Explaining quality of life with crisis theory

Based on the premises of crisis theory. we expected cancer patients in-crisis to report a poorer quality of life (QL) and cancer patients post-crisis to report a similar level of overall QL in comparison to healthy individuals. To explain these hypothesized findings, we expected the coping resources and strategies of patients in-crisis to be equally effective and those of patients post-crisis to be more effective as compared to those of healthy individuals. The sample consisted of: (a) 217 consecutive cancer patients in the acute phases of their illness (patients in-crisis) (b) 192 disease-free cancer patients (patients post-crisis): and (c) 201 randomly selected healthy individuals. Established measures of QL, self-esteem and neuroticism (coping resources) and coping behavior (coping strategies) were mailed. As expected. patients in-crisis reported a poorer QL (p <0.001) and patients post-crisis a similar overall QL as compared to healthy individuals. There were no significant or systematic differences between the mean levels of coping resources and strategies between the respective groups, Two-way analysis of variance indicated a group X coping resource interaction effect on overall QL for self-esteem (p <0.01). As expected, the amount of variance of overall QL explained by self-esteem was largest for patients post-crisis (27%) and comparable for patients in-crisis and healthy individuals (10 and 11%). Patients in-crisis were not able to make their coping resources and strategies more effective, whereas patients post-crisis seemed to have enhanced the effectiveness of self-esteem in restoring their QL as compared to healthy persons. Copyright (C) 2002 John Wiley Sons, Ltd

Using structural equation modeling to investigate change and response shift in patient-reported outcomes: practical considerations and recommendations

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.Health and self-regulationMultivariate analysis of psychological dat

Comparing quality of life and postoperative pain after limited access and conventional aortic valve replacement: design and rationale of the LImited access aortic valve replacement (LIAR) trial

Background: Surgical aortic valve replacement (SAVR) via limited access approaches ('mini-AVR') have proven to be safe alternative for the surgical treatment of aortic valve disease. However, it remains unclear whether these less invasive approaches are associated with improved quality of life and/or reduced postoperative pain when compared to conventional SAVR via full median sternotomy (FMS).Study design: The LImited access Aortic valve Replacement (LIAR) trial is a single-center, single blind randomized controlled clinical trial comparing 2 arms of 80 patients undergoing limited access SAVR via J-shaped upper hemi-sternotomy (UHS) or conventional SAVR through FMS. In all randomized patients, the diseased native aortic valve is planned to be replaced with a rapid deployment stented bioprosthesis. Patients unwilling or unable to participate in the randomized trial will be treated conventionally via SAVR via FMS and with implantation of a sutured valve prosthesis. These patients will participate in a prospective registry.Study methods: Primary outcome is improvement in cardiac-specific quality of life, measured by two domains of the Kansas City Cardiomyopathy Questionnaire up to one year after surgery. Secondary outcomes include, but are not limited to: generic quality of life measured with the Short Form-36, postoperative pain, perioperative (technical success rate, operating time) and postoperative outcomes (30-day and one-year mortality), complication rate and hospital length of stay.Conclusion: The LIAR trial is designed to determine whether a limited access approach for SAVR ("mini-AVR") is associated with improved quality of life and/or reduced postoperative pain compared with conventional SAVR through FMS

Developing core outcomes sets: Methods for identifying and including patient-reported outcomes (PROs)

Background: Synthesis of patient-reported outcome (PRO) data is hindered by the range of available PRO measures (PROMs) composed of multiple scales and single items with differing terminology and content. The use of core outcome sets, an agreed minimum set of outcomes to be measured and reported in all trials of a specific condition, may improve this issue but methods to select core PRO domains from the many available PROMs are lacking. This study examines existing PROMs and describes methods to identify health domains to inform the development of a core outcome set, illustrated with an example.Methods: Systematic literature searches identified validated PROMs from studies evaluating radical treatment for oesophageal cancer. PROM scale/single item names were recorded verbatim and the frequency of similar names/scales documented. PROM contents (scale components/single items) were examined for conceptual meaning by an expert clinician and methodologist and categorised into health domains. A patient advocate independently checked this categorisation.Results: Searches identified 21 generic and disease-specific PROMs containing 116 scales and 32 single items with 94 different verbatim names. Identical names for scales were repeatedly used (for example, 'physical function' in six different measures) and others were similar (overlapping face validity) although component items were not always comparable. Based on methodological, clinical and patient expertise, 606 individual items were categorised into 32 health domains.Conclusion: This study outlines a methodology for identifying candidate PRO domains from existing PROMs to inform a core outcome set to use in clinical trials

Critical examination of current response shift methods and proposal for advancing new methods

Purpose This work is part of an international, interdisciplinary initiative to synthesize research on response shift in results of patient-reported outcome measures. The objective is to critically examine current response shift methods. We additionally propose advancing new methods that address the limitations of extant methods. Methods Based on literature reviews, this critical examination comprises design-based, qualitative, individualized, and preference-based methods, latent variable models, and other statistical methods. We critically appraised their definition, operationalization, the type of response shift they can detect, whether they can adjust for and explain response shift, their assumptions, and alternative explanations. Overall limitations requiring new methods were identified. Results We examined 11 methods that aim to operationalize response shift, by assessing change in the meaning of one's self-evaluation. Six of these methods distinguish between change in observed measurements (observed change) and change in the construct that was intended to be measured (target change). The methods use either (sub)group-based or individual-level analysis, or a combination. All methods have underlying assumptions to be met and alternative explanations for the inferred response shift effects. We highlighted the need to address the interpretation of the results as response shift and proposed advancing new methods handling individual variation in change over time and multiple time points. Conclusion No single response shift method is optimal; each method has strengths and limitations. Additionally, extra steps need to be taken to correctly interpret the results. Advancing new methods and conducting computer simulation studies that compare methods are recommended to move response shift research forward.Multivariate analysis of psychological dat