Table 2.

Performance of various tests in analyzing sample data

MethodEstimated difference95% CI for differencep value for test of no difference
1. Two-sample t test (individual observations)0.517(0.230, 0.805)0.0005
2. Wilcoxon (individual observations)0.486(0.200, 0.769)0.0013
3. Two-sample t test (means)0.476(−0.318, 1.269)0.2235
4. Wilcoxon (means)0.425(−0.387, 1.286)0.2475
5. LMM0.476(−0.322, 1.275)0.2260
6. GEE0.476(−0.228, 1.180)0.1850
7. Rank-sum test for clustered data (Datta and Satten)0.2015
  • Table 2 summarizes the results of applying the above methods to the dataset pictured in Fig. 1. We tested the null hypothesis that there was no significant difference between group 1 and group 2 observations. Methods 1 (t test) and 2 (Wilcoxon) were conducted on individual observations, thereby ignoring clustering. Methods 3 and 4 were applied after the data were reduced to the means of each cluster, thereby eliminating clustering. Unlike the t and Wilcoxon tests, LMM, GEE, and the rank-sum test of Datta and Satten (2005) are able to explicitly account for clustering. CI, Confidence interval.