Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT03203889 |
| Other study ID # |
947549-9 |
| Secondary ID |
R34DA040064-01A1 |
| Status |
Completed |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
June 27, 2017 |
| Est. completion date |
July 25, 2021 |
Study information
| Verified date |
April 2022 |
| Source |
University of New Mexico |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
It is important to address the substantial substance-related health disparities of American
Indians (AI). This project aims to examine the effectiveness of a culturally tailored
Community Reinforcement and Family Training (CRAFT) approach and Twelve-step facilitation
with Concerned Significant Others (TSF-CSOs) among AIs to increase engagement of treatment
refusing individuals into treatment/healing and to reduce distress of their loved ones. Study
hypotheses are that (1) CRAFT will result in higher numbers of people entering treatment for
substance use disorders than will TSF-CSO, (2) both groups will yield similar improvements in
the family member's functioning, and (3) we will explore potential factors of the treatments
to see which aspects of the treatment are most important and to test which characteristics of
the clients impact the outcomes for better or worse. This knowledge may impact dissemination
and diffusion efforts for CRAFT-AI and other evidence-based treatments among AIs and other
culturally diverse groups.
Description:
Treatment research with AI/AN is in its infancy. One route to address the substantial
substance use related health disparities is to employ evidence-based treatments (EBTs) with
AI/AN. EBTs are rarely tested with sufficiently large samples of racial ethnic groups, so
outcomes are often unknown. Recently, an R01 DA021672 adapted two EBTs for substance use
disorders (SUDs) (motivational interviewing and community reinforcement approach - MICRA) and
conducted a randomized controlled trial (RCT) to MICRA to treatment as usual with the same AI
community as involved in this proposal. The present study seeks to expand on that R01, which
culturally tailored CRA for people with SUDs, but now turn our focus to working with families
and concerned significant others (CSOs) who want to help engage their loved one with an
addiction (identified person; IP) into treatment. The intervention, Community Reinforcement
and Family Training (CRAFT), is built on reinforcement principles. CRAFT teaches CSOs to take
better care of themselves, to cease any behaviors that are not working to address the IP's
substance use, and to consistently reward the IP for any sober and treatment engagement
behavior. Importantly, CRAFT has been shown to increase IP entry into treatment, which would
address: (1) the high rates of SUDs and related problems among AI/AN, (2) low rates of AI/AN
self-referrals to treatment, and (3) higher rates of AI entering treatment due to court
mandates. The present study aims to conduct an RCT to compare an AI culturally adapted
version of CRAFT (CRAFT-AI; n=20) to Twelve-step facilitation for CSO's (TSF-CSO) using
principles and groups of Nar/Al-Anon (n=20) to examine acceptability of a culturally tailored
EBT and estimate effect sizes for IP treatment entry and for CSO functioning pre to post
intervention. Notably, this will be the first examination of potential mediators and
moderators of CRAFT-AI including variables such as: demographic variables, severity of SUD,
self-efficacy, and cultural risk and protective factors. Finally, we will examine the
appropriateness of broadening the dependent variable of IPs entering formal treatment to
include AI traditional healing. The methodology of this grant using a partnership between
academic and tribal researchers is well-poised to contribute to nascent AI/AN SUD treatment
research to impact the substantial health disparities AI/ANs and other indigenous populations
endure. Positive study results will facilitate future cultural adaptation research and
dissemination and diffusion of EBTs to interested AI/AN tribes and other culturally diverse
populations.
Quality Assurance 1. For quality assurance of data collection, Roberta Chavez, who has over
25 years of experience working on clinical trials at University of New Mexico (UNM)/Center on
Alcoholism, Substance Abuse, and Addiction (CASAA), will train the research assistant and
conduct data quality assurance checks monthly. Data entry will be conducted with two passes.
For the intervention, all counselors will receive expert training in either CRAFT-AI or
TSF-CSO. All counselors will be certified in CRAFT-AI or demonstrate proficiency in TSF-CSO
prior to the RCT using a CRAFT coding manual and TSF-CSO coding forms. All counseling
sessions will be digitally recorded and coded for fidelity to the treatment modalities. When
counselors do not pass a CRAFT module or TSF-CSO component, they will receive individualized
training and remediation to meet fidelity requirements.
Transmission
a. All data will be collected using pre-specified measures, instruments, and qualitative
questions. b. Entry of data will occur within 30 days after it is collected. Second pass
within 60 days. c. Statistician, Katie Witkiewitz, will conduct analyses to insure data
fidelity upon completion of data entry and reconciliation of any discrepancies.
Regulatory Issues 1. Reporting mechanisms of adverse events (AEs) and serious adverse events
(SAEs) to Institutional Review Board (IRB) and National Institute on Drug Abuse (NIDA). The
PI will report any SAE, whether or not related to study intervention to the UNM IRB, NIDA and
the Zuni Tribal Council within 48 hours of the principal investigator's (PI's) notification
of the SAE. Outcomes of SAEs will be reported to NIDA and the Zuni Tribal Council and UNM IRB
as they become known. A summary of the SAEs that occurred during the previous year will be
included in the annual progress report to NIDA, which will also be sent to the Zuni Tribal
Council.
2. Reporting mechanisms of IRB actions to NIDA The PI will be responsible to report any IRB
actions to NIDA.
3. Trial Stopping Rules In the event that those participants in the intervention arm
receiving CRAFT-AI counseling experience a significantly higher rate of SAE's that are
probably study related than those participants in TSF-CSO arm of the study, then the trial
will be stopped.
Potential risks and benefits for participants The primary risks involved in this research
project are: a) loss of confidentiality, b) discomfort talking about problems caused by loved
one with a substance use problems, c) potential disclosure of own substance use problems, and
c) potential for conflict between the subject and loved one. Steps have been taken to
minimize the likelihood of these risks and to ameliorate them if they are present. Past
studies have assessed for level of conflict and instituted safety plans in case domestic
violence should be imminent or occur.
Participants may potentially benefit from receiving the CRAFT-AI or TSF-CSO interventions in
terms of improved psychological functioning such as decreased anxiety and depression. In
addition, their loved one may enter treatment.
Trial Efficacy Plans for Interim Analysis of Efficacy Data: We have no plans for interim
analyses.
Data analysis plan Analysis Plan. Data Integrity Preliminary Analyses: First we will compare
demographics and primary study measures at baseline between randomized treatment groups,
using ANOVA for continuous variables (or Kruskal-Wallis if parametric assumptions are
violated) and using χ2 tests for categorical variables. Subsequent analyses will adjust for
significant baseline differences among the randomized groups. Additionally, the data will be
examined for both missing cases and outlier scores on measures. Variable distributions will
be checked for normality and if necessary, transformations will be performed to normalize the
distributions.
Statistical Analysis Plan Aim #1 Analyses for Adaptations: We will use a blend of CSP and the
ADAPT-ITT procedures to adapt CRAFT in partnership with this tribal community. Digital
recordings from the four focus groups will be transcribed and reviewed for themes and
important areas for cultural adaptations using NVivo software.
Aim #2: Analyses for pilot feasibility RCT (N=8) 3 month follow-up. The primary outcome of IP
treatment/healing engagement will be examined using Hedges's g effect sizes to correct for
small sample size bias with 95% confidence intervals of the effect sizes.
Aim #2a RCT (N=40): To estimate the effect of CRAFT-AI vs. TSF-CSO on IP treatment/healing
entry (primary outcome) from intake to 3- and 6-month follow-ups. The primary outcomes of IP
treatment/healing entry, and CSO functioning will be examined using an intent-to-treat
analysis with a generalized linear mixed effects model with fixed effects of treatment and
random effects of time. The model for IP engagement will be estimated using the binomial
distribution and a logit link function. Additional analyses of CSO treatment sessions
attended will be examined using a negative binomial hurdle model with a log link function,
which will allow us to simultaneously test the effect of treatment on engagement (yes vs no)
outcome and the count of sessions attended for CSOs with or without IP treatment entry.
Demographic, substance use, and psychological variables for which the randomized groups
differed significantly at baseline will be included as covariates in all models. In addition,
attendance at other mutual help groups such as Nar/Al-Anon will be assessed and used as a
covariate as necessary, especially for the CRAFT-AI CSOs. Models will be estimated using an
intent-to-treat approach that analyzes data from all randomized participants. We will use
maximum likelihood estimation for all analyses, which provides the variance-covariance matrix
for all available data. While attrition in the previous RCT was less than 7% at 12 months,
attrition analyses will determine whether there are any differences in study variables
between those with missing and complete data. Study variables associated with missing data
will be covaried in all analyses.
Statistical power for Aim #2a: The primary hypothesis is that participation in CRAFT-AI will
predict greater engagement of IPs in treatment/healing, as compared to those assigned to
TSF-CSO. Effect size estimates were drawn from a recent meta-analysis of CRAFT. Results from
the meta-analysis indicated that IP engagement rate following CRAFT was on average 67%, while
engagement IP rate following TSF-CSO was on average 18%, which results in an overall effect
size odds ratio of 9.25. Given this effect size for odds of IP engagement between groups with
α =.05 we will have 89% power with 18 subjects per condition (n=36) to detect the main effect
of treatment. Assuming 10% dropout, we propose to recruit 20 subjects per condition (n=40) to
assure a final sample size of 36.
Analyses for Aim #2b: To estimate the effect of CRAFT-AI and TSF-CSO on CSO functioning and
relationship functioning (secondary outcomes) from intake to 3- and 6-month follow-ups. The
secondary outcomes of CSO functioning and relationship functioning will be examined using
latent growth curve modeling. We hypothesize that the slope of CSO functioning and
relationship functioning will indicate that participants in both CRAFT-AI and TSF-CSO will
show significant decreases in depression and anxiety and significant improvement in
relationship functioning regardless of whether their IP enters treatment or healing ceremony.
Statistical power for Aim #2b: A Monte Carlo simulation study was conducted to determine
power for the latent growth curve models. The population parameter values for data generation
and coverage were taken from the parameter estimates of an analysis of prior alcohol
treatment outcome studies. We are primarily interested in testing the hypothesis that the
slope would be significantly greater than zero. Based on a sample size of 40 across both
conditions, we will have power greater than .80 (α<.05) to detect a slope that is
statistically different from zero.
Analyses for Aim #2c: While underpowered to identify all but large mediation effects, we plan
to obtain estimates of effect sizes for future studies that will examine possible mechanism
of change for CRAFT-AI and potential treatment moderators. Effect sizes for possible
mechanisms of change will be derived from mediation models estimated using the product of
coefficients method. Given that the distribution of the product of coefficients can be
non-normal we will use bootstrapping to obtain 95% confidence intervals of the mediated
effect. We will have power greater than .80 to detect large mediating effects with a final
sample size of 36. Effect sizes for potential treatment moderators will be derived by
including interaction terms (treatment x moderator) in the Aim 2a and Aim 2b analyses.
