Steven Edland - US grants

The Data Management and Statistics Core provide statistical consulting, data management and information transfer resources to enhance the quality of Alzheimer's disease research conducted by the ADRC investigators. The specific aims are: 1. To provide database support to the Cores and Projects. To coordinate the entry, quality control, management and analysis of data generated by the enrollment, evaluation, and follow up of outpatient and control subjects recruited by the Centers Clinical Core. Similarly, to coordinate the entry, quality control, management and analysis of data generated by the Center's Neuropathology Core. 2. To prepare the ADRC database for routine submission to the National Alzheimer's Coordinating Center (NACC). 3. To provide statistical design and analysis consultation services to ADRC investigators. Design consultation power calculations and statistical analysis planning. Data analysis support ranges from providing simple descriptive statistics to the conduct of complex multivariate statistical analyses. 4. To develop new statistical methodology focused on application to Alzheimer's disease data. 5. To educate investigators, trainees and junior faculty in the principles and use of statistical analysis methodologies. RELEVANCE (See instructions): AD affects millions of Americans with its risk growing exponentially with age. The AD Centers Program fosters research related to AD and non-AD dementias. The ADRC will enhance the performance of innovative research on AD and related topics, including research that may lead to potential disease modifying therapies or behavioral treatments. It will provide an environment and core resources to enhance research, foster professional and community training, and coordinate interdisciplinary research.

DESCRIPTION (provided by principal investigator): The Food and Drug Administration has recently relaxed its rules governing statistical analysis plans for clinical trials of investigational new drugs to allow rate of change analysis. Specifically, analyses that estimate slope using all longitudinal data points, not just first and last, have now been reported for investigational new drugs to treat Alzheimers disease. A representative example is the recently reported Alzheimers disease treatment trial of bapineuzumab, which tested the effect of treatment on rate of decline on a global cognitive assessment scale. The analysis used a modied intent to treat (MITT) sample, restricting the analysis to subjects with at least one follow-up observation. Sample size considerations are poorly developed for this analysis plan, and the many assumptions implicit in this analysis have never been formally tested with representative Alzheimers disease data. Two unique data resources are available to explore these issues. One data resource is the accumulated clinical trial data from the Alzheimers Disease Cooperative Study (ADCS). The ADCS, which performs clinical trials of non-licensible treatments not under the purview of the FDA, has over ten years of experience with the rate of change analysis. The second data resource is the Alzheimers Disease Neuroimaging Initiative (ADNI) cohort, which was created expressly for the purpose of informing the design of future Alzheimers disease treatment trials and secondary prevention trials of mild cognitively impaired (MCI) subjects. With this in mind, we propose the following Specific Aims: Specific Aim 1. Using data from the Alzheimers Disease Cooperative Study (ADCS) and the Alzheimers Disease Neuroimaging Initiative (ADNI), to accurately determine statistical sample size requirements for Alzheimer treatment trials and secondary prevention (MCI) trials using standard clinical and neuropsychometric outcomes. Specific Aim 2. Using data from ADCS and ADNI, to describe the potential relative utility of various biomarkers proposed as surrogate outcome measures for Alzheimer treatment trials and secondary prevention (MCI) trials. Specific Aim 3. Using data from ADCS and ADNI, to test the validity of the statistical assumptions implicit in the MITT rate of change analysis, specically, the assumption of random dropout and the assumption of linear progression over time. Specific Aim 4. Using data from ADCS and ADNI, to explore the performance of standard MITT analyses using mixed effects models or generalized estimating equations relative to alternative methods that are robust to failures of the random dropout assumptions. PUBLIC HEALTH RELEVANCE: Clinical trials that are too small are noninformative and prone to 'false negatives', that is, erroneous conclusions that an effective treatment is ineffective. Trials that are too large incur unnecessary study subject burden and cost, a not inconsequential concern, as Alzheimer treatment trials require hundreds of subjects and millions of dollars to perform. We will use accumulating data from NIH sponsored cohort studies and treatment trials to determine optimal samples size for future clinical trials.

CORE C: DATA MANAGEMENT AND STATISTICAL - ABSTRACT The Data Management and Statistical Core provide statistical consulting, data management and information transfer resources to enhance the quality of Alzheimer's disease research conducted by the ADRC investigators. The specific aims are: 1. To provide database support to the Cores and Projects. To coordinate the entry, quality control, management and analysis of data generated by the enrollment, evaluation, and follow up of outpatient and control subjects recruited by the Center's Clinical Core. Similarly, to coordinate the entry, quality control, management and analysis of data generated by the Center's Neuropathology Core and neuroimaging data generated by the Clinical Core and collaborating investigators. 2. To prepare the ADRC database for routine submission to the National Alzheimer's Coordinating Center (NACC), support new NACC procedures and initiatives, and resolve NACC queries. 3. To provide statistical design and analysis consultation services to ADRC investigators. Design consultation includes power calculations and statistical analysis planning. Data analysis support ranges from providing simple descriptive statistics to the conduct of complex multivariate statistical analyses. 4. To develop new statistical methodology focused on application to Alzheimer's disease data. 5. To educate investigators, trainees and junior faculty in the principles and use of statistical analysis methodologies.

ABSTRACT This grant will develop statistical methods for deriving optimal endpoints for clinical trials and longitudinal cohort studies of cognitive aging, mild cognitive impairment, and Alzheimer's disease, and will publish new statistically efficient clinical trial outcome measures derived using these methods. Methods to be developed in this grant will substantially improve the efficiency of clinical trials, reducing the cost and increasing the probability that effective treatments will be identified. The Specific Aims are: Specific Aim 1. To derive and apply methods for optimal calculation of instrument total scores. This is an extension of our earlier work using Item Response Theory to find the optimal scoring of items when calculating an instrument total score. Our earlier work trained the rescoring algorithm on cross-sectional data. The extension will be to train on longitudinal data. We have pilot data demonstrating a 25% reduction in required sample size for select instruments by the proposed method. Specific Aim 2. To derive and apply methods for optimal construction of composite scales. Composite scales combining cognitive and functional measures promise to dramatically improve the efficiency of clinical trials of mild cognitive impairment and is an area of active research. We have derived an optimal formula and can demonstrate superior performance relative to current methods with real data and simulations. Specific Aim 3. To demonstrate de novo outcome measure development by applying the methods developed in Aims 1 and 2 to a different but related progressive disease, frontotemporal dementia (FTD). This exercise is intended to demonstrate the generalizability of our methods to other disease areas. Moreover, software developed in performance of this grant will be posted as the methods are published and will be applicable to instrument development for any chronic disease for which quantitative traits are used as endpoints for clinical trials. This grant is entirely motivated by the observation that clinical trials of chronic progressive disease are prohibitively expensive. In Alzheimer's disease research this has limited our ability to test new treatments and find a cure for the disease. For less common diseases such as FTD and progressive supranuclear palsy the need for more efficient endpoints is even more pressing, as the availability of study subjects for clinical trials further limits our ability to test treatments. Every subject enrolled in a clinical trial is a precious resource. This grant is intended to advance methods to optimally utilize all information obtained from subjects enrolled in clinical trials and increase the probability that effective treatments are identified.

? DESCRIPTION (provided by applicant): In Response to PA-13-168 (Secondary Analyses of Existing Data Sets and Stored Biospecimens To Address Clinical Aging Research Questions (R01)). Despite increasing sophistication in the application of biomarkers to the study of mild forms of cognitive impairment (MCI), sophistication in profiling cognition has not been commensurate. Criteria for MCI diagnosis in many large-scale studies rely on a single cognitive test score, screening measures, rating scales and clinical judgment, resulting in coarse characterizations of the types or severity of MCI being studied despite the availability of rich neuropsychological data from such studies. We propose to apply our novel actuarial neuropsychological and statistical methods to more accurately diagnose MCI and predict its progression. Applying these methods to large-scale existing open source (ADCS donepezil trial, ADNI, NACC/UDS) and institutional (FHS, MCSA, WHICAP) datasets will uncover stronger relationships between biomarkers, cognition, pathology, and progression rates, and will result in stronger treatment effects in clinical trials aimed at MCI. Our methods will improve effect sizes that inform power analyses for clinical trials and reduce the number of patients needed for such trials. Finally, our methods will be implemented to improve the NIA-AA operational definition of 'subtle cognitive decline' in Preclinical AD. These improvements will have important impacts on prospective design of future biomarker and clinical trial studies. Specific aims: Aim 1. Actuarial neuropsychological criteria for MCI diagnosis will better specify cognitive phenotypes as well as identify possible diagnostic errors from conventional criteria; removal of the resultant false positive (i.e., cognitively normal via neuropsychological criteria) cases and addition of false negative (i.e., `missed') cases will strengthen biomarker and trial findings from several large-scale studies. Aim 2. Empirically derived MCI diagnostic criteria will result in more efficient tril and study designs (i.e., studies that need fewer subjects) compared to conventional MCI criteria. Aim 3. An operational definition of subtle cognitive decline based on extensions of the above neuropsychological MCI criteria will improve characterization of NIA-AA criteria for Preclinical AD. Aim 4. In exploratory analyses, we will use novel computational tools to harmonize and combine 1) cognitive and 2) multi-marker profiles predictive of progression/pathology across multiple datasets. Demonstrations of improvement in diagnostic precision in MCI and Preclinical AD will have an important impact on prospective design of future studies of genetics, biomarkers, treatments and ultimately prevention. If successful, we will be able to more clearly model effects of biomarkers changes and neurodegeneration, together with factors such as age and comorbidities, on specific profiles and trajectories of cognitive decline.

ABSTRACT The proposed UC San Diego MADURA (Mentorship for Advancing Diversity in Undergraduate Research on Aging) Program responds to the NIA ADAR R25 Training Program Announcement, to offer under- represented Hispanic/Latino undergraduates tailored, longitudinal mentorship, training and work experiences with researchers and clinicians focused on aging and Alzheimer?s disease. It will provide effective, sustained academic and social support, skills training and supervision, to foster academic success and retention over the near term, and subsequent increased rates of application for aging-related graduate training or employment, thereby improving inclusion in the field. The MADURA project is designed to achieve these goals through a multi-component program comprised of: career-relevant Individual, Paid Aging and Alzheimer?s Disease Research-related Internship Placements with researcher/clinician mentors (8hours/week); Paid Weekly Group Mentorship/Training Meetings, facilitated by a team of doctoral level trainers and research faculty (2 hours per week); integrated, tailored Professional Development Experiences (some with additional funding support); Guided Outreach Experiences for a partner high school that serves potential first generation college attendees; and formal curriculum and process development activities and rigorous evaluation, enabling continuous quality improvements and future dissemination. UC San Diego is an emerging Hispanic Serving Institution with a deep field of diversity- promoting academic support and Hispanic/Latino student groups, centers and services which welcome collaboration with the MADURA Program. The MADURA Program is innovative in depth, comprehensiveness and integration of its evidence-based supportive elements: student pay, broad array of experiential placements, full integration of weekly Group Mentorship and tailored Training (provided by skilled aging research facilitators from similar cultural backgrounds), co-occurring peer mentorship and support, and finally, its fidelity to rigorous evaluation and dissemination of results and materials. MADURA is positioned for success, given the convergence of experienced program leadership, strong program development and evaluation teams, pay for students who must earn income in order to stay in school, exemplary willing advisors and complimentary training activity partners, and existing linkages with Hispanic/Latino student networks. The carefully conceptualized MADURA Program brings together the leadership, advisors, training and placement experiences to successfully promote diversity in Aging/Alzheimer?s disease MSTEM careers for participating Hispanic/Latino undergraduates, within the nurturing context of a University energized around improving diversity and inclusion.