Analyzing Post-Treatment Institutionalization in Adolescent Substance Abuse Treatment

1. Latent Class Pattern Mixture Modeling Accomodates Post-Treatment Institutionalization in an RCT of Adolescent Substance Abuse Treatment Craig Hendersona,c, Paul Greenbaumb, Gayle Dakofc, Cindy Rowec, Howard Liddlec a Sam Houston State University b University of South Florida c University of Miami Miller School of Medicine Paper Presented at the Annual Meeting of the Society for Prevention ResearchSan Francisco, CAMay 29, 2008

2. Background • Evaluating the effectiveness of drug treatment programs for adolescents is confounded by the high risk clients have for institutionalization

3. Background (ctd) • When institutionalized, freedom to use drugs or commit crimes is substantially decreased • Leads to apparent improvement in many of the most important substance abuse treatment outcomes (e.g., frequency of drug use, delinquent activity) • If ignored, treatment effects on institutionalization and treatment effects on outcomes of interest are combined in the observed “treatment effect”

4. Institutionalization Confound • Two potential sources of bias: • Individual’s outcome, Y, is related to the number of days institutionalized, Z • E.g.: Drug use tends to decrease as number of days institutionalized increases (suppression effect) • Cases with any days institutionalized are not a random sample of the study population • E.g.: Youths who are heavy drug users are more likely to have large numbers of days institutionalized (selection effect)

5. Suppression and Selection Effects ● Suppression Effect:: As days institutionalized increases, outcome decreases Selection Effect: Cases with no institutionalization different from those with only a few days of institutionalization McCaffery, Morral, Ridgeway, & Griffin (2007). Drug and Alcohol Dependence.

6. What Do We Do? McCaffery, Morral, Ridgeway, & Griffin (2007). Drug and Alcohol Dependence.

7. “Interpreting Treatment Effects When Cases Are Institutionalized After Treatment” McCaffrey, Morral, Ridgeway and Griffin (2007) • Evaluated these 4 standard approaches to the problem of institutionalization • Conclusion: “It’s All About the Assumptions” • Some assumptions were strong and most unlikely to hold in practice • Treatment effect estimate ranged from significant and beneficial to significant and detrimental depending on the approach

8. Pattern Mixture Models Examples exist in the literature, but the models have been primarily conceptualized as a missing data problem (Heddeker & Gibbons, 1997; Morgan-Lopez & Fals-Stewart, 2006) Morgan-Lopez & Fals-Stewart (2007) extend Hedeker and Gibbons’ model using observed groups to the latent variable modeling framework Question: Does the same logic apply to institutionalization patterns? Analytic approach based on latent growth mixture modeling (Muthén, 2004) in which the mixtures are derived from placement patterns over the duration of the study

9. Monthly Placement Status By Follow-Up Period

10. Procedure • Create groups according to latent placement classes and drug use trajectories • Estimate the model separately in each group, comparing people within the same latent class • Treatment effect compares μ1T and μ1C (treatment and control group means) conditional on latent class membership

11. Assumptions • Relationship between institutionalization and outcome subsumed by latent class membership • As long as no remaining selection bias within a group, treatment effect within the group should be unbiased for cases like those within group • Akin to conditional independence assumption • Treatment assignment unrelated to latent class membership • Distributional assumptions pertaining to outcome variable • E.g., outcome is continuous and normally distributed within latent class

12. Case Example

13. Study Design • Randomized to long term residential treatment or an in-home alternative, Multidimensional Family Therapy • Eligibility Criteria • ASAM and community criteria for referral to residential treatment (e.g., failed less restrictive treatments). • Substance abuse or dependence • At least one comorbid psychiatric condition • Assessment Points • Intake, 2, 4, 12, 18 months post-intake. • Assessed change in (30 day) substance use using Timeline Follow-Back and piecewise growth curve modeling (Intake-2 Months; 2 Months-18 Months)

14. Participants (n=113) • Referral Source: • Juvenile Justice 85% • Self 11% • Child welfare 3% • Schools 2% • Gender • Female 25% • Male 75%

15. Participants (ctd.) • Ethnicity: • Hispanic 69% • African American 15% • Caucasian 12% • Other 4% • Age at baseline • Mean 15.85 • SD 1.05

17. Three Latent Classes Reflecting Probability of Placement in Controlled Environment

18. Results • Early Placement Class • No differences between treatments at intake (pseudo z = -1.20, ns) • Between intake and 2 months (pseudo z = 1.30, ns) • Or between 2 months and 18 months (pseudo z = -0.42, ns) • Late Placement Class • No differences between treatments at intake (pseudo z = -0.45, ns) • Between intake and 2 months (pseudo z = 0.84, ns) • Or between 2 months and 18 months (pseudo z = -1.16, ns) • Low Probability of Placement Class • No differences between treatments at intake (pseudo z = -1.45, ns) • Or between intake and 2 months (pseudo z = 0.43, ns) • But, change in substance use between 2 months and 18 months was significant (pseudo z = 2.01, p < .05)

19. Modeling Implications • LCPMM improves on traditional approaches for dealing with the institutionalization-outcome confound by comparing similar groups of participants • More effectively deals with selection effects • LCPMM isolates a group of participants who show a low probability of placement • More effectively deals with suppression effects

20. Treatment Implications • Findings support the usefulness of MDFT as an alternative to residential treatment • Results add to an accumulating evidence base for MDFT with a range of substance abusing adolescents—in this case a sample of severely impaired youth