When undertaking clinical trials with limited sample size and repeated measures, the question arises: which is more advantageous—employing "Generalized Estimating Equations (GEE)" or opting for "non-parametric analysis"? We seek insights from experts to elucidate the respective strengths and limitations of each approach.
Hi,
GEE is preferred for efficiency and power, but non-parametric tests may be better suited if assumptions are violated with very small samples. Using simulations to compare approaches with the sample size and distribution of actual data can help inform the best analysis strategy.
Hope this helps.
Hsin-Yuan Chen
Thank you for your opinion.
I searched for two papers, both methods have their advantages and limitations. I'll list them as follows:
1. Analysis of Repeated Measurements Using Nonparametric Smoothers and Randomization Tests
https://www.jstor.org/stable/2531687
2. Improving the estimation of change from baseline in a continuous outcome measure in the clinical trial setting
Article Improving the estimation of change from baseline in a contin...
Welcome to put forward your professional opinions!
Hi,
These papers note the strengths and limitations of GEE and nonparametric methods for analysing repeated measures in clinical trials. Paper 2 emphasises GEE's data optimisation and power but notes its reliance on covariance assumptions, which is problematic in small samples. Paper 1 presents nonparametric analysis as less assumption-dependent and effective for detecting specific effects, though generally less efficient than GEE. While GEE excels in efficiency and power, nonparametric methods are preferable for small samples where assumptions may fail. Using simulations to mirror actual data can guide the choice between efficiency and power and assumption dependency.
Hope this helps.
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