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 |
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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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