Symmetrized percent change for treatment comparisons

Treatment effects are commonly analyzed by relating post-treatment (F) measurements with corresponding baseline (B) measurements. As effect measures, absolute difference (D) and percent change (PC) are used more than symmetrized percent change (SPC). However, all these measures alter the dependency structure with B and their distributions can differ from B and F. To examine their interpretability and relative performance, we considered simulations under independence and additive and multiplicative correlation structures for parametric and nonparametric analyses. Under independence, nonparametric analysis on F had the greatest power. Elsewhere, simple ANOVA on SPC had power equal to or greater than alternative analysis methods.