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Purpose: As life expectancy continues to rise, post-treatment health-related quality of life (HRQoL) of breast cancer patients becomes increasingly important. This study examined the one-year longitudinal relation between axillary treatments and physical, psychosocial, and sexual wellbeing and arm symptoms. Methods: Women diagnosed with breast cancer who received different axillary treatments being axilla preserving surgery (APS) with or without axillary radiotherapy or full axillary lymph node dissection (ALND) with or without axillary radiotherapy were included. HRQoL was assessed at baseline, 6- and 12-months postoperatively using the BREAST-Q and the European Organization for Research and Treatment of Cancer QoL Questionnaire Breast Cancer Module (EORTC QLQ-BR23). Mixed regression models were constructed to assess the impact of axillary treatment on HRQoL. HRQoL at baseline was compared to HRQoL at 6- and at 12-months postoperatively. Results: In total, 552 patients were included in the mixed regressions models. Except for ALND with axillary radiotherapy, no significant differences in physical and psychosocial wellbeing were found. Physical wellbeing decreased significantly between baseline and 6- and 12-months postoperatively (p < 0.001, p = 0.035) and psychosocial wellbeing decreased significantly between baseline and 12 months postoperatively (p = 0.028) for ALND with axillary radiotherapy compared to APS alone. Arm symptoms increased significantly between baseline and 6 months and between baseline and 12 months postoperatively for APS with radiotherapy (12.71, 13.73) and for ALND with radiotherapy (13.93, 16.14), with the lowest increase in arm symptoms for ALND without radiotherapy (6.85, 7.66), compared to APS alone (p < 0.05). Conclusion: Physical and psychosocial wellbeing decreased significantly for ALND with radiotherapy compared to APS alone. Shared decision making and expectation management pre-treatment could be strengthened by discussing arm symptoms per axillary treatment with the patient. Plain English summary: Breast cancer is one of the most common cancers among women worldwide. Since the survival rates are improving, the health-related quality of life of breast cancer patients after treatment becomes more important. One of the most important factors in the prognosis of breast cancer patients is the presence of axillary metastasis, the spread of breast cancer to lymph nodes in the armpit. There are different axillary treatments which can all affect health-related quality of life negatively. Therefore, the aim of this study is to examine the effect of different axillary treatments on physical, psychosocial, and sexual wellbeing and arm symptoms of breast cancer patients. This study included women with breast cancer who received either axillary preserving surgery with or without axillary radiotherapy, or a full axillary lymph node dissection with or without axillary radiotherapy. health-related quality of life was assessed with validated breast cancer specific questionnaires at baseline, 6- and 12-months postoperatively. Patients who underwent axilla preserving surgery without axillary radiotherapy experienced significantly less decline in both physical and psychosocial wellbeing as well as significantly fewer increase in arm symptoms compared to patients who received the more extensive axillary treatments. The results of this study can be used to improve shared decision making and expectation management for future breast cancer patients.b [ABSTRACT FROM AUTHOR]

Purpose: Item response theory (IRT) models are an increasingly popular method choice for evaluating clinical outcome assessments (COAs) for use in clinical trials. Given common constraints in clinical trial design, such as limits on sample size and assessment lengths, the current study aimed to examine the appropriateness of commonly used polytomous IRT models, specifically the graded response model (GRM) and partial credit model (PCM), in the context of how they are frequently used for psychometric evaluation of COAs in clinical trials.Data were simulated under varying sample sizes, measure lengths, response category numbers, and slope strengths, as well as under conditions that violated some model assumptions, namely, unidimensionality and equality of item slopes. Model fit, detection of item local dependence, and detection of item misfit were all examined to identify conditions where one model may be preferable or results may contain a degree of bias.For unidimensional item sets and equal item slopes, the PCM and GRM performed similarly, and GRM performance remained consistent as slope variability increased. For not-unidimensional item sets, the PCM was somewhat more sensitive to this unidimensionality violation. Looking across conditions, the PCM did not demonstrate a clear advantage over the GRM for small sample sizes or shorter measure lengths.Overall, the GRM and the PCM each demonstrated advantages and disadvantages depending on underlying data conditions and the model outcome investigated. We recommend careful consideration of the known, or expected, data characteristics when choosing a model and interpreting its results.Methods: Item response theory (IRT) models are an increasingly popular method choice for evaluating clinical outcome assessments (COAs) for use in clinical trials. Given common constraints in clinical trial design, such as limits on sample size and assessment lengths, the current study aimed to examine the appropriateness of commonly used polytomous IRT models, specifically the graded response model (GRM) and partial credit model (PCM), in the context of how they are frequently used for psychometric evaluation of COAs in clinical trials.Data were simulated under varying sample sizes, measure lengths, response category numbers, and slope strengths, as well as under conditions that violated some model assumptions, namely, unidimensionality and equality of item slopes. Model fit, detection of item local dependence, and detection of item misfit were all examined to identify conditions where one model may be preferable or results may contain a degree of bias.For unidimensional item sets and equal item slopes, the PCM and GRM performed similarly, and GRM performance remained consistent as slope variability increased. For not-unidimensional item sets, the PCM was somewhat more sensitive to this unidimensionality violation. Looking across conditions, the PCM did not demonstrate a clear advantage over the GRM for small sample sizes or shorter measure lengths.Overall, the GRM and the PCM each demonstrated advantages and disadvantages depending on underlying data conditions and the model outcome investigated. We recommend careful consideration of the known, or expected, data characteristics when choosing a model and interpreting its results.Results: Item response theory (IRT) models are an increasingly popular method choice for evaluating clinical outcome assessments (COAs) for use in clinical trials. Given common constraints in clinical trial design, such as limits on sample size and assessment lengths, the current study aimed to examine the appropriateness of commonly used polytomous IRT models, specifically the graded response model (GRM) and partial credit model (PCM), in the context of how they are frequently used for psychometric evaluation of COAs in clinical trials.Data were simulated under varying sample sizes, measure lengths, response category numbers, and slope strengths, as well as under conditions that violated some model assumptions, namely, unidimensionality and equality of item slopes. Model fit, detection of item local dependence, and detection of item misfit were all examined to identify conditions where one model may be preferable or results may contain a degree of bias.For unidimensional item sets and equal item slopes, the PCM and GRM performed similarly, and GRM performance remained consistent as slope variability increased. For not-unidimensional item sets, the PCM was somewhat more sensitive to this unidimensionality violation. Looking across conditions, the PCM did not demonstrate a clear advantage over the GRM for small sample sizes or shorter measure lengths.Overall, the GRM and the PCM each demonstrated advantages and disadvantages depending on underlying data conditions and the model outcome investigated. We recommend careful consideration of the known, or expected, data characteristics when choosing a model and interpreting its results.Conclusion: Item response theory (IRT) models are an increasingly popular method choice for evaluating clinical outcome assessments (COAs) for use in clinical trials. Given common constraints in clinical trial design, such as limits on sample size and assessment lengths, the current study aimed to examine the appropriateness of commonly used polytomous IRT models, specifically the graded response model (GRM) and partial credit model (PCM), in the context of how they are frequently used for psychometric evaluation of COAs in clinical trials.Data were simulated under varying sample sizes, measure lengths, response category numbers, and slope strengths, as well as under conditions that violated some model assumptions, namely, unidimensionality and equality of item slopes. Model fit, detection of item local dependence, and detection of item misfit were all examined to identify conditions where one model may be preferable or results may contain a degree of bias.For unidimensional item sets and equal item slopes, the PCM and GRM performed similarly, and GRM performance remained consistent as slope variability increased. For not-unidimensional item sets, the PCM was somewhat more sensitive to this unidimensionality violation. Looking across conditions, the PCM did not demonstrate a clear advantage over the GRM for small sample sizes or shorter measure lengths.Overall, the GRM and the PCM each demonstrated advantages and disadvantages depending on underlying data conditions and the model outcome investigated. We recommend careful consideration of the known, or expected, data characteristics when choosing a model and interpreting its results. [ABSTRACT FROM AUTHOR]

Background: Patient-reported outcomes (PROs) are of increasing importance for health-care evaluations. However, the interpretation of change in PROs may be obfuscated due to changes in the meaning of the self-evaluation, i.e., response shift. Structural equation modeling (SEM) is the most widely used statistical approach for the investigation of response shift. Yet, non-technical descriptions of SEM for response shift investigation are lacking. Moreover, application of SEM is not straightforward and requires sequential decision-making practices that have not received much attention in the literature. Aims: To stimulate appropriate applications and interpretations of SEM for the investigation of response shift, the current paper aims to (1) provide an accessible description of the SEM operationalizations of change that are relevant for response shift investigation; (2) discuss practical considerations in applying SEM; and (3) provide guidelines and recommendations for researchers who want to use SEM for the investigation and interpretation of change and response shift in PROs. Conclusion: Appropriate applications and interpretations of SEM for the detection of response shift will help to improve our understanding of response shift phenomena and thus change in PROs. Better understanding of patients' perceived health trajectories will ultimately help to adopt more effective treatments and thus enhance patients' wellbeing. [ABSTRACT FROM AUTHOR]

Purpose: Patient-reported outcome (PRO) data from clinical trials can promote valuable patient-clinician communication and aid the decision-making process regarding treatment options. Despite these benefits, both patients and doctors face challenges in interpreting PRO scores. The purpose of this study was to identify best practices for presenting PRO results expressed as proportions of patients with changes from baseline (improved/stable/worsened) for use in patient educational materials and decision aids.Methods: We electronically surveyed adult cancer patients/survivors, oncology clinicians, and PRO researchers, and conducted one-on-one cognitive interviews with patients/survivors and clinicians. Participants saw clinical trial data comparing two treatments as proportions changed using three different formats: pie charts, bar graphs, icon arrays. Interpretation accuracy, clarity, and format preference were analyzed quantitatively and online survey comments and interviews, qualitatively.Results: The internet sample included 629 patients, 139 clinicians, and 249 researchers; 10 patients and 5 clinicians completed interviews. Bar graphs were less accurately interpreted than pie charts (OR 0.39; p < .0001) and icon arrays (OR 0.47; p < .0001). Bar graphs and icon arrays were less likely to be rated clear than pie charts (OR 0.37 and OR 0.18; both p < .0001). Qualitative data informed interpretation of these findings.Conclusions: For communicating PROs as proportions changed in patient educational materials and decision aids, these results support the use of pie charts. [ABSTRACT FROM AUTHOR]

Objectives: To examine the reliability, validity, and responsiveness of the Burden of Stroke Scale (BOSS).Study Design: A prospective cohort of stroke survivors were assessed at 3 (T1, T2), 6 (T3), and 12 (T4) months post onset (MPO) of stroke. Test-retest reliability was evaluated by calculating intra-class correlation coefficients (ICCs) between T1 and T2 scale scores. Convergent validity was evaluated by calculating Pearson product moment correlation coefficients between T1 BOSS, Stroke Impact Scale and MOS SF-36 scale scores assessing similar health concepts. Responsiveness was evaluated using a repeated measures ANOVA and the linear trend test in the full study sample and by calculating standardized response means (SRM) and a probability of change statistic, p between T1, T3, and T4 scale scores in participants demonstrating change on external criteria.Results: ICCs ranged from 0.78 to 0.94. Pearson coefficients ranged from -0.57 to -0.86. Moderate to high responsiveness estimates were obtained for 9 of 12 subscales with SRM (p) values ranging from .497 (.690) to 1.161 (.877). Composite scale SRM (p) values ranged from .661 (.746) to 1.192 (.883).Conclusions: The BOSS provided valid and reliable score estimates that were responsive to positive changes in functioning and well-being during the first year of recovery from stroke. [ABSTRACT FROM AUTHOR]