Preventing Drug Use and Risk Behaviors in Adolescent Girls
Phase: NA
Conditions: Juvenile Delinquency Unspecified, Risk Behavior, Adolescent Behavior, Sexual Behavior, Drug Use
Sponsor: University of Oregon

COVER PAGE 
 
 
OFFICIAL TITLE OF THE STUDY: PREVENTING DRUG USE AND RISK BEHAVIORS IN ADOLESCENT GIRLS 
 
BRIEF TITLE: SAFE, HEALTHY, ADOLESCENT RELATIONSHIPS AND PEERS (SHARP-TEEN) 
 
 
NCT NUMBER: NCT02420548 
 
DATE OF DOCUMENT: JANUARY 21, 2019 
 
 
 

STUDY PROTOCOL WITH SAP 
 
 
Brief Summary 
Initiation of drug use and participation in sexual-risk behaviors such as having multiple sexual partners, 
unprotected sexual intercourse, and intercourse with drug users are all too common among female youth with 
involvement in the child welfare or juvenile justice system. Studies consistently find that these youth have 
disproportionately higher rates of these problems that, in addition to increasing risk for negative outcomes, 
have other costly sequelae such as drug addiction, early pregnancy, sexually transmitted infection, HIV 
contraction, delinquency, and early mortality (e.g., Santelli et al., 2001; Stueve et al., 2005). The goal of this 
study, which is Research Component 2 within a P50 Center Grant, is to prevent drug use and sexual-risk 
behaviors in 13–18 year-old girls with early adverse life experiences such as maltreatment (n = 123) through 
the development and evaluation of a preventive intervention. 
 
1. To Examine the Effects of the SHARP (Safe, Healthy, Adolescent Relationships and Peers) 
Intervention on the Developmental trajectories of Drug Use, Sexual-Risk Behaviors, and 
Delinquency. We will examine outcomes at intervention termination (approx. 6 months) and a 12-
month follow-up using quantitative analysis methods. 
 
2. To Examine Mediating Processes Linking the Intervention with Developmental Trajectories of 
Drug Use, Sexual-Risk Behaviors, and Delinquency. We will examine two sets of mediators, directly 
targeted in our intervention: a) improvement in parenting practices, including positive reinforcement, 
limit setting, and supervision; and b) improvements in the girls’ relationships and skills, including peer 
and partner relations, refusal skills, and goal setting skills. Analyses will focus on whether these two 
domains partially mediate the effects of the SHARP intervention on the developmental outcomes. 
Trajectories of drug use, sexual-risk behaviors, and delinquency will be examined separately, and 
together, in relation to the two proposed sets of mediators.  
 
3. To Examine the Influence of Girls’ Knowledge and Comfort Around Safe Behaviors on Her Drug 
Use and Sexual-Risk Behaviors. Our prior work with girls in the juvenile justice system indicates that 
they have poor knowledge about safe sex practices and the effects of drugs. Further, a lack of comfort 
talking about safe sex practices exacerbated the odds contracting a sexually-transmitted infection (Leve 
et al., 2013). In this Aim, we will examine the role that comfort in discussing safe behaviors and 
knowledge about safe sex and drug use behaviors has on moderating the intervention effect on 
trajectories of drug use and sexual-risk behaviors. 
 
4. (Exploratory). To Examine Whether Brain Development Moderates the Effect of the Intervention 
on Developmental trajectories of Drug Use, Sexual-Risk Behaviors, and Delinquency Observed 
in Aim 1 (above). We will employ indices of structural and functional brain development as identified in 
previous studies (Bloch et al. 2013; Dosenbach et al., 2010) in a subset of 25 participants, in order to 
identify potential salient neurodevelopmental moderators of treatment outcomes.  
 
Study Protocol 
 
Recruitment and retention. The sample will consist of 123 girls between the ages of 13 and 18 and 
her primary caregiver (123 primary caregivers). Girls will be eligible if they are: (a) female; (b) age 13–18 years 
at enrollment; (c) reside in Lane County and within 70 miles of the University of Oregon at the time of 
enrollment; (d) have a current caregiver; (e) and both girl and caregiver are fluent in English. Girls will not be 
eligible for the study if they (a) are medically fragile; (b) have a significant developmental disability; or (c) have 
graduated from high school or have a GED. The sample will be recruited in three annual cohorts, with 
approximately 40 participants each year (~20 intervention, ~20 control/year). A subset of 25 girls in the full 
sample will be drawn from the final cohort to complete an MRI assessment to address Aim 4. 
To recruit the sample, the research team will work collaboratively with the Department of Youth 
Services in Lane County (DYS) and other community and school-based organizations. For recruitment through 
community agencies, study staff will provide information about the project and recruitment flyers to liaisons at 
each agency. Interested families will contact the study recruiter to complete eligibility screening and provide 
contact information. For recruitment through DYS, a designated study liaison working at these organizations 
will identify female youth who meet study age eligibility requirements. The liaison will contact eligible families to 

provide them with basic information about the study, using the study’s recruitment script. Interested families will 
complete a short screening questionnaire with the liaison. If the family is determined to be eligible, the liaison 
will collect contact information from the family.  During regular meetings, the liaison will provide the study 
recruiter with the contact information for families who meet study eligibility criteria. 
Once the study has received contact information from a potential participant, the recruiter will call the 
family to provide specific information about the study using the recruitment script. If the girl and her caregiver 
decide to participate in the study, the recruiter will schedule a time for an in-home visit.  At the home visit, both 
written and verbal explanations of the project will be presented to the caregiver and the youth, along with an 
opportunity to ask questions about the project. If they agree, caregivers and youth age 18 will sign a study 
consent form and youth > age 18 will sign a study assent form.  
We will use several strategies that we have found facilitate high retention in studies with DYS-involved 
populations, including adequate compensation for participation, assistance with transportation costs, incentives 
for updating contact information, and continuity in the staff member responsible for family contact. In addition to 
tracking placement change information, we have found that we can improve retention for follow-up 
assessments by sending holiday and birthday cards and study newsletters. Finally, the teen and caregivers will 
be asked to provide contact information for people who might help locate them and permission to contact those 
people.  
 
Assessment. Data will be collected through three 2-hour in-person interviews with the teenage girl and 
her primary caregiver. The interviews will take place either at the participant’s home or at the university 
research center, whichever is most convenient for the participant. The first interview (baseline) will be 
scheduled immediately after recruitment. The second two interviews will occur 6- and 12- months after the 
baseline interview, respectively. The caregiver and youth interviews will consist of self-administered questions 
on a computer and interviewer-administered questions. For the drug use and sexual-risk behavior questions, 
the interviews and questionnaires have skip patterns, such that youth who report never engaging in any drug 
use (or sexual-risk behavior) are not asked the follow-up questions about frequency, etc. A short, 10-minute 
discussion activity involving the teen and her caregiver will be videotaped at the 6-month assessment. Child 
welfare records pertaining to incidents where the youth participant was reported to be a victim of child 
maltreatment or neglect will be requested from the State of Oregon. Staff will also collect juvenile court 
information about the girl from the Department of Youth Services. At the 12-month interview, the teen will 
provide a urine specimen for a urinalysis (UA). The UA will reveal the presence or absence of a class of drugs 
in the urine specimen and a second assay will be completed for a subset of youth at the Levitt Laboratory at 
Children’s Hospital Los Angeles (CHLA) to measure isoprostane levels (a measure of oxidative stress).  
 
Intervention. Three intervention groups will be conducted per year (7-8 caregivers per parenting 
group), held on a rolling basis. Girls will be randomly assigned to intervention or control conditions during the 
baseline assessment home visit, where caregivers will be presented with two sealed, blank envelopes from 
which to choose. A slip of paper in the envelope will determine study condition (the randomization procedure 
will target blocks of 20, representing 10 girls in SHARP and 10 corresponding control girls; 10 pieces of paper 
labeled “SHARP group” and 10 pieces of paper labeled “Usual Services” will be placed in 20 separate 
unlabeled envelopes). 
 
 
The intervention (Safe, Healthy, Adolescent Relationships and Peers; SHARP) has two components: a 
caregiver parenting group that meets weekly for 90-min for 14 weeks, focused on increasing parenting skills, 
and a Life Coach component where trained and supported skills coaches meet individually with youth weekly 
for 60 min over the same period to build the girls’ social skills and peer/partner relationships skills. Both 
intervention approaches have been implemented successfully in our previous efficacy trials. We will include all 
caregiver types (biological, foster, kinship) in parenting groups together. The sessions for the components are 
based on manualized curricula. Sessions focus on topics such as drug use, HIV-risk behaviors, parental 
supervision, communication, and peer relations. All intervention staff attend a 5-day experiential training where 
they are oriented to the theory and perform each of the intervention sessions with other participants playing the 
role of the caregiver or youth. During the weekly sessions, caregivers and youth are given specific home 
practice assignments to complete during the week. Intervention sessions are structured to first debrief the 
home practice, then discuss new content as it relates to the ongoing experiences of the caregivers/youth. 
Finally, a new home practice is given.  
 
 
 
 

Statistical Analysis Plan 
 
Data will primarily be collected using a computerized program, which allows ready access to the 
dataset for analysis. The data management and analysis team will clean and check the data on an ongoing 
basis to ensure quality of the data following the centralized data management protocols. Below we provide 
descriptions of our procedures for construct development and handling missing data, our hypothesis-testing 
approach (with specific examples relevant to each Specific Aim), and power analyses.  
 
Construct Development. To avoid inflation of the Type I error rate due to multiple comparisons, we 
will employ latent construct-building strategies that have standardized criteria for combining various sources of 
information referring to specific higher-order concepts into latent construct measures (Patterson & Bank, 1986). 
Multiple indicator composites for core theoretical constructs will be developed where feasible, thus increasing 
measurement fidelity through stringent requirements of convergent, discriminant, and construct validity 
(Campbell & Fiske, 1959; Cronbach, 1970). For example, the parenting behavior construct will include youth 
reports and caregiver reports on parenting behavior. Our criteria for including an indicator in a construct are as 
follows: the items comprising the indicator must show internal consistency (α ≥ .6; item-total correlation ≥ .2), 
and the scale or indicator created must converge with other indicators designed to assess the construct (the 
factor loading for the forced one-factor solution ≥ .3). Some exceptions to these criteria may exist. For 
example, official arrest records are not necessarily expected to correlate highly with adolescent self-report of 
delinquent behavior. When disagreements exist between different methods/informants of specific constructs, 
we will analyze measures separately.  
We expect very little missing data over time due to our data collection and data checking procedures, 
and high retention rates in our previous RCT studies involving similar families. We will first examine the nature 
of missing data (whether or not the data are missing at random [MAR]) and the pattern of missing data with 
other predictor variables. Within the context of multiple regressions and multivariate analysis of variance, we 
will use multiple imputation strategies (Schafer, 1997). For other analyses using latent variable framework 
(e.g., latent growth model), we will utilize full information maximum likelihood (FIML) estimation, which enables 
modeling of data sets that have missing data (i.e., the full sample will be used in the analyses without listwise 
deletion; Curran & Hussong, 2003). FIML is known to produce less biased estimates than listwise deletion, 
even when the missing at random (MAR) assumption is not strictly met (Arbuckle, 1996). 
 
Analysis Approach. We will rely primarily upon latent growth modeling (LGM) using Mplus 6.12 
(Muthén & Muthén, 1998-2010) and the r statistical package to take advantage of the longitudinal nature of the 
study design. The description below presents specific examples of analyses that will be used to test each Aim. 
Note that demographic characteristics, intervention dosage, caregiver type (foster, biological), and sample 
(DYS or other) will be included in models as control variables. Early adversity will be included as a covariate. 
We will use an intent-to-treat approach to all analyses, including all participants in their randomly assigned 
intervention group, regardless of how many sessions were received. 
Aim 1. To Examine the Effect of the SHARP intervention on the Developmental trajectories  of Drug 
Use, HIV-Risk Behaviors, and Delinquency. We hypothesize that girls in the SHARP intervention will show 
significantly lower levels of drug use, HIV-risk behaviors, and delinquency during follow-ups than the girls in the 
services-as-usual (SAU) condition. We will test intervention effects using a latent growth modeling (LGM) 
approach, whereby we will examine group differences in the slope of developmental trajectories of each 
outcome across T1–T3, controlling for factors described above. As an example, we will examine a two-factor 
LGM (the intercept and slope) of marijuana use across baseline (T1) through the 12-month follow-up (T3). We 
hypothesize that the SHARP girls will show significantly lower levels of growth in marijuana use over time than 
the SAU girls. LGM offers the advantage of testing both the longer-term effects of the SHARP intervention and 
also to model developmental trajectories. Our assessment includes the same measures at every wave and 
data are thus particularly well-suited to LGM analyses. Parallel models will be used to examine developmental 
trajectories of each substance (alcohol, tobacco, marijuana, and all other illicit drugs), HIV-risk behaviors, and 
delinquency, as well as their co-occurrence.  
Aim 2. To Examine Mediating Processes of the Developmental Trajectories of Drug Use, HIV-Risk 
Behaviors, and Delinquency. This Aim addresses two domains of mediating processes that lead to drug use, 
HIV-risk behaviors, and delinquency in girls with risk histories. A latent variable model will be used to test the 
hypothesis that caregivers’ parenting practices at T2 mediate the SHARP intervention effect on outcomes at 
T3. We hypothesize that intervention effects will be significantly related to outcomes (drug use, HIV-risk 
behaviors, delinquency) at T3 directly and indirectly through parenting behaviors at T2. By comparing the path 
weights, this test will also allow us to understand the relative strength of immediate and longer-term 

intervention effects. All three sets of outcomes (drug use, HIV-risk behaviors, delinquency) will be tested for 
each of the two hypothesized mediating process separately and simultaneously. In addition, both mediating 
processes can be examined simultaneously in the model. In the course of these mediational models, we will 
also examine main effects of the intervention on the mediators. 
Aim 3. To Examine the Influence of Girls’ Knowledge and Comfort Around Safe Behaviors on Her Drug 
Use and HIV-Risk Behaviors. We expect moderation of intervention effects by girls’ knowledge about safe sex 
practices, her knowledge of drug use risks and norms, and her comfort talking about safe sex practices. We 
expect that significant intervention effects on drug use and HIV-risk behaviors will vary depending on girls’ level 
of knowledge and comfort. Lack of knowledge and lack of comfort are expected to potentially reduce 
intervention effects. The direct effect model can be expanded to test the potential moderating effects of girls’ 
knowledge of safe sex practices and drug use risks and norms.  
Aim 4 (exploratory). Indices or brain maturation will be added to the analyses described in Aim 1 to 
examine the main effect and interaction of brain maturation on intervention outcomes for the three sets of 
outcomes (drug use, HIV-risk behaviors, delinquency). 
 
Power. In line with the results of our previous work on girls, we expect a medium effect size for the 
proposed intervention. Power analyses were conducted for two key models to determine whether the proposed 
sample size (N = 123) would be sufficient to detect the hypothesized effects with alpha set at .05 and power of 
at least .80 (Cohen, 1988). We conducted Monte Carlo simulations in Mplus (Muthén & Muthén, 2002) based 
on the LGM pictured using 5,000 draws with α = .05. We used an estimate of 10% missing data across time in 
Aim 1. For a medium effect size (d ≈ .45), all resulting values for the parameter bias (< 10%), standard error 
bias (< 10%), and coverage (< .5%) satisfied the recommended criteria (Muthén & Muthén, 2002). 
Furthermore, power to detect intervention effects on the slope factor was .81. We conducted another Monte 
Carlo analysis to examine power for the mediated model described in Aim 2 including the four control variables 
listed above. Again, five thousand draws, assuming medium effect sizes (coefficient range = .4–.45) with 10% 
missing data over time were specified to ensure robustness of the results. All values for the parameter bias, 
standard error bias, and coverage satisfied the recommended criteria (Muthén & Muthén, 2002) and power to 
detect direct and indirect intervention effects ranged from .81 to 1.00 in the model. Thus, even though our 
sample size is modest, current power analyses suggest that it will be sufficient to test all hypothesized models 
(detect medium effect sizes with power of at least .80).