Measuring change in health-related quality of life: the impact of different analytical methods on the interpretation of treatment effects in glioma patients

Background. Different analytical methods may lead to different conclusions about the impact of treatment on health-related quality of life (HRQoL). This study aimed to examine 3 different methods to evaluate change in HRQoL and to study whether these methods result in different conclusions. Methods. HRQoL data from 15 randomized clinical trials were combined (CODAGLIO project). Change in HRQoL scores, measured with the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 and BN20 questionnaires, was analyzed in 3 ways: (1) at the group level, comparing mean changes in scale/item scores between treatment arms, (2) at the patient level per scale/item, calculating the percentage of patients that deteriorated, improved, or remained stable per scale/item, and (3) at the individual patient level, combining all scales/items. Results. Baseline and first follow-up HRQoL data were available for 3727 patients. At the group scale/item level, only the item "hair loss" showed a significant and clinically relevant change (ie, &amp;gt;= 10 points) over time, whereas change scores on the other scales/items were statistically significant only (all P &amp;lt;.001; range in change score, 0.1-6.2). Although a large proportion of patients had stable HRQoL over time (range, 27%-84%) on the patient level per scale/item, many patients deteriorated (range, 6%-43%) or improved (range, 8%-32%) on a specific scale/item. At the individual patient level, the majority of patients (86%) showed both deterioration and improvement, whereas only 1% remained stable on all scales. Conclusions. Different analytical methods of changes in HRQoL result in distinct conclusions of treatment effects, all of which may be relevant for informing clinical decision making.Funding Agencies|European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Group [1515]</p

El "Flos santorum con sus Ethimologías". Relaciones con la tradición manuscrita medieval

Background. Symptom management in glioma patients remains challenging, as patients suffer from various concurrently occurring symptoms. This study aimed to identify symptom clusters and examine the association between these symptom clusters and patients’ functioning. Methods. Data of the CODAGLIO project was used, including individual patient data from previously published international randomized controlled trials (RCTs) in glioma patients. Symptom prevalence and level of functioning were assessed with European Organisation for Research and Treatment of Cancer (EORTC) quality of life QLQC30 and QLQ-BN20 self-report questionnaires. Associations between symptoms were examined with Spearman correlation coefficients and partial correlation networks. Hierarchical cluster analyses were performed to identify symptom clusters. Multivariable regression analyses were performed to determine independent associations between the symptom clusters and functioning, adjusted for possible confounders. Results. Included in the analysis were 4307 newly diagnosed glioma patients from 11 RCTs who completed the EORTC questionnaires before randomization. Many patients (44%) suffered from 5–10 symptoms simultaneously. Four symptom clusters were identified: a motor cluster, a fatigue cluster, a pain cluster, and a gastrointestinal/seizures/bladder control cluster. Having symptoms in the motor cluster was associated with decreased (≥10 points difference) physical, role, an