62 Chapter 3 Patient characteristics Patient characteristics were obtained from the semi-structured interview and electronic patient files (HiX, Chipsoft, The Netherlands). This included sex, age at inclusion, level of education according to the Verhage criteria19, handedness, KPS4, primary tumor, presence of extracranial metastases, time since BMs diagnosis, previous anti-tumor therapy, dexamethasone dose 1-5 days prior to radiotherapy, and symptoms at BMs diagnosis. As part of standard medical care, the preradiotherapy MRI scans of each patient were evaluated to determine the number of BMs, hemisphere, and lobe involvement. Statistical analyses Analyses were performed using SPSS (IBM SPSS Statistics, 25.0.0). Statistical significance was set at p<0.05, adjusted for multiple comparisons when necessary. Cognitive test scores were analyzed using different methods: 1. Group-level: comparison of mean Z-score of the sample with normative performance for each domain (“domain-level”) and each task (“task-level”) using one-sample t-tests (with the null hypothesis Z=0, meaning no difference between patients and expected normative performance) or Wilcoxon-signed rank tests, depending on normality of data distribution. 2. Individual-level: the percentage of patients with test performance below the impairment threshold (Z≤-1.5) was calculated for each domain (“domain-level”) and each task (“task-level). To assess the relationship between subjective and objective cognitive performance, subjective complaints were compared between patients with versus without impairment on the domain-level using chi-square tests and Mann-Whitney U tests for categorical and continuous data, respectively. To assess the influence of stress on cognitive performance, correlation analyses were performed between stress and domain-level cognitive performance (see Supplementary Results). Additionally, the domainlevel impairments were descriptively compared between the comprehensive and the core battery. 3. Exploratory cluster analysis: with a data-driven approach patients were clustered based on similarities in deficits at the domain-level using Ward’s linkage with squared Euclidean distance. The number of distinguishable clusters was selected by visual inspection of the dendrogram and confirmed by discriminant function analysis. As cluster analysis uses complete cases, only patients with data for all included cognitive domains were considered. To assess how domainlevel deficits differed across clusters and whether patient and/or clinical characteristics (see Supplementary Table 2 for specific variables) differed
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