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. 2015 Apr;24(2):255-72.
doi: 10.1177/0962280211416038. Epub 2011 Aug 24.

Measuring continuous baseline covariate imbalances in clinical trial data

Jody D Ciolino  1 , Reneé H Martin  2 , Wenle Zhao  2 , Michael D Hill  3 , Edward C Jauch  4 , Yuko Y Palesch  2
Affiliations

Measuring continuous baseline covariate imbalances in clinical trial data

Jody D Ciolino et al. Stat Methods Med Res. 2015 Apr.

Abstract

This paper presents and compares several methods of measuring continuous baseline covariate imbalance in clinical trial data. Simulations illustrate that though the t-test is an inappropriate method of assessing continuous baseline covariate imbalance, the test statistic itself is a robust measure in capturing imbalance in continuous covariate distributions. Guidelines to assess effects of imbalance on bias, type I error rate and power for hypothesis test for treatment effect on continuous outcomes are presented, and the benefit of covariate-adjusted analysis (ANCOVA) is also illustrated.

Keywords: baseline; clinical trial; covariate; imbalance.

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Figures

Figure 1
Figure 1
Relationships of imbalance Measure
Figure 2
Figure 2
These plots show the estimated (a) type I error rate, (b) power, and (c,d) bias for given levels of imbalance defined by the t-statistic comparing mean covariate values across two treatment groups. The t-statistic values greater than$>$ zero correspond to larger values of the covariate in the active treatment group. Plots (a) and (c) correspond to effects on these parameters for a covariate that is positively associated with outcome and plots (b) and (d) correspond to effects on these parameters for a covariate that is negatively associated with outcome.
Figure 3
Figure 3. Relationship between AMCOVA and Unadjustment Hyposthesis Tests, N = 1000
Figure 3 illustrates the relationship between ANCOVA and the standard unadjusted test for treatment effect using the p-values for each test. In each of the plots illustrated, there was no simulated treatment effect and thus the p-values for each type of analysis should be uniformly distributed on (0,1). When there is no association between the covariate and outcome, there is almost perfect correlation between the two tests' p-values (Pearson's r = 0.999). As figure 3 illustrates, as the level of correlation between the covariate and the outcome increases, the strength of the association between the two types of statistical analyses diminishes. Figure 3(d) suggests relatively weak and noisy association between the two statistical tests when the covariate X is strongly associated with outcome Y. Thus, it is not surprising
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References

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