Untangling the Web: An Approach to Analyzing the Impacts of Individually Tailored, Multi-Component Treatment Interventions
Document Type
Article
Publication Date
9-2004
Keywords
maximum change, change score analysis, treatment outcomes, individual tailoredtreatments.
Digital Object Identifier (DOI)
https://doi.org/10.1023/B:MHSR.0000036488.12833.6e
Abstract
In this paper the use of a maximum individualized change score is proposed as an analytic alternative to the more traditional MANOVA and latent variable approaches in studies examining the use of individually tailored interventions. This strategy offers a number of significant advantages when multiple indicators are used to assess a broad array of potential outcomes that might result from client-specific treatments. Data on 146 children from a study examining the effectiveness of 3 short-term intensive in-home services were used to contrast the results of our proposed analytic strategy with those from the MANOVA and latent variable approaches. Results indicate that the maximum individualized change score approach improves the outcome comparisons among the 3 treatment interventions and eliminates some concerns regarding subjectivity that exists with procedures such as goal-attainment scaling. A simulation study suggests the maximum change score statistics is a nonbiased estimate for assessing between-group differences in program effectiveness and has more power than MANOVA to produce significant differences when smaller program effects exist. Suggestions for strengthening this analytic approach as well as examples regarding use of this technique in other research contexts are also provided.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Mental Health Services Research, v. 6, issue 3, p. 143–153
Scholar Commons Citation
Boothroyd, Roger A.; Banks, Steven M.; Evans, Mary E.; Greenbaum, Paul E.; and Brown, Eric, "Untangling the Web: An Approach to Analyzing the Impacts of Individually Tailored, Multi-Component Treatment Interventions" (2004). Mental Health Law & Policy Faculty Publications. 300.
https://digitalcommons.usf.edu/mhlp_facpub/300
