Guidelines for secondary analysis in search of response shift

Schwartz, Carolyn E.; Ahmed, Sara; Sawatzky, Richard; Sajobi, Tolulope T.; Mayo, Nancy E.; Finkelstein, Joel; Lix, Lisa M.; Verdam, Mathilde G. E.; Oort, Frans J.; Sprangers, Mirjam A. G.

doi:10.1007/s11136-013-0402-0

Cited by 36 publications

(29 citation statements)

References 75 publications

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“…Response shift has been recognised in several chronic illnesses including cancer 50 and has also been explored in MM patients calculating MID by Kvam using the anchor-based method. 51 This is a further work of Kvams′ MID criteria where the magnitude and direction of response shift are calculated based on the pretest-then-test data and Cohen's criteria for effect size.…”

Section: 49mentioning

confidence: 99%

A systematic review of health‐related quality of life in longitudinal studies of myeloma patients

Nielsen

Jarden

Andersen

et al. 2017

European J of Haematology

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Objectives: Multiple myeloma (MM) patients report high symptom burden and reduced health-related quality of life (HRQoL) compared to patients with other haematological malignancies. The aim of this review was to analyse published longitudinal studies including MM patients according to a change in HRQoL scores, which is perceived as beneficial to the patient according to two published guidelines. The background of these findings should be in focus in future studies, and HRQoL measurements should be integrated in maintenance studies. Methods: K E Y W O R D SHealth-related quality of life, multiple myeloma, longitudinal studies

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Section: 49mentioning

confidence: 99%

A systematic review of health‐related quality of life in longitudinal studies of myeloma patients

Nielsen

Jarden

Andersen

et al. 2017

European J of Haematology

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“…Current evidence has shown the use of various statistical approaches to detect response shift in the context of HRQoL research [46]. The presence of a change in health status can trigger the lead to changes in standards, values, and conceptualization, known as response shift.…”

Section: Resultsmentioning

confidence: 99%

“…Limited evidence is available with the use of advanced SEM methods to assess the explanatory and confounding variables associated with response shift [19]. A recent study has highlighted challenges inherent to the use of secondary data sets for identification of response shift in HRQoL research [46]. Response shift research should be also conducted using other methodologies, such as qualitative methodologies and cognitive interviewing [45] to better understand how and why different variables contributing to response shift in HRQoL rating.…”

Section: Resultsmentioning

confidence: 99%

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Influence of explanatory and confounding variables on HRQoL after controlling for measurement bias and response shift in measurement

Gandhi

Ried

Kimberlin

et al. 2013

Expert Review of Pharmacoeconomics & Outcomes Research

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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 (HRQoL) 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 SF-36 questionnaire at both baseline and one 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 one year later compared to younger patients and males reported better PF than females, conditioning on 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 HRQoL over time is warranted.

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“…This method suffers from reliability and validity problems and thus has fallen out of favor [11– 14]. Although there are a number of emerging methods for response shift detection [15, 16], Oort’s structural equation modeling (SEM) measurement method [9] is one of the most comprehensive approaches for testing different forms of response shift. This approach addresses other measurement issues simultaneously that include measurement bias and response shift effects in measurement.…”

Section: Introductionmentioning

confidence: 99%

An item-level response shift study on the change of health state with the rating of asthma-specific quality of life: a report from the PROMIS® Pediatric Asthma Study

et al. 2016

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Purpose To examine item-level response shift associated with the change in asthma-related health state (i.e., change in asthma control status and global rating of change (GRC) in breathing problems). Methods Study sample comprised 238 asthmatic children who were between 8 and 17.9 years and completed the Pediatric Asthma Quality of Life Questionnaire (PAQLQ) symptoms, emotion function, and activity limitation domains at baseline and a follow-up assessment. Structural equation modeling was implemented to assess item-level response shift associated with the change in asthma-related health state with the adjustment for the influence of confounding variables. The magnitude of item-level response shift and its influence on the change of domain scores was estimated using Cohen’s effect sizes. Results We found no instances of item-level response shift. However, two items were identified with measurement bias related to GRC due to breathing problems. Specifically, asthmatic children with better/about the same GRC due to breathing problems reported lower scores for one item in the emotional domain at follow-up compared to those with deteriorated GRC due to breathing problems. In addition, asthmatic children with better/about the same GRC due to breathing problems reported better scores for another item in the symptom domain at baseline compared to those with deteriorated GRC due to breathing problems. The impact of measurement bias was small and did not bias the change of domain scores over time. Conclusions No item-level response shift, but two instances of measurement bias, appears in asthmatic children. However, the impact of these measurement issues is negligible.

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Guidelines for secondary analysis in search of response shift

Abstract: The guidelines-checklist has the potential to stimulate rigorous and replicable research using existing data sets and to assist investigators in assessing the appropriateness and potential of a data set and model-based methods for response shift research.

Cited by 36 publications

References 75 publications

A systematic review of health‐related quality of life in longitudinal studies of myeloma patients

A systematic review of health‐related quality of life in longitudinal studies of myeloma patients

Influence of explanatory and confounding variables on HRQoL after controlling for measurement bias and response shift in measurement

An item-level response shift study on the change of health state with the rating of asthma-specific quality of life: a report from the PROMIS® Pediatric Asthma Study

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