clinical trials in AD.

Association of Regional Cortical Network Atrophy With Progression to Dementia in Patients With Primary Progressive Aphasia

BACKGROUND AND OBJECTIVES

Patients with primary progressive aphasia (PPA) have gradually progressive language deficits during the initial phase of the illness. As the underlying neurodegenerative disease progresses, patients with PPA start losing independent functioning due to the development of nonlanguage cognitive or behavioral symptoms. The timeline of this progression from the mild cognitive impairment stage to the dementia stage of PPA is variable across patients. In this study, in a sample of patients with PPA, we measured the magnitude of cortical atrophy within functional networks believed to subserve diverse cognitive and affective functions. The objective of the study was to evaluate the utility of this measure as a predictor of time to subsequent progression to dementia in PPA.

METHODS

Patients with PPA with largely independent daily function were recruited through the Massachusetts General Hospital Frontotemporal Disorders Unit. All patients underwent an MRI scan at baseline. Cortical atrophy was then estimated relative to a group of amyloid-negative cognitively normal control participants. For each patient, we measured the time between the baseline visit and the subsequent visit at which dementia progression was documented or last observation. Simple and multivariable Cox regression models were used to examine the relationship between cortical atrophy and the likelihood of progression to dementia.

RESULTS

Forty-nine patients with PPA (mean age = 66.39 ± 8.36 years, 59.2% females) and 25 controls (mean age = 67.43 ± 4.84 years, 48% females) were included in the data analysis. Greater baseline atrophy in not only the left language network (hazard ratio = 1.47, 95% CI = 1.17-1.84) but also in the frontoparietal control (1.75, 1.25-2.44), salience (1.63, 1.25-2.13), default mode (1.55, 1.19-2.01), and ventral frontotemporal (1.41, 1.16-1.71) networks was associated with a higher risk of progression to dementia. A multivariable model identified contributions of the left frontoparietal control (1.94, 1.09-3.48) and ventral frontotemporal (1.61, 1.09-2.39) networks in predicting dementia progression, with no additional variance explained by the language network (0.75, 0.43-1.31).

DISCUSSION

These results suggest that baseline atrophy in cortical networks subserving nonlanguage cognitive and affective functions is an important predictor of progression to dementia in PPA. This measure should be included in precision medicine models of prognosis in PPA.

Neuroimaging in Frontotemporal Lobar Degeneration: Research and Clinical Utility

Frontotemporal lobar dementia (FTLD) is a clinically and pathologically complex disease. Advances in neuroimaging techniques have provided a specialized set of tools to investigate underlying pathophysiology and identify clinical biomarkers that aid in diagnosis, prognostication, monitoring, and identification of appropriate endpoints in clinical trials. In this chapter, we review data discussing the utility of neuroimaging biomarkers in sporadic FTLD, with an emphasis on current and future clinical applications. Among those modalities readily utilized in clinical settings, T1-weighted structural magnetic resonance imaging (MRI) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) are best supported in differential diagnosis and as targets for clinical trial endpoints. However, a number of nonclinical neuroimaging modalities, including diffusion tensor imaging and resting-state functional connectivity MRI, show promise as biomarkers to predict progression and as clinical trial endpoints. Other neuroimaging modalities, including amyloid PET, Tau PET, and arterial spin labeling MRI, are also discussed, though more work is required to establish their utility in FTLD in clinical settings.

Prospective longitudinal atrophy in Alzheimer's disease correlates with the intensity and topography of baseline tau-PET

β-Amyloid plaques and tau-containing neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer's disease (AD) and are thought to play crucial roles in a neurodegenerative cascade leading to dementia. Both lesions can now be visualized in vivo using positron emission tomography (PET) radiotracers, opening new opportunities to study disease mechanisms and improve patients' diagnostic and prognostic evaluation. In a group of 32 patients at early symptomatic AD stages, we tested whether β-amyloid and tau-PET could predict subsequent brain atrophy measured using longitudinal magnetic resonance imaging acquired at the time of PET and 15 months later. Quantitative analyses showed that the global intensity of tau-PET, but not β-amyloid-PET, signal predicted the rate of subsequent atrophy, independent of baseline cortical thickness. Additional investigations demonstrated that the specific distribution of tau-PET signal was a strong indicator of the topography of future atrophy at the single patient level and that the relationship between baseline tau-PET and subsequent atrophy was particularly strong in younger patients. These data support disease models in which tau pathology is a major driver of local neurodegeneration and highlight the relevance of tau-PET as a precision medicine tool to help predict individual patient's progression and design future clinical trials.

Sex-specific composite scales for longitudinal studies of incipient Alzheimer's disease

Introduction

The impact of Alzheimer's disease (AD) on cognitive decline differs by sex. Composite scores are useful as singular outcomes in clinical trials, yet to date these have not been developed to measure sex-specific change.

Method

We derived optimal composites from component scales available in the AD Neuroimaging Initiative (ADNI) database among cognitively normal and mild cognitively impaired subjects who are cerebrospinal fluid amyloid-β positive for early AD. Maximally sensitive composites were constructed separately for men and women using standard formulas. We compared the statistical power of the composites with the ADNI Prodromal Alzheimer's Cognitive Composite.

Results

Among 9 cognitive measures and clinical dementia rating sum of boxes, the optimal sex-specific composites included 5 measures, including the clinical dementia rating and 4 distinct cognitive measures. The sex-specific composites consistently outperformed sex-agnostic composites and the ADNI Prodromal Alzheimer's Cognitive Composite.

Discussion

Sex-specific composite scales may improve the power of longitudinal studies of early AD and clinical trials.

Clinical and cortical decline in the aphasic variant of Alzheimer's disease

INTRODUCTION

Primary progressive aphasia (PPA) displays variable progression trajectories that require further elucidation.

METHODS

Longitudinal quantitation of atrophy and language over 12 months was completed for PPA patients with and without positive amyloid PET (PPA^Aβ+ and PPA^Aβ-), an imaging biomarker of underlying Alzheimer's disease.

RESULTS

Over 12 months, both PPA groups showed significantly greater cortical atrophy rates in the left versus right hemisphere, with a more widespread pattern in PPA^Aβ+. The PPA^Aβ+ group also showed greater decline in performance on most language tasks. There was no obligatory relationship between the logopenic PPA variant and amyloid status. Effect sizes from quantitative MRI data were more robust than neuropsychological metrics.

DISCUSSION

Preferential language network neurodegeneration is present in PPA irrespective of amyloid status. Clinical and anatomical progression appears to differ for PPA due to Alzheimer's disease versus non-Alzheimer's disease neuropathology, a distinction that may help to inform prognosis and the design of intervention trials.

Design of pilot studies to inform the construction of composite outcome measures

Introduction

Composite scales have recently been proposed as outcome measures for clinical trials. For example, the Preclinical Alzheimer's Cognitive Composite (PACC) is the sum of z-score normed component measures assessing episodic memory, timed executive function, and global cognition. Alternative methods of calculating composite total scores using the weighted sum of the component measures that maximize the signal-to-noise ratio of the resulting composite score have been proposed. Optimal weights can be estimated from pilot data, but it is an open question how large a pilot trial is required to calculate reliably optimal weights.

Methods

We describe the calculation of optimal weights and use large-scale computer simulations to investigate the question as how large a pilot study sample is required to inform the calculation of optimal weights. The simulations are informed by the pattern of decline observed in cognitively normal subjects enrolled in the Alzheimer's Disease Cooperative Study Prevention Instrument cohort study, restricting to n = 75 subjects aged 75 years and older with an APOE ε4 risk allele and therefore likely to have an underlying Alzheimer's disease neurodegenerative process.

Results

In the context of secondary prevention trials in Alzheimer's disease and using the components of the PACC, we found that pilot studies as small as 100 are sufficient to meaningfully inform weighting parameters. Regardless of the pilot study sample size used to inform weights, the optimally weighted PACC consistently outperformed the standard PACC in terms of statistical power to detect treatment effects in a clinical trial. Pilot studies of size 300 produced weights that achieved near-optimal statistical power and reduced required sample size relative to the standard PACC by more than half.

Discussion

These simulations suggest that modestly sized pilot studies, comparable to that of a phase 2 clinical trial, are sufficient to inform the construction of composite outcome measures. Although these findings apply only to the PACC in the context of prodromal Alzheimer's disease, the observation that weights only have to approximate the optimal weights to achieve near-optimal performance should generalize. Performing a pilot study or phase 2 trial to inform the weighting of proposed composite outcome measures is highly cost-effective. The net effect of more efficient outcome measures is that smaller trials will be required to test novel treatments. Alternatively, second generation trials can use prior clinical trial data to inform weighting, so that greater efficiency can be achieved as we move forward.

Potential implications of practice effects in Alzheimer's disease prevention trials

Introduction

Practice effects (PEs) present a potential confound in clinical trials with cognitive outcomes. A single-blind placebo run-in design, with repeated cognitive outcome assessments before randomization to treatment, can minimize effects of practice on trial outcome.

Methods

We investigated the potential implications of PEs in Alzheimer's disease prevention trials using placebo arm data from the Alzheimer's Disease Cooperative Study donepezil/vitamin E trial in mild cognitive impairment. Frequent ADAS-Cog measurements early in the trial allowed us to compare two competing trial designs: a 19-month trial with randomization after initial assessment, versus a 15-month trial with a 4-month single-blind placebo run-in and randomization after the second administration of the ADAS-Cog. Standard power calculations assuming a mixed-model repeated-measure analysis plan were used to calculate sample size requirements for a hypothetical future trial designed to detect a 50% slowing of cognitive decline.

Results

On average, ADAS-Cog 13 scores improved at first follow-up, consistent with a PE and progressively worsened thereafter. The observed change for a 19-month trial (1.18 points) was substantively smaller than that for a 15-month trial with 4-month run-in (1.79 points). To detect a 50% slowing in progression under the standard design (i.e., a 0.59 point slowing), a future trial would require 3.4 times more subjects than would be required to detect the comparable percent slowing (i.e., 0.90 points) with the run-in design.

Discussion

Assuming the improvement at first follow-up observed in this trial represents PEs, the rate of change from the second assessment forward is a more accurate representation of symptom progression in this population and is the appropriate reference point for describing treatment effects characterized as percent slowing of symptom progression; failure to accommodate this leads to an oversized clinical trial. We conclude that PEs are an important potential consideration when planning future trials.

Power analysis to detect treatment effects in longitudinal clinical trials for Alzheimer's disease

INTRODUCTION

Assessing cognitive and functional changes at the early stage of Alzheimer's disease (AD) and detecting treatment effects in clinical trials for early AD are challenging.

METHODS

Under the assumption that transformed versions of the Mini-Mental State Examination, the Clinical Dementia Rating Scale-Sum of Boxes, and the Alzheimer's Disease Assessment Scale-Cognitive Subscale tests'/components' scores are from a multivariate linear mixed-effects model, we calculated the sample sizes required to detect treatment effects on the annual rates of change in these three components in clinical trials for participants with mild cognitive impairment.

RESULTS

Our results suggest that a large number of participants would be required to detect a clinically meaningful treatment effect in a population with preclinical or prodromal Alzheimer's disease. We found that the transformed Mini-Mental State Examination is more sensitive for detecting treatment effects in early AD than the transformed Clinical Dementia Rating Scale-Sum of Boxes and Alzheimer's Disease Assessment Scale-Cognitive Subscale. The use of optimal weights to construct powerful test statistics or sensitive composite scores/endpoints can reduce the required sample sizes needed for clinical trials.

CONCLUSION

Consideration of the multivariate/joint distribution of components' scores rather than the distribution of a single composite score when designing clinical trials can lead to an increase in power and reduced sample sizes for detecting treatment effects in clinical trials for early AD.