Limitations of clinical trial sample size estimate by subtraction of two measurements

Autor: Yinghua Chen, Alzheimer’s Disease Neuroimaging Initiative, Yi Su, Eric M. Reiman, Chengjie Xiong, Rong Pan, Danielle J Harvey, Li Yao, Xiaojuan Guo, Kewei Chen
Rok vydání: 2021
Předmět:
Statistics and Probability
Aging
Epidemiology
Statistics & Probability
Variable time
Monte Carlo method
Bioengineering
Neuroimaging
Neurodegenerative
Alzheimer's Disease
Article
law.invention
Mathematical equations
two time point measurement
Randomized controlled trial
Alzheimer Disease
law
Statistics
Acquired Cognitive Impairment
Humans
Mathematics
Observational error
sample size estimation
Alzheimer's Disease Neuroimaging Initiative
Neurosciences
Subtraction
Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD)
randomized clinical trial
Magnetic Resonance Imaging
Brain Disorders
8.4 Research design and methodologies (health services)
Clinical trial
Research Design
Sample size determination
Sample Size
Public Health and Health Services
linear mixed effects model
subtraction
Biomedical Imaging
Dementia
Health and social care services research
Zdroj: Stat Med
Statistics in medicine, vol 41, iss 7
ISSN: 1097-0258
0277-6715
Popis: In planning randomized clinical trials (RCTs) for diseases such as Alzheimer's disease (AD), researchers frequently rely on the use of existing data obtained from only two time points to estimate sample size via the subtraction of baseline from follow-up measurements in each subject. However, the inadequacy of this method has not been reported. The aim of this study is to discuss the limitation of sample size estimation based on the subtraction of available data from only two time points for RCTs. Mathematical equations are derived to demonstrate the condition under which the obtained data pairs with variable time intervals could be used to adequately estimate sample size. The MRI-based hippocampal volume measurements from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Monte Carlo simulations (MCS) were used to illustrate the existing bias and variability of estimates. MCS results support the theoretically derived condition under which the subtraction approach may work. MCS also show the systematically under- or over-estimated sample sizes by up to 32.27 % bias. Not used properly, such subtraction approach outputs the same sample size regardless of trial durations partly due to the way measurement errors are handled. Estimating sample size by subtracting two measurements should be treated with caution. Such estimates can be biased, the magnitude of which depends on the planned RCT duration. To estimate sample sizes, we recommend using more than two measurements and more comprehensive approaches such as linear mixed effect models.
Databáze: OpenAIRE
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