Mathilde G. E. Verdam

Purpose: The aim of this systematic review is to provide insight into whether response shift impacts the conclusions about change in patient-reported outcome measures (PROMs) in terms of statistical significance, magnitude, and decisions made. Methods: Response shift studies from Sawatzky et al. (2025; QLR) were analyzed: longitudinal quantitative studies that examined response shift using PROMs, published before May 2023. We determined whether: 1) impact of response shift was investigated, 2) information about change in the scores of a PROM (i.e., PROM-result) before and after taking response shift into account was provided, and 3) impact of response shift was evidenced in terms of statistical significance, magnitude, or decisions made. Results: A total of 173 response shift studies that included 943 PROM-results were evaluated. 55% of studies (N = 96) investigated impact of response shift and information about impact was available for 51% of studies (N = 89). The corresponding percentages based on PROM-results were 47% (N = 446) and 53% (N = 502), respectively. Impact of response shift was evidenced in 69% of studies (N = 61) and 41% of PROM-results (N = 207), where impact on statistical significance and/or magnitude of change were most often evidenced (49–53% of studies and 20–31% of PROM-results), whereas impact on decisions was evidenced less often (8% and 2% respectively). Conclusion: About half of the studies addressed impact of response shift and showed evidence of impact, however, impact on decisions was rarely addressed. Future research should focus on evaluating impact of response shift on conclusions about change in PROMs, especially in the context of healthcare decision-making.

Background and Aims: ‘Inflammatory Bowel Disease (IBD)-specific-Cognitive-Behavioral Therapy’ (CBT) is effective in improving Quality of Life (QoL) and in decreasing anxiety and depression in IBD-patients with poor mental QoL, one month after completing CBT. Main aim was to examine the sustainability of treatment effects up to three years after treatment with CBT. Method: Participants (n = 118) of a previously conducted randomized-control-study on the effects of ‘IBD-specific-CBT’ for IBD-patients were contacted for a long-term follow-up assessment on main outcomes: generic and IBD-specific-QoL (SF-36, IBDQ), anxiety and depression (HADS, CES-D) and DSM-IV disorders (SCID-I). Change over time was examined with multilevel-regression-analyses. Results: Three years after finishing ‘IBD-specific-CBT’, 61 IBD-patients (response rate 52%) completed the follow-up SCID-I assessment and 52 patients (response rate 44%) completed the assessments for symptomatology. There were no differences between dropouts and participants at three year follow-up, except for a longer disease duration in dropouts. At three-year follow-up the chance of patients having a DSM-disorder significantly decreased with an estimated 48% (from 87% at baseline to 38% at follow-up). Multilevel analyses showed a significant improvement between baseline (n = 118) and follow-up measurements (n = 52) on outcomes: IBDQ-Total (Cohen’s d effect-size = .89), SF-36 Physical (d = .54), and SF-36 Mental (d = .69), HADS-A (d = -.77), HADS-D (d = -.65) and CES-D (d = -.55); all p < .01. QoL outcomes showed further improvement between completion (n = 90 for IBD-specific QoL and n = 91 for generic QoL) and follow-up measurements, with significant improvements for IBDQ-Total (d = 0.31) and SF-36 Physical (d = 0.32). Conclusions: Sustainable positive effects of ‘IBD-specific-CBT’ for IBD-patients with poor mental QoL were found and the prevalence of mental conditions substantially decreased over three year follow-up.

Purpose: Our objectives were to identify characteristics of response shift studies using patient-reported outcomes (PROMs) that explain variability in (1) the detection and (2) the magnitude of response shift effects. Methods: We conducted a systematic review of quantitative studies published before June 2023. First, two-level multivariable logistic regression models (effect- and sample-levels) were used to explain variability in the probability of finding a response shift effect. Second, variability in effect sizes (standardized mean differences) was investigated with 3-level meta-regression models (participant-, effect- and sample-levels). Explanatory variables identified via the purposeful selection methodology included response shift method and type, and population-, study design-, PROM- and study-quality characteristics. Results: First, logistic regression analysis of 5597 effects from 206 samples in 171 studies identified variables explaining 41.5% of the effect-level variance, while no variables explained sample-level variance. The average probability of response shift detection is 0.20 (95% CI: 0.17-0.28). Variation in detection was predominantly explained by response shift methods and type (recalibration vs. reprioritization/reconceptualization). Second, effect sizes were analyzed for 769 effects from 114 samples and 96 studies based on the then-test and structural equation modeling methods. Meta-regression analysis identified variables explaining 11.6% of the effect-level variance and 26.4% of the sample-level variance, with an average effect size of 0.30 (95% CI: 0.26-0.34). Conclusion: Response shift detection is influenced by study design and methods. Insights into the variables explaining response shift effects can be used to interpret results of other comparable studies using PROMs and inform the design of future response shift studies.

Purpose: Statistical power for response shift detection with structural equation modeling (SEM) is currently underreported. The present paper addresses this issue by providing worked-out examples and syntaxes of power calculations relevant for the statistical tests associated with the SEM approach for response shift detection. Methods: Power calculations and related sample-size requirements are illustrated for two modelling goals: (1) to detect misspecification in the measurement model, and (2) to detect response shift. Power analyses for hypotheses regarding (exact) overall model fit and the presence of response shift are demonstrated in a step-by-step manner. The freely available and user-friendly R-package lavaan and shiny-app ‘power4SEM’ are used for the calculations. Results: Using the SF-36 as an example, we illustrate the specification of null-hypothesis (H₀) and alternative hypothesis (H₁) models to calculate chi-square based power for the test on overall model fit, the omnibus test on response shift, and the specific test on response shift. For example, we show that a sample size of 506 is needed to reject an incorrectly specified measurement model, when the actual model has two-medium sized cross loadings. We also illustrate power calculation based on the RMSEA index for approximate fit, where H₀ and H₁ are defined in terms of RMSEA-values. Conclusion: By providing accessible resources to perform power analyses and emphasizing the different power analyses associated with different modeling goals, we hope to facilitate the uptake of power analyses for response shift detection with SEM and thereby enhance the stringency of response shift research.

Review question / Objective: Are the serum levels of estrogens (estrone, estradiol, estriol), gonadotropins (FSH, LH), or other hormones altered after intravaginal application of estriol for the treatment of genitourinary syndrome of menopause? Condition being studied: The aim of this review is to study whether the vaginal application of estriol has any effects on the serum levels of different sex hormones, mainly estriol, estradiol, and FSH, as those have been previously used as proxies for the safety of similar estrogenic products. Study designs to be included: Included: RCTs, controlled studies, head-to-head comparisons, systematic reviews, meta-analyses, quasi-experimental studies (intervention/no control).

Purpose. Statistical power for response shift detection with structural equation modeling (SEM) is currently underreported. The present paper addresses this issue by providing worked-out examples and syntaxes of power calculations relevant for the statistical tests associated with the SEM approach for response shift detection. Methods. Power calculations and related sample-size requirements are illustrated for two modelling goals: 1) to detect misspecification in the measurement model, and 2) to detect response shift. Power analyses for hypotheses regarding (exact) overall model fit and the presence of response shift are demonstrated in a step-by-step manner. The freely available and user-friendly R-package lavaan and shiny-app ‘power4SEM’ are used for the calculations. Results. Using the SF-36 as an example, we illustrate the specification of null-hypothesis (H0) and alternative hypothesis (H1) models to calculate chi-square based power for the test on overall model fit, the omnibus test on response shift, and the specific test on response shift. For example, we show that a sample size of 506 is needed to reject an incorrectly specified measurement model, when the actual model has two-medium sized cross loadings. We also illustrate an alternative power calculation based on the RMSEA index for approximate fit, where H0 and H1 are defined in terms of RMSEA-values. Conclusion. By providing accessible resources to perform power analyses and emphasizing the different power analyses associated with different modeling goals, we hope to facilitate the uptake of power analyses for response shift detection with SEM and thereby enhance the stringency of response shift research.

Background: Cognitive behavioral therapy (CBT) is an evidence-based intervention for severe fatigue. Changes in patients’ fatigue scores following CBT might reflect not only the intended relief in fatigue but also response shift, a change in the meaning of patients’ self-evaluation. Objectives were to (1) identify the occurrence of response shift in patients undergoing CBT, (2) determine the impact of response shift on the intervention effect, and (3) investigate whether changes in fatigue-related cognitions and perceptions, targeted during CBT, are associated with response shift. Methods: Data of three randomized controlled trials testing the efficacy of CBT in individuals with chronic fatigue syndrome (CFS, n = 222), cancer (n = 123), and diabetes (n = 107) were re-analyzed. Fatigue severity was measured with 8 items from the Checklist Individual Strength, a valid and widely used self-report questionnaire. Structural equation modelling was applied to assess lack of longitudinal measurement invariance, as indication of response shift. Results: As expected, in all three trials, response shift was indicated in the CBT groups, not the control groups. Response shift through reprioritization was indicated for the items “Physically, I feel exhausted” (CFS) and “I tire easily” (cancer, diabetes), which became less vs. more important to the measurement of fatigue, respectively. However, this did not affect the intervention effects. Some changes in cognitions and perceptions were associated with the response shifts. Conclusions: CBT seems to induce response shift through reprioritization across patient groups, but its occurrence does not affect the intervention effect. Future research should corroborate these findings and investigate whether patients indeed change their understanding of fatigue.

The Video Engagement Scale (VES) is a quality indicator to assess engagement in experimental video-vignette studies, but its measurement properties warrant improvement. Data from previous studies were combined (N = 2676) and split into three subsamples for a stepped analytical approach. We tested construct validity, criterion validity, reliability, and measurement invariance. Confirmatory factor analysis (CFA) reiterated poor fit of the previously suggested subscales. An alternative 2-factor structure is presented, which was cross-validated and further shortened (VES-sf). Reliability of both subscales (Immersion, Emotional Impact) was very high (α > .86). Multi-group CFAs demonstrated partial and complete measurement invariance among male/female and differently educated participants respectively. Invariance between healthy versus ill participants was not established, but CFAs supported the VES-sf structure in both groups separately. To conclude, we provide the VES-sf with sound measurement properties and demonstrated the appropriateness of comparing certain subgroups. Overall, we recommend the VES-sf to assess engagement/ecological validity in video-vignette research.