Abstract

PurposeThe structural equation modeling (SEM) approach for detection of response shift (Oort in Qual Life Res 14:587–598, 2005. doi:10.1007/s11136-004-0830-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.MethodsThe 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.ResultsResponse 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.ConclusionThe 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.Electronic supplementary materialThe online version of this article (doi:10.1007/s11136-015-1195-0) contains supplementary material, which is available to authorized users.

Highlights

  • Assessment of change in health-related quality of life (HRQL) is important for determining the clinical effectiveness of treatment, as well as for monitoring well-being of individual patients over time

  • Response shift effects were detected in items of the subscales mental health, physical functioning, role limitations due to physical health, and bodily pain

  • Advantages of the structural equation modeling (SEM) approach are that it allows for the operationalization of all three types of response shift and that possible response shift effects can be taken into account to assess ‘‘true’’ change

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Summary

Introduction

Assessment of change in health-related quality of life (HRQL) is important for determining the clinical effectiveness of treatment, as well as for monitoring well-being of individual patients over time. Comparison of HRQL-scores across time may be invalidated by the occurrence of ‘‘response shift’’. Response shift refers to a change in respondents’ frames of reference that hinders a meaningful comparison of questionnaire-scores across time. Several methodological approaches have been developed for the detection of response shift in HRQL outcomes. Advantages of the SEM approach are that it allows for the operationalization of all three types of response shift and that possible response shift effects can be taken into account to assess ‘‘true’’ change. Within the SEM framework, the observed scores (e.g., questionnaire scales) are modeled to be reflective of an underlying unobserved latent variable or common factor (e.g., HRQL). The means and covariances of the observed variables (y) are given by: MeanðyÞ 1⁄4 l 1⁄4 s þ K j; ð1Þ and

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