Statistical analysis
MCI, fatigue, ’
Statistical analyses were performed with the Statistical Package for the Social Sciences (SPSS) version 24.0 (IBM Corporate Headquarters, Armonk, New York) and R (R Core Team Independent-samples t-tests were conducted to investigate potential differences in mean raw MFI scores between the total group of patients with BM and Dutch controls at pre-GKRS, 6, 12, and 21 months (using controls’ first-assessment scores at each comparison). Glass’s delta effect sizes were calculated for each MFI scale. An effect size ≤ 0.49 was considered a ‘small’ effect, from 0.50 to 0.79 a ‘medium’ effect and ≥ 0.80 a ‘large’ effect (Cohen The nlme package (Pinheiro et al. Although LMM can deal with missing data, there is a risk of biased results if the amount of missing data is substantial, and the data is missing in a non-random pattern. In studies regarding patients with BM, a high dropout rate is common due to short survival (Leung et al. At the individual level, minimal clinically important (MCI) changes in fatigue between the above-mentioned intervals were calculated. Based on Purcell et al. (The survival Package (Therneau In a second model (model 2), the 5 continuous pre-GKRS raw fatigue subscale scores were separately added as predictor (5 different models in total) to the clinical base model. Potential sociodemographic covariates of fatigue were evaluated with Pearson correlation (age), point-biserial correlation (sex), and Spearman’s correlation (educational level). In case of a significant (In a third model (model 3), an MCI increase in fatigue between pre-GKRS and 3 months thereafter (0 = stable/declined fatigue versus 1 = increased fatigue) was added as a separate predictor to the clinical base model (5 different models in total), as our previous study (Verhaak et al. 