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Alzheimer's Association International Conference (AAIC) · 2024:Poster Tue-17 · Hendrix, Dickson et al.
Companies recently identified clinical study sites engaging in fraudulent practices, reducing success in Alzheimer's disease (AD) clinical trials. Successful trials are reliable…
When the sample size a conventional design demands is larger than the trial you can run, a Global Statistical Test lets the clinical-endpoint hypotheses be tested anyway. The paper sets out when GST applies, how it is pre-specified, and how it has been received in review.
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2009 · Dissertation
Abstract: In recent years population-based association studies have been advocated as the most powerful method of discovering genetic loci that are associated with heritable traits, particularly for complex traits that are likely caused by a variety of factors including environmental effects and multiple genetic loci. Genome-wide association studies (GWAS) have already yielded a large number of such associations, but there is growing concern that the results of these studies are not explaining as much genetic variation as they were expected to. Chapter 2 discusses tagging and imputation to leverage the information available on commercial genotyping chips to make inferences about variants found in large reference samples such as those made available by the International HapMap Consortium. Transferability of multi-marker tagging is assessed. Tagging and imputation are compared, and a method of using tagging to select a reduced tag set to be used for imputation. Chapter 3 details how multiple low frequency causal variants can create synthetic associations among more common variants and may be responsible for many of the genome-wide associations that have already been observed. Examples of synthetic associations are demonstrated in congenital deafness and sickle-cell anemia. Chapter 4 examines issues related to combining samples of diverse genetic ancestry for analysis in genetic association studies. Through simulation it is shown that type I error can be controlled and power increased using statistical methods to account for differences in populations.
2009 · Dissertation
Abstract: Recruitment for clinical trials is undergoing a major transition, from trial centers concentrated in North America and Western Europe to a much greater emphasis on Eastern Europe, South and East Asia, and Latin America. This shift challenges traditional approaches to genetic association studies, in which studies have been designed around populations of relatively common ancestry. There is particular concern about the potential impact of genetic substructure, stratification and heterogeneity on the analysis of diverse samples. Although methods such as those implemented in STRUCTURE and EIGENSTRAT have been developed to assess and address the problems arising from the analysis of samples containing individuals from multiple populations, we have an insufficient understanding of how population-dependent prevalence, allele frequency, penetrance, and LD affect study type I error and power. Through simulation we explore the effects of heterogeneity in these population parameters on the analysis with a variety of statistical models. We find that type 1 error can be controlled when combining samples. Methods using combined samples outperform methods that rely on analyzing samples separately in ~70% of the conditions considered. The models with the highest overall power were those that assumed a main effect for population with no difference in genetic effect between populations. We conclude that the same information that has been used to exclude data to minimize diversity would be better employed as a statistical covariate in an inclusive analysis.
2009 · Dissertation
Abstract: Tagging and imputation have been developed to make inferences about unmeasured genetic variants. While these methods attempt to address the same problem, their ability to make these inferences has never been directly compared. We compare the efficiency of the inference of tagging using pairwise and multi-marker tags and imputation. Multi-marker tagging is shown to transfer well from a reference population to genetically similar populations in European and African samples, though pairwise tags maintain their predictive power better than multi-marker tags in independent samples. Imputation makes more accurate inferences than tagging, especially compared to multi-marker tags. Imputation is less efficient when imputing SNPs with no tags than for imputing SNPs that have tags, so a method is proposed to use tagging to select a tag set for imputation using lower thresholds and impute the remaining SNPs with this tag set. This method is shown to produce accurate results with fewer SNPs than traditional tag selection methods.
2009 · Alzheimer's & Dementia
Background: Recently, there has been substantial interest in establishing methods for identifying a disease modifying therapy for Alzheimer's disease. The concept of Disease Modification has been approached in many different ways including cross-over study designs (randomized withdrawal and staggered start), use of biomarkers or imaging outcomes, measuring clinically relevant milestones, comparing slopes of decline and comparing adjusted slopes (Natural History Staggered Start analysis - NHSS). These methods are able to distinguish between different patterns of clinical response. Methods: A definition of Disease Modification is proposed that defines a substantially different pattern of clinical response than that of a symptomatic therapy. Additional definitions are proposed to separate out long term and short term symptomatic responses, and permanent and temporary disease modification effects. Clinical approaches are discussed in the context of supporting these distinct types of mechanisms. Results: The Randomized Withdrawal and Staggered Start designs can separate between disease modifying effects and symptomatic effects. The Natural History Staggered Start analysis can also make this distinction without the difficulties of a two-phase study such as: the potential bias and loss of power associated with a high dropout rate, ethical concerns related to removing a potentially efficacious treatment and the long treatment duration required for these two phase studies. Unadjusted slopes analysis and use of clinically relevant milestones can identify long lasting effects without distinguishing between disease modification and symptomatic effects. Use of biomarkers and imaging outcomes can detect effects on the underlying disease process, but will need additional validation in order to demonstrate that these effects are due to disease modification. Conclusions: The pathological process of Alzheimer's disease is not understood well enough to be directly measurable with biomarkers or imaging outcomes, and is not tied to clinical outcomes except through the symptomatology. In this setting, demonstration of disease modification should rely on clinical evidence with support from several sources including: pre-clinical data supporting a disease modifying mechanism of action, biomarkers connecting that mechanism to the clinical efficacy, and imaging outcomes supporting a structural change consistent with modification of the underlying disease.
2009 · The journal of nutrition, health & aging
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