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Purpose The extant response shift definitions and theoretical response shift models, while helpful, also introduce predicaments and theoretical debates continue. To address these predicaments and stimulate empirical research, we propose a more specific formal definition of response shift and a revised theoretical model. Methods This work is an international collaborative effort and involved a critical assessment of the literature. Results Three main predicaments were identified. First, the formal definitions of response shift need further specification and clarification. Second, previous models were focused on explaining change in the construct intended to be measured rather than explaining the construct at multiple time points and neglected the importance of using at least two time points to investigate response shift. Third, extant models do not explicitly distinguish the measure from the construct. Here we define response shift as an effect occurring whenever observed change (e.g., change in patient-reported outcome measures (PROM) scores) is not fully explained by target change (i.e., change in the construct intended to be measured). The revised model distinguishes the measure (e.g., PROM) from the underlying target construct (e.g., quality of life) at two time points. The major plausible paths are delineated, and the underlying assumptions of this model are explicated. Conclusion It is our hope that this refined definition and model are useful in the further development of response shift theory. The model with its explicit list of assumptions and hypothesized relationships lends itself for critical, empirical examination. Future studies are needed to empirically test the assumptions and hypothesized relationships.

Purpose Results of patient-reported outcome measures (PROMs) are increasingly used to inform healthcare decision-making. Research has shown that response shift can impact PROM results. As part of an international collaboration, our goal is to provide a framework regarding the implications of response shift at the level of patient care (micro), healthcare institute (meso), and healthcare policy (macro). Methods Empirical evidence of response shift that can influence patients’ self-reported health and preferences provided the foundation for development of the framework. Measurement validity theory, hermeneutic philosophy, and micro-, meso-, and macro-level healthcare decision-making informed our theoretical analysis. Results At the micro-level, patients’ self-reported health needs to be interpreted via dialogue with the clinician to avoid misinterpretation of PROM data due to response shift. It is also important to consider the potential impact of response shift on study results, when these are used to support decisions. At the meso-level, individual-level data should be examined for response shift before aggregating PROM data for decision-making related to quality improvement, performance monitoring, and accreditation. At the macro-level, critical reflection on the conceptualization of health is required to know whether response shift needs to be controlled for when PROM data are used to inform healthcare coverage. Conclusion Given empirical evidence of response shift, there is a critical need for guidelines and knowledge translation to avoid potential misinterpretations of PROM results and consequential biases in decision-making. Our framework with guiding questions provides a structure for developing strategies to address potential impacts of response shift at micro-, meso-, and macro-levels.

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.

Purpose Some IRT models have the advantage of being robust to missing data and thus can be used with complete data as well as different patterns of missing data (informative or not). The purpose of this paper was to develop an algorithm for response shift (RS) detection using IRT models allowing for non-uniform and uniform recalibration, reprioritization RS recognition and true change estimation with these forms of RS taken into consideration if appropriate. Methods The algorithm is described, and its implementation is shown and compared to Oort's structural equation modeling (SEM) procedure using data from a clinical study assessing health-related quality of life in 669 hospitalized patients with chronic conditions. Results The results were quite different for the two methods. Both showed that some items of the SF-36 General Health subscale were affected by response shift, but those items usually differed between IRT and SEM. The IRT algorithm found evidence of small recalibration and reprioritization effects, whereas SEM mostly found evidence of small recalibration effects. Conclusion An algorithm has been developed for response shift analyses using IRT models and allows the investigation of non-uniform and uniform recalibration as well as reprioritization. Differences in RS detection between IRT and SEM may be due to differences between the two methods in handling missing data. However, one cannot conclude on the differences between IRT and SEM based on a single application on a dataset since the underlying truth is unknown. A next step would be to implement a simulation study to investigate those differences.

Purpose The structural equation modeling (SEM) approach for detection of response shift (Oort in Qual Life Res 14:587-598, 2005. doi: 10.1007/sl 1136-004-0830-y) is especially suited for continuous data, e.g., questionnaire scales. The present objective is to explain how the SEM approach can be applied to discrete data and to illustrate response shift detection in items measuring health-related quality of life (HRQL) of cancer patients. Methods The SEM approach for discrete data includes two stages: (1) establishing a model of underlying continuous variables that represent the observed discrete variables, (2) using these underlying continuous variables to establish a common factor model for the detection of response shift and to assess true change. The proposed SEM approach was illustrated with data of 485 cancer patients whose HRQL was measured with the SF-36, before and after start of antineoplastic treatment. Results Response shift effects were detected in items of the subscales mental health, physical functioning, role limitations due to physical health, and bodily pain. Recalibration response shifts indicated that patients experienced relatively fewer limitations with \"bathing or dressing yourself (effect size d = 0.51) and less \"nervousness\" (d = 0.30), but more \"pain\" (d = -0.23) and less \"happiness\" (d = -0.16) after antineoplastic treatment as compared to the other symptoms of the same subscale. Overall, patients' mental health improved, while their physical health, vitality, and social functioning deteriorated. No change was found for the other subscales of the SF-36. Conclusion The proposed SEM approach to discrete data enables response shift detection at the item level. This will lead to a better understanding of the response shift phenomena at the item level and therefore enhances interpretation of change in the area of HRQL.

Purpose Missing data due to attrition present a challenge for the assessment and interpretation of change and response shift in HRQL outcomes. The objective was to handle such missingness and to assess response shift and 'true change' with the use of an attrition-based multigroup structural equation modeling (SEM) approach. Method Functional limitations and health impairments were measured in 1,157 cancer patients, who were treated with palliative radiotherapy for painful bone metastases, before [time (T) 0], every week after treatment (T1 through T12), and then monthly for up to 2 years (T13 through T24). To handle missing data due to attrition, the SEM procedure was extended to a multigroup approach, in which we distinguished three groups: short survival (3—5 measurements), medium survival (6—12 measurements), and long survival (>12 measurements). Results Attrition after third, sixth, and 13th measurement occasions was 11, 24, and 41 %, respectively. Results show that patterns of change in functional limitations and health impairments differ between patients with short, medium, or long survival. Moreover, three response-shift effects were detected: recalibration of 'pain' and 'sickness' and reprioritization of 'physical functioning.' If response-shift effects would not have been taken into account, functional limitations and health impairments would generally be underestimated across measurements. Conclusions The multigroup SEM approach enables the analysis of data from patients with different patterns of missing data due to attrition. This approach does not only allow for detection of response shift and assessment of true change across measurements, but also allow for detection of differences in response shift and true change across groups of patients with different attrition rates.

Purpose Response shift (RS) is an important phenomenon that influences the assessment of longitudinal changes in health-related quality of life (HRQOL) studies. Given that RS effects are often small, missing data due to attrition or item non-response can contribute to failure to detect RS effects. Since missing data are often encountered in longitudinal HRQOL data, effective strategies to deal with missing data are important to consider. This study aims to compare different imputation methods on the detection of reprioritization RS in the HRQOL of caregivers of stroke survivors. Methods Data were from a Canadian multi-center longitudinal study of caregivers of stroke survivors over a oneyear period. The Stroke Impact Scale physical function score at baseline, with a cutoff of 75, was used to measurepatient stroke severity for the reprioritization RS analysis. Mean imputation, likelihood-based expectation-maximization imputation, and multiple imputation methods were compared in test procedures based on changes in relative importance weights to detect RS in SF-36 domains over a 6-month period. Monte Carlo simulation methods were used to compare the statistical powers of relative importance test procedures for detecting RS in incomplete longitudinal data under different missing data mechanisms and imputation methods. Results Of the 409 caregivers, 15.9 and 31.3 % of them had missing data at baseline and 6 months, respectively. There were no statistically significant changes in relative importance weights on any of the domains when completecase analysis was adopted. But statistical significant changes were detected on physical functioning and/or vitality domains when mean imputation or EM imputation was adopted. There were also statistically significant changes in relative importance weights for physical functioning, mental health, and vitality domains when multiple imputation method was adopted. Our simulations revealed that relative importance test procedures were least powerful under complete-case analysis method and most powerful when a mean imputation or multiple imputation method was adopted for missing data, regardless of the missing data mechanism and proportion of missing data. Conclusions Test procedures based on relative importance measures are sensitive to the type and amount of missing data and imputation method. Relative importance test procedures based on mean imputation and multiple imputation are recommended for detecting RS in incomplete data.