Evidence Building and Information Accumulation: Using the Bayesian Paradigm to Advance Child Welfare Intervention Research

Chen, Ding‐Geng; Testa, Mark F.; Ansong, David; Brevard, Kanisha C.

doi:10.1086/711376

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…Evidence-based intervention research seeks to strategically incorporate evidence from previous studies to design more comprehensive studies for intervention effectiveness. In that direction, the stepped-wedge design should be combined with the Bayesian paradigm as a complement to conventional frequentist intervention approaches to testing validity of statistical conclusions (Chen & Fraser, 2017a, 2017bChen et al, 2018Chen et al, , 2019Freisthler & Weiss, 2008). The integration of the stepped-wedge approach and Bayesian paradigm into evidence building holds great promise for achieving sufficient power for testing intervention effects.…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

Stepped-Wedge Cluster Randomized Controlled Trial for Intervention Research: Design and Analysis

Chen

Pan

Kainz

2021

Journal of the Society for Social Work and Research

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This article introduces the stepped-wedge design for social and health intervention research, focusing on statistical analysis with mixed-effects modeling to account for inherent clustering. We use data from Implementing Networks' Self-management Tools Through Engaging Patients and Practices (INSTTEPP) to illustrate the utility of the stepped-wedge design and provide detailed step-by-step implementation for conceptualizing stepped-wedge studies and analyzing data to identify treatment effects. Method: Summary data are from the INSTTEPP trial to evaluate impact of a bootcamp translation intervention on self-management support in primary care. We used these data as reference trial settings to generate simulated data for illustration purposes. Results: Using summary parameters from the INSTTEPP trial, we simulated individual-level data for 320 participants in 16 clusters within a stepped-wedge design. We then demonstrated data analysis using statistical mixedeffects modeling to incorporate the within-and between-cluster variation in a typical stepped-wedge study. Conclusions: This article demonstrates the potential of steppedwedge cluster design for improving interventions. Social work researchers can use the guidelines provided in this paper to design and analyze interventions, improving the effectiveness of interventions and strategies to achieve desired outcomes.

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Section: Discussion and Recommendationsmentioning

confidence: 99%

Stepped-Wedge Cluster Randomized Controlled Trial for Intervention Research: Design and Analysis

Chen

Pan

Kainz

2021

Journal of the Society for Social Work and Research

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“…We aim to foster the greater use of joint modeling in social work research and related fields; hence, we have provided a step-by-step guide and R codes to guide the data analysis processes. Joint modeling is in the direction of emerging data pooling and harmonization practices, where Bayesian approaches are commonly used to incorporate multiple data sources (Chen & Ansong, 2019;Chen et al, 2018Chen et al, , 2020Chen & Fraser, 2017). This paper contributes to this new direction and practice.…”

Section: Conclusion and Recommendationsmentioning

confidence: 98%

“…Notwithstanding their increased use in applied research, joint-modeling methods are rarely used by social work intervention researchers even though these researchers have a dual interest in understanding long-term changes in child well-being and how such changes affect permanency outcomes such as reunification with parents. More broadly, the renewed emphasis on the scientific principle of phase-based evidence-building in child welfare (Chen et al, 2020) could benefit from advances in data pooling and harmonization methodologies that maximize available data.…”

mentioning

confidence: 99%

Harmonizing Longitudinal and Survival Data Using a Joint-Modeling Framework: An Efficient Approach to Assessing Social Interventions

Chen

Ansong

Brevard

et al. 2021

Journal of the Society for Social Work and Research

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This article is an exposition of the joint-modeling approach to testing intervention effects through the harmonization of longitudinal and timeto-event data. We demonstrate the advantages of the joint-modeling approach over the classical approach of separately analyzing these types of outcome data. Method: We used a subset of 150 participants from the Illinois Birth through Three Title IV-E Waiver intervention study, which collected longitudinal Devereux Early Childhood Assessment for Infants and Toddlers (DECA-I/T) scores and time-to-permanence data for up to 3 years. We ran and contrasted three competing models: Cox proportional hazard, linear mixed-effects, and joint modeling. Results: If analyzed separately, the DECA-I/T scores are highly nonsignificantly related to time to permanence ( p 5 :929). However, when analyzed jointly, the significance level drops 88 percentage points, from .929 to .105. Because of its efficiency in addressing information loss when longitudinal and survival data are incorporated together, the joint model properly accounts for outcome-dependent missingness. Conclusion: This article highlights the utility of joint modeling in randomized longitudinal intervention studies by demonstrating its ability to preserve information from both longitudinal and time-to-event data, produce unbiased estimates, and retain higher statistical power than the traditional approach.

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“…Unfortunately, scant evidence exists on the effects of diversion. Chen et al (2020) evaluated Safe Families for Children, a program that placed children with foster families supervised by a nonprofit agency, but without formal placement in the foster care system. Using a randomized encouragement design across cases, they found this program increased the share of children who had been returned home at one year, while not leading to greater subsequent reports of abuse.…”

Section: Family Preservation Family Preservationmentioning

confidence: 99%

Economics of Foster Care

Bald

Doyle

Gross

et al. 2022

Journal of Economic Perspectives

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Foster care provides substitute living arrangements to protect maltreated children. The practice is remarkably common: it is estimated that 5 percent of children in the United States are placed in foster care at some point during childhood. This paper describes the main tradeoffs in child welfare policy and provides background on policy and practice most in need of rigorous evidence. Trends include efforts to prevent foster care on the demand side and to improve foster home recruitment on the supply side. With increasing data availability and a growing interest in evidence-based practices, there are opportunities for economic research to inform policies that protect vulnerable children.

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Evidence Building and Information Accumulation: Using the Bayesian Paradigm to Advance Child Welfare Intervention Research

Cited by 6 publications

References 19 publications

Stepped-Wedge Cluster Randomized Controlled Trial for Intervention Research: Design and Analysis

Stepped-Wedge Cluster Randomized Controlled Trial for Intervention Research: Design and Analysis

Harmonizing Longitudinal and Survival Data Using a Joint-Modeling Framework: An Efficient Approach to Assessing Social Interventions

Economics of Foster Care

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