TY - JOUR

T1 - Statistical tests for homogeneity of variance for clinical trials and recommendations

AU - Zhou, Yuhang

AU - Zhu, Yiyang

AU - Wong, Weng Kee

N1 - Publisher Copyright:
© 2023 The Author(s)

PY - 2023/6

Y1 - 2023/6

N2 - In most clinical trials, the main interest is to test whether there are differences in the mean outcomes among the treatment groups. When the outcome is continuous, a common statistical test is a usual t-test for a two-group comparison. For more than 2 groups, an ANOVA setup is used and the test for equality for all groups is based on the F-distribution. A key assumption for these parametric tests is that data are normally, independently distributed and the response variances are equal. The robustness of these tests to the first two assumptions is quite well investigated, but the issues arising from heteroscedasticity are less studied. This paper reviews different methods for ascertaining homogeneity of variance across groups and investigates the consequences of heteroscedasticity on the tests. Simulations based on normal, heavy-tailed, and skewed normal data demonstrate that some of the less known methods, such as the Jackknife or Cochran's test, are quite effective in detecting differences in the variances.

AB - In most clinical trials, the main interest is to test whether there are differences in the mean outcomes among the treatment groups. When the outcome is continuous, a common statistical test is a usual t-test for a two-group comparison. For more than 2 groups, an ANOVA setup is used and the test for equality for all groups is based on the F-distribution. A key assumption for these parametric tests is that data are normally, independently distributed and the response variances are equal. The robustness of these tests to the first two assumptions is quite well investigated, but the issues arising from heteroscedasticity are less studied. This paper reviews different methods for ascertaining homogeneity of variance across groups and investigates the consequences of heteroscedasticity on the tests. Simulations based on normal, heavy-tailed, and skewed normal data demonstrate that some of the less known methods, such as the Jackknife or Cochran's test, are quite effective in detecting differences in the variances.

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U2 - 10.1016/j.conctc.2023.101119

DO - 10.1016/j.conctc.2023.101119

M3 - Review article

AN - SCOPUS:85153476712

SN - 2451-8654

VL - 33

JO - Contemporary Clinical Trials Communications

JF - Contemporary Clinical Trials Communications

M1 - 101119

ER -