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The purpose of this study was to examine the influence of explanatory and confounding variables on health-related quality of life after accounting for response shift, measurement bias and response shift in measurement using structural equation modeling. Hypertensive patients with coronary artery disease randomized to anti-hypertensive treatment, completed the ShortForm-36 questionnaire at both baseline and 1 year (n = 788). Three measurement biases were found and all three were considered as response shift in measurement. Older patients reported worse scores for both physical functioning (PF) and role-physical at baseline and 1 year later compared to younger patients; and males reported better PF than females after conditioning on the latent trait of general physical health. Before controlling for response shift, patients' PF scores were not statistically different over time; however, PF scores significantly improved (p < 0.01) after controlling for recalibration response shift. Assessment of how patients perceive their change in health-related quality of life over time is warranted.

Objective: To identify response shift using two structural equation modeling (SEM) techniques., Study Design and Setting: Hypertensive patients (n = 909) with coronary artery disease (CAD) completed SF-36 surveys at both baseline and 1-year follow-up. Response shift was identified using Oort and Schmitt SEM techniques. The type of response shift linked to changes in various parameters of the SEM measurement model is defined differently for both SEM approaches. Effect sizes were calculated for the impact of response shift on the change of SF-36 domain scores when using the Oort approach., Results: Both Oort and Schmitt SEM approaches identified response shift only in the SF-36 physical functioning (PF) scale. The effect size of recalibration on the change of PF domain scores when using the Oort approach was -0.12., Conclusion: This study showed that hypertensive patients with CAD experienced a response shift over a 1-year period. Both the SEM approaches identified response shift (uniform recalibration using the Oort approach and recalibration using the Schmitt approach); however, both approaches use different parameters to define and test response shift. We found that either the variation in analytic methods or the sample used may influence the identification and type of response shift.

Objective: Existing estimates of human immunodeficiency virus (HIV)-related health state utilities are inadequate for comparing alternative treatments on the basis of regimen-specific attributes such as dosing requirements or tolerability. The objective of this study was to examine the marginal impact of dosing, adverse events (AEs), and other factors on patients' health state utilities., Methods: Treatment naive and experienced HIV patients participating in five open-label trials of highly active antiretroviral therapy (HAART) completed the 36-Item Short Form Health Survey (SF-36) instrument at various time points. SF-36 responses were converted to utilities using a previously reported algorithm. Expected utilities were estimated as a function of patient demographics, regimen attributes, disease status, and AEs using a mixed-effects maximum likelihood model. Mean utilities for five HIV health states were derived from predicted patient utilities., Results: Negative predictors of utility included greater age (-0.001), prior acquired immune deficiency syndrome-defining events (-0.036), female gender (-0.038), and injection drug use (-0.056; P < 0.01 for all). Utility also depended on CD4+ cell count (P < 0.01), but not the presence of undetectable viral load. Regimen attributes were marginally associated with changes in utility. Depression was associated with the largest decrease in utility (-0.054, P < 0.001) among the AEs examined. Using the model to generate predicted utilities from the sample provided mean estimates ranging from 0.742 (SD 0.058) to 0.798 (0.052) for CD4+ counts between 0 and 99 and > or =500 cells/mm(3), respectively., Conclusions: HIV patients' health-related quality of life may be substantially affected by clinically relevant patient-, disease-, and treatment-related factors, such as injection drug use, disease status, food/drink restrictions, and AEs.