Clinical Trial Design in Alzheimer’s Disease Research
Sponsored by Eisai Inc. and Biogen
The Alzheimer’s disease research and clinical trial landscape has never been so exciting. With the significant advancements seen in Alzheimer’s disease research over the last several years, understanding clinical trial design and impact on outcomes is critical when evaluating data outcomes. At their core, late-stage clinical trials are designed to evaluate the efficacy and safety of investigational therapies. However, clinical trial design can vary greatly and can be quite complex.
Sharon Cohen, M.D., FRCPC, Behavioral Neurologist and Medical Director of the Toronto Memory Program, David Weisman, M.D., Director of Clinical Research, Abington Neurological Associates and Michael Irizarry, M.D., MPH, Senior Vice President of Clinical Research and Deputy Chief Clinical Officer, Neurology, Eisai, share their perspective on Alzheimer’s disease clinical trial design and what those in the field need to know.
Designing a Well-Powered Trial
In order to design a well-powered trial that can determine the safety and efficacy of a treatment, you must consider several factors, including: the study objective, the number of patients needed, who the appropriate patients are, the duration of the trial, and how to handle variability. Each of these factors has the potential to significantly impact the trial results and how they are interpreted.
For example, in one Alzheimer’s disease (AD) trial, a fifth of the participants did not have the underlying disease pathology that the treatment was designed to target. This was not known at the time of patient enrollment and had significant impact on the analyses, potentially impacting the overall trial results.1
Equally important is understanding the nuances of the study population when evaluating clinical trial results. While there are general definitions of the stages of AD, this is a progressive condition marked by a continuum of disease characteristics that evolve over time within each stage.2
AD typically progresses in three stages: mild, moderate, and severe.2 To diagnose AD and determine the stage a person is in, physicians may use medical history, mental status tests, physical and neurological exams, diagnostic tests, and brain imaging.3 These tests are likewise utilized to determine eligibility for clinical trials.
In early AD, there are people who are just entering the earliest stages of mild cognitive impairment and those who are on the cusp of progressing to moderate AD.2 Patients at one end of this continuum may respond differently to a treatment than patients who are on the other end. So, when we evaluate that data, it is essential to consider measures that may reflect where the study population is on that spectrum.
Additionally, there is no typical AD patient – people living with AD can come from a variety of backgrounds and may have comorbidities, such as heart disease, type 2 diabetes, or obesity.4 Thus, it is necessary to consider the inclusion and exclusion criteria of a clinical trial to understand how the results may translate to patients in the real world.
The Impact of Missing Data
Another factor that can lead to loss of study power is missing data – a major source of missing data is when participants drop out of a trial.5 Some of the most common reasons a participant may discontinue participation in a trial include: difficulty tolerating treatment, perceived lack of efficacy of the treatment, or inability to continue attending medical appointments and clinical evaluations.5,6
The general rule of thumb is that a dropout rate of up to 20% during a trial is considered acceptable.7 A dropout or discontinuation rate above 20% can significantly impact the interpretability of the results. Clinical trials are randomized to balance the treatment and control groups as carefully as possible; this randomization may be jeopardized when key data are missing due to discontinuation.7
Substantial instances of missing data are a serious problem that undermine the scientific credibility of causal conclusions from clinical trials. Looking at the dropout rate helps us understand the quality of the data from a clinical trial.
The Challenges of Comparing Clinical Trial Results
As we continue to see progress in Alzheimer’s disease clinical research, at times we may see comparisons made across studies. It is important to remember, head-to-head studies are the only way in which we can directly compare the efficacy or safety of one medication to another.8
Clinical studies are very complex and include a vast number of variables. Even when trials sound like they have the same endpoints, they may be using different measures of that endpoint. Head-to-head studies, however, are designed specifically to eliminate variables that could impact study outcomes. As tempting as it may be to compare the results of two individual studies, that comparison does not yield clinically meaningful information.
As the focus of AD therapeutic development has shifted to the early stages of the disease, the clinical endpoints used in drug trials -- and how these might translate into clinical practice -- are of increasing importance.9 While a common endpoint in AD studies aims to measure cognitive decline, there is no commonly accepted tool to assess cognitive decline that can be applied to all clinical trials. Therefore, outcomes based on different measures cannot be compared.9
Recognizing Trial Complexities to Advance Understanding
The past year has been an incredibly exciting time for the AD community. With each new advancement there is more to uncover about AD and potential treatment, and it’s imperative to recognize the complexities of clinical trial design and implementation in this field.
Learn more about Eisai and its research into Alzheimer’s disease at http://us.eisai.com/
1. Banks, SJ, Qiu, Y, Fan, CC, et al. Enriching the design of Alzheimer's disease clinical trials: Application of the polygenic hazard score and composite outcome measures. Alzheimer's Dement. 2020; 6:e12071. https://doi.org/10.1002/trc2.12071
2. Alzheimer’s Association. 2023 Alzheimer's Disease Facts and Figures. Alzheimers Dement. 2023;19(4). Doi: 10.1002/alz.13016. https://www.alz.org/media/Documents/alzheimers-facts-and-figures.pdf
3. Alzheimer's Association. Medical Tests for Alzheimer's and Dementia Diagnosis. Alzheimer's Disease and Dementia. Accessed October 2, 2023. https://www.alz.org/alzheimers-dementia/diagnosis/medical_tests
4. Santiago JA, Potashkin JA. The Impact of Disease Comorbidities in Alzheimer's Disease. Front Aging Neurosci. 2021 Feb 12;13:631770. doi: 10.3389/fnagi.2021.631770. PMID: 33643025; PMCID: PMC7906983.
5. National Research Council (US) Panel on Handling Missing Data in Clinical Trials. The Prevention and Treatment of Missing Data in Clinical Trials. Washington (DC): National Academies Press (US); 2010. Available from: https://www.ncbi.nlm.nih.gov/books/NBK209904/ doi: 10.17226/12955
6. Crimin, K., Allen, P.J., Abba, I., Ahlberg, C., Benz, L., Lau, H., Liu, J., Melhem, F., Fisseha, N. and Florian, H. (2021), Identifying predictive factors of patient dropout in Alzheimer’s disease clinical trials. Alzheimer's Dement., 17: e052361. https://doi.org/10.1002/alz.052361
7. Catalogue of Bias Collaboration, Bankhead C, Aronson JK, Nunan D. Attrition bias. In: Catalogue Of Bias 2017. https://catalogofbias.org/biases/attrition-bias/
8. Kim H, Gurrin L, Ademi Z, Liew D. Overview of methods for comparing the efficacies of drugs in the absence of head-to-head clinical trial data. Br J Clin Pharmacol. 2014 Jan;77(1):116-21. doi: 10.1111/bcp.12150. PMID: 23617453; PMCID: PMC3895352.
9. Cohen S, Cummings J, Knox S, Potashman M, Harrison J. Clinical Trial Endpoints and Their Clinical Meaningfulness in Early Stages of Alzheimer's Disease. J Prev Alzheimers Dis. 2022;9(3):507-522. doi: 10.14283/jpad.2022.41. PMID: 35841252; PMCID: PMC9843702.
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