Using structural equation modeling to detect response shifts and true change in discrete variables: an application to the items of the SF-36

Purpose

The 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.

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.