Most Likely Separation of Intensity and Warping Effects in Image Registration

This paper introduces a class of mixed-effects models for joint modeling of spatially correlated intensity variation and warping variation in 2D images. Spatially correlated intensity variation and warp variation are modeled as random effects, resulting in a nonlinear mixed-effects model that enables simultaneous estimation of template and model parameters by optimization of the likelihood function. We propose an algorithm for fitting the model which alternates estimation of variance parameters and image registration. This approach avoids the potential estimation bias in the template estimate that arises when treating registration as a preprocessing step. We apply the model to datasets of facial images and 2D brain magnetic resonance images to illustrate the simultaneous estimation and prediction of intensity and warp effects

Insights into globalization: comparison of patient characteristics and disease progression among geographic regions in a multinational Alzheimer’s disease clinical program

Background: Globalization of clinical trials has important consequences for trial planning and interpretation. This study investigated heterogeneity in patient characteristics and outcomes among world regions in the global idalopirdine Phase 3 clinical program. Methods: Data were pooled from three 24-week randomized controlled trials in patients aged ≥ 50 years with mild-to-moderate Alzheimer’s disease (AD) (n = 2506). Patients received idalopirdine (10, 30, or 60 mg/day) or placebo, added to cholinesterase inhibitor treatment. Patients were categorized into the following regions: Eastern Europe/Turkey (n = 759), Western Europe/Israel (n = 709), USA/Canada (n = 444), South America/Mexico (n = 361), Asia (n = 134), and Australia/South Africa (n = 99). For each region, operational characteristics, baseline demographic and clinical characteristics, adverse events, and mean change from baseline to week 24 in clinical rating scale scores (placebo group only) were summarized using descriptive statistics. Results: Completion rates were 0.86–0.90 in all regions. Heterogeneity among global regions was evident. Protocol deviations were twice as common in South America/Mexico as in USA/Canada (2.64 vs 1.35 per patient screened). Educational level ranged from 9.2 years in South America/Mexico to 13.4 years in USA/Canada. APOE ε4 carriage was 80.6% in Australia/South Africa, 63.1% in Western Europe/Israel, and < 60% in other regions. Screening Mini-Mental State Examination scores were higher in Eastern Europe/Turkey (18.0) and USA/Canada (17.5) than in other regions (16.9–17.1). Baseline AD Assessment Scale-Cognitive subscale (ADAS-Cog) scores ranged from 24.3 in USA/Canada to 27.2 in South America/Mexico. Baseline AD Cooperative Study - Activities of Daily Living, 23-item version (ADCS-ADL23) scores ranged from 58.5 in USA/Canada to 53.5 in Eastern Europe/Turkey. In the placebo group, adverse events were 1.6–1.7 times more common in Western Europe/Israel, USA/Canada, and Australia/South Africa than in Eastern Europe/Turkey. On the ADAS-Cog, Australia/South Africa and Western Europe/Israel showed the most worsening among patients receiving placebo (1.56 and 1.40 points, respectively), whereas South America/Mexico showed an improvement (−0.71 points). All regions worsened on the ADCS-ADL23, from −3.21 points in Western Europe/Israel to −0.59 points in Eastern Europe/Turkey. Conclusions: Regional heterogeneity - in terms of study conduct, patient characteristics, and outcomes-exists, and should be accounted for, when planning and conducting multinational AD clinical trials. Trial registration ClinicalTrials.gov, NCT01955161. Registered on 27 September 2013. ClinicalTrials.gov, NCT02006641. Registered on 5 December 2013. ClinicalTrials.gov, NCT02006654. Registered on 5 December 2013

Progression models for repeated measures : Estimating novel treatment effects in progressive diseases

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

Statistical Disease Progression Modeling in Alzheimer Disease

Background: The characterizing symptom of Alzheimer disease (AD) is cognitive deterioration. While much recent work has focused on defining AD as a biological construct, most patients are still diagnosed, staged, and treated based on their cognitive symptoms. But the cognitive capability of a patient at any time throughout this deterioration reflects not only the disease state, but also the effect of the cognitive decline on the patient's pre-disease cognitive capability. Patients with high pre-disease cognitive capabilities tend to score better on cognitive tests that are sensitive early in disease relative to patients with low pre-disease cognitive capabilities at a similar disease stage. Thus, a single assessment with a cognitive test is often not adequate for determining the stage of an AD patient. Repeated evaluation of patients' cognition over time may improve the ability to stage AD patients, and such longitudinal assessments in combinations with biomarker assessments can help elucidate the time dynamics of biomarkers. In turn, this can potentially lead to identification of markers that are predictive of disease stage and future cognitive decline, possibly before any cognitive deficit is measurable. Methods and Findings: This article presents a class of statistical disease progression models and applies them to longitudinal cognitive scores. These non-linear mixed-effects disease progression models explicitly model disease stage, baseline cognition, and the patients' individual changes in cognitive ability as latent variables. Maximum-likelihood estimation in these models induces a data-driven criterion for separating disease progression and baseline cognition. Applied to data from the Alzheimer's Disease Neuroimaging Initiative, the model estimated a timeline of cognitive decline that spans ~15 years from the earliest subjective cognitive deficits to severe AD dementia. Subsequent analyses demonstrated how direct modeling of latent factors that modify the observed data patterns provides a scaffold for understanding disease progression, biomarkers, and treatment effects along the continuous time progression of disease. Conclusions: The presented framework enables direct interpretations of factors that modify cognitive decline. The results give new insights to the value of biomarkers for staging patients and suggest alternative explanations for previous findings related to accelerated cognitive decline among highly educated patients and patients on symptomatic treatments

Approximate inference for spatial functional data on massively parallel processors

With continually increasing data sizes, the relevance of the big n prob-lem of classical likelihood approaches is greater than ever. The functional mixed-e↵ects model is a well established class of models for analyzing functional data. Spatial functional data in a mixed-e↵ects setting is con-sidered, and so-called operator approximations for doing inference in the resulting models are presented. These approximations embed observations in function space, transferring likelihood calculations to the functional domain. The resulting approximated problems are naturally parallel and can be solved in linear time. An extremely ecient GPU implementation is presented, and the proposed methods are illustrated by conducting a classical statistical analysis of 2D chromatography data consisting of more than 140 million spatially correlated observation points.1