| 1. |
- Douglas, Susan, et al.
(författare)
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A Clinical Leadership Lens on Implementing Progress Feedback in Three Countries: Development of a Multidimensional Qualitative Coding Scheme
- 2023
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Ingår i: Administration and Policy in Mental Health and Mental Health Services Research. - : SPRINGER. - 0894-587X .- 1573-3289.
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Tidskriftsartikel (refereegranskat)abstract
- Background Progress feedback, also known as measurement-based care (MBC), is the routine collection of patient-reported measures to monitor treatment progress and inform clinical decision-making. Although a key ingredient to improving mental health care, sustained use of progress feedback is poor. Integration into everyday workflow is challenging, impacted by a complex interrelated set of factors across patient, clinician, organizational, and health system levels. This study describes the development of a qualitative coding scheme for progress feedback implementation that accounts for the dynamic nature of barriers and facilitators across multiple levels of use in mental health settings. Such a coding scheme may help promote a common language for researchers and implementers to better identify barriers that need to be addressed, as well as facilitators that could be supported in different settings and contexts. Methods Clinical staff, managers, and leaders from two Dutch, three Norwegian, and four mental health organizations in the USA participated in semi-structured interviews on how intra- and extra-organizational characteristics interact to influence the use of progress feedback in clinical practice, supervision, and program improvement. Interviews were conducted in the local language, then translated to English prior to qualitative coding. Results A team-based consensus coding approach was used to refine an a priori expert-informed and literature-based qualitative scheme to incorporate new understandings and constructs as they emerged. First, this hermeneutic approach resulted in a multi-level coding scheme with nine superordinate categories and 30 subcategories. Second-order axial coding established contextually sensitive categories for barriers and facilitators. Conclusions The primary outcome is an empirically derived multi-level qualitative coding scheme that can be used in progress feedback implementation research and development. It can be applied across contexts and settings, with expectations for ongoing refinement. Suggestions for future research and application in practice settings are provided. Supplementary materials include the coding scheme and a detailed playbook.
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| 2. |
- Falkenström, Fredrik, et al.
(författare)
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Do therapist effects really impact estimates of within-patient mechanisms of change? A Monte Carlo simulation study
- 2020
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Ingår i: Psychotherapy Research. - : ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD. - 1050-3307 .- 1468-4381. ; 30:7, s. 885-899
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Tidskriftsartikel (refereegranskat)abstract
- Objective:Existing evidence highlights the importance of modeling differential therapist effectiveness when studying psychotherapy outcome. However, no study to date examined whether this assertion applies to the study of within-patient effects in mechanisms of change. The study investigated whether therapist effects should be modeled when studying mechanisms of change on a within-patient level. Methods:We conducted a Monte Carlo simulation study, varying patient- and therapist level sample sizes, degree of therapist-level nesting (intra-class correlation), balanced vs. unbalanced assignment of patients to therapists, and fixed vs random within-patient coefficients. We estimated all models using longitudinal multilevel and structural equation models that ignored (2-level model) or modeled therapist effects (3-level model). Results:Across all conditions, 2-level models performed equally or were superior to 3-level models. Within-patient coefficients were unbiased in both 2- and 3-level models. In 3-level models, standard errors were biased when number of therapists was small, and this bias increased in unbalanced designs. Ignoring random slopes led to biased standard errors when slope variance was large; but 2-level models still outperformed 3-level models. Conclusions:In contrast to treatment outcome research, when studying mechanisms of change on a within-patient level, modeling therapist effects may even reduce model performance and increase bias.
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| 3. |
- Falkenström, Fredrik, et al.
(författare)
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Dynamic Models of Individual Change in Psychotherapy Process Research
- 2017
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Ingår i: Journal of Consulting and Clinical Psychology. - : AMER PSYCHOLOGICAL ASSOC. - 0022-006X .- 1939-2117. ; 85:6, s. 537-549
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Tidskriftsartikel (refereegranskat)abstract
- Objective: There is a need for rigorous methods to study the mechanisms that lead to individual-level change (i.e., process-outcome research). We argue that panel data (i.e., longitudinal study of a number of individuals) methods have 3 major advantages for psychotherapy researchers: (1) enabling microanalytic study of psychotherapeutic processes in a clinically intuitive way, (2) modeling lagged associations over time to ensure direction of causality, and (3) isolating within-patient changes over time from between-patient differences, thereby protecting against confounding influences because of the effects of unobserved stable attributes of individuals. However, dynamic panel data methods present a complex set of analytical challenges. We focus on 2 particular issues: (1) how long-term trajectories in the variables of interest over the study period should be handled, and (2) how the use of a lagged dependent variable as a predictor in regression-based dynamic panel models induces endogeneity (i.e., violation of independence between predictor and model error term) that must be taken into account in order to appropriately isolate within-and between-person effects. Method: An example from a study of working alliance in psychotherapy in primary care in Sweden is used to illustrate some of these analytic decisions and their impact on parameter estimates. Results: Estimates were strongly influenced by the way linear trajectories were handled; that is, whether variables were "detrended" or not. Conclusions: The issue of when detrending should be done is discussed, and recommendations for research are provided. What is the public health significance of this article? This article provides recommendations on how to study psychotherapy processes using dynamic panel data models to strengthen causal inferences. Accurate estimates of what drives individual development in psychotherapy are needed to generate recommendations on what therapists should focus on in therapy. Using the alliance-outcome association as an example, we show that estimated effect sizes may vary greatly depending on which modeling approach is used, with the decision on whether to remove time-trends from the outcome variable making the largest difference.
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| 4. |
- Falkenström, Fredrik, 1972-, et al.
(författare)
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How to Model and Interpret Cross-Lagged Effects in Psychotherapy Mechanisms of Change Research : A Comparison of Multilevel and Structural Equation Models
- 2022
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Ingår i: Journal of Consulting and Clinical Psychology. - : American Psychological Association (APA). - 0022-006X .- 1939-2117. ; 90:5, s. 446-458
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Modeling cross-lagged effects in psychotherapy mechanisms of change studies is complex and requires careful attention to model selection and interpretation. However, there is a lack of field-specific guidelines. We aimed to (a) describe the estimation and interpretation of cross lagged effects using multilevel models (MLM) and random-intercept cross lagged panel model (RI-CLPM); (b) compare these models' performance and risk of bias using simulations and an applied research example to formulate recommendations for practice. Method: Part 1 is a tutorial focused on introducing/describing dynamic effects in the form of autoregression and bidirectionality. In Part 2, we compare the estimation of cross-lagged effects in RI-CLPM, which takes dynamic effects into account, with three commonly used MLMs that cannot accommodate dynamics. In Part 3, we describe a Monte Carlo simulation study testing model performance of RI-CLPM and MLM under realistic conditions for psychotherapy mechanisms of change studies. Results: Our findings suggested that all three MLMs resulted in severely biased estimates of cross-lagged effects when dynamic effects were present in the data, with some experimental conditions generating statistically significant estimates in the wrong direction. MLMs performed comparably well only in conditions which are conceptually unrealistic for psychotherapy mechanisms of change research (i.e., no inertia in variables and no bidirectional effects). Discussion: Based on conceptual fit and our simulation results, we strongly recommend using fully dynamic structural equation modeling models, such as the RI-CLPM, rather than static, unidirectional regression models (e.g., MLM) to study cross-lagged effects in mechanisms of change research. What is the public health significance of this article? We describe the differences between multilevel and structural equation modeling in the study of mechanisms of change in psychotherapy research. We argue that the common application of multilevel modeling assumes that there is no within-patient inertia in predictor or outcome variable, and the outcome variable does not impact the predictor, both of which seem highly unrealistic in psychotherapy research. Moreover, we demonstrate that violations of these assumptions may lead to severe bias in estimated coefficients, resulting in inaccurate recommendations for clinical practice. Thus, we recommend researchers to use structural equation modeling to estimate the effects of proposed change mechanisms over time.
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| 5. |
- Falkenström, Fredrik, Professor, 1972-, et al.
(författare)
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To Detrend, or Not to Detrend, That Is the Question? : The Effects of Detrending on Cross-Lagged Effects in Panel Models
- 2023
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Ingår i: Psychological methods. - : American Psychological Association (APA). - 1082-989X .- 1939-1463.
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Tidskriftsartikel (refereegranskat)abstract
- Intervention studies in psychology often focus on identifying mechanisms that explain change over time. Cross-lagged panel models (CLPMs) are well suited to study mechanisms, but there is a controversy regarding the importance of detrending-defined here as separating longer-term time trends from cross-lagged effects-when modeling these change processes. The aim of this study was to present and test the arguments for and against detrending CLPMs in the presence of an intervention effect. We conducted Monte Carlo simulations to examine the impact of trends on estimates of cross-lagged effects from several longitudinal structural equation models. Our simulations suggested that ignoring time trends led to biased estimates of auto- and cross-lagged effects in some conditions, while detrending did not introduce bias in any of the models. We used real data from an intervention study to illustrate how detrending may affect results. This example showed that models that separated trends from cross-lagged effects fit better to the data and showed nonsignificant effect of the mechanism on outcome, while models that ignored trends showed significant effects. We conclude that ignoring trends increases the risk of bias in estimates of auto- and cross-lagged parameters and may lead to spurious findings. Researchers can test for the presence of trends by comparing model fit of models that take into account individual differences in trends (e.g., autoregressive latent trajectory model, the latent curve model with structured residuals, or the general cross-lagged model).
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| 6. |
- Falkenström, Fredrik, 1972-, et al.
(författare)
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Using time-lagged panel data analysis to study mechanisms of change in psychotherapy research: Methodological recommendations
- 2020
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Ingår i: Counselling and Psychotherapy Research. - : WILEY. - 1473-3145 .- 1746-1405. ; 20:3, s. 435-441
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Tidskriftsartikel (refereegranskat)abstract
- The introduction of novel methodologies in the past decade has advanced research on mechanisms of change in observational studies. Time-lagged panel models allow us to track session-by-session changes and focus on within-patient associations between predictors and outcomes. This shift is crucial as change in mechanisms inherently takes place at a within-patient level. These models also enable preliminary casual inferences, which can guide the development of effective personalised interventions that target mechanisms of change, used at specific treatment phases for optimal effect. Given their complexity, panel models need to be implemented with caution, as different modelling choices can significantly affect results and reduce replicability. We outline three central methodological recommendations for use of time-lagged panel analysis to study mechanisms of change: (a) taking patient-specific effects into account, separating out stable between-person differences from within-person fluctuations over time; (b) properly controlling for autoregressive effects; and (c) considering long-term time trends. We demonstrate these recommendations in an applied example examining the session-by-session alliance-outcome association in a naturalistic psychotherapy study. We present limitations of time-lagged panel analysis and future directions.
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| 7. |
- Fluckiger, Christoph, et al.
(författare)
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The Reciprocal Relationship Between Alliance and Early Treatment Symptoms: A Two-Stage Individual Participant Data Meta-Analysis
- 2020
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Ingår i: Journal of Consulting and Clinical Psychology. - : AMER PSYCHOLOGICAL ASSOC. - 0022-006X .- 1939-2117. ; 88:9, s. 829-843
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Even though the early alliance has been shown to robustly predict posttreatment outcomes, the question whether alliance leads to symptom reduction or symptom reduction leads to a better alliance remains unresolved. To better understand the relation between alliance and symptoms early in therapy, we meta-analyzed the lagged session-by-session within-patient effects of alliance and symptoms from Sessions I to 7. Method: We applied a 2-stage individual participant data meta-analytic approach. Based on the data sets of 17 primary studies from 9 countries that comprised 5,350 participants, we first calculated standardized session-by-session within-patient coefficients. Second, we meta-analyzed these coefficients by using random-effects models to calculate omnibus effects across the studies. Results: In line with previous meta-analyses, we found that early alliance predicted posttreatment outcome. We identified significant reciprocal within-patient effects between alliance and symptoms within the first 7 sessions. Cross-level interactions indicated that higher alliances and lower symptoms positively impacted the relation between alliance and symptoms in the subsequent session. Conclusion: The findings provide empirical evidence that in the early phase of therapy. symptoms and alliance were reciprocally related to one other, often resulting in a positive upward spiral of higher alliance/lower symptoms that predicted higher alliances/lower symptoms in the subsequent sessions. Two-stage individual participant data meta-analyses have the potential to move the field forward by generating and interlinking well-replicable process-based knowledge.
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