2.5. Statistical Methods
RECRUITMENT
The power calculation was based on the data from Dittrich et al. (2015) [The global power was calculated based on a linear model (analysis of variance) to compare the EPA concentrations at the end of the study for the four groups. The power was 91.1%, assuming a standard deviation of 0.35 and a significance level of 5%.Contrasts are considered in the linear model mentioned above for power calculation for single comparisons. The most considerable difference in EPA concentration is expected between the HLA and LLA groups or the LLA and MF groups. Based on these data, a group size of 37 subjects has 81% power to achieve an EPA difference of 0.2% FAME between the LLA and control groups (EPA difference between μ1 = 1.2% FAME; μ2 = 1.3% FAME), assuming that the standard deviation is 0.3. In addition, a group size of 37 subjects has 99% power to achieve an EPA difference of 0.3% FAME between the HLA and LLA groups (EPA difference between μ1 = 1.1% FAME; μ2 = 1.4% FAME), assuming that the standard deviation is 0.3. The number of cases was planned using SAS proc power version 9.4 (SAS Institute, Cary, NC, United States). Based on the power calculation with the described data, 37 subjects per group were to be enrolled. Due to the lack of availability of suitable subjects in the recruitment period, only 134 instead of the planned 148 subjects could be included (For assigning the participants to the four groups, a randomization list was generated with the statistical software R version 3.5.2 (The R Foundation for Statistical Computing, Vienna, Austria).The primary endpoint of the study was the change in EPA (% FAME) in erythrocyte lipids. Secondary endpoints were the effects on blood lipids, markers of glucose metabolism, micronutrient status, and anthropometric parameters.For statistical analyses, the statistical software IBM SPSS Statistics version 28.0.1.1 (14) (IBM Germany, Ehningen, Germany) was used. If the data followed a normal distribution, this was tested with the Shapiro–Wilk test. Differences between groups were assessed using one-way ANOVA for normally distributed variables or the Kruskal–Wallis test for not normally distributed variables. Differences within groups comparing baseline and weeks 4, 8, and 12 were assessed using ANOVA for repeated measurements for normally distributed variables or the Friedman test for not normally distributed variables. Fisher’s least significant difference test was performed as a post-hoc test and calculated For calculations of the sum of We divided our study collective according to EPA status at baseline (<0.9% FAME vs. ≥0.9% FAME) and LA change (% FAME) throughout the study period (<1.5% FAME vs. ≥1.5% FAME) to evaluate the influence of these factors on the conversion of ALA into its long-chain metabolites. The limits represent the cut-off values of each 50% of our participants to compare groups of equal size, as no reference values are available.