Conditions: Cannabis Use Disorder
Keywords: Marijuana Treatment
Allocation: RANDOMIZED | Intervention model: PARALLEL
Enrollment: 68
Healthy volunteers: no

Brief summary:
The proposed protocol is a double-blind, placebo-controlled inpatient and outpatient study,looking at the clinical treatment of cannabis use disorder.

The treatment study is a total of 12 weeks. There will be two options offered to participants for week 1 of the treatment study. 1) Patient will go inpatient for 5 nights and after discharge from the inpatient phase will complete the 11-weeks of outpatient treatment or 2) patients who cannot complete the inpatient phase due to work or other obligations will complete the treatment 12-week study outpatient. 80 patients seeking treatment for cannabis use disorder will be enrolled into either the inpatient/outpatient or only outpatient study. This combined design will provide a comprehensive understanding of clonazepam's effects on individuals with cannabis use disorder across a range of outcome measures while also testing the medication's ability to prevent relapse in cannabis-abstinent patients.

Detailed description:
Patients seeking treatment for Cannabis Use Disorder (CUD) will be enrolled into an inpatient laboratory for 5 nights, where they will be initiated on medication and be assessed for the influence of clonazepam (or placebo) on (1) cannabis withdrawal (mood, sleep, cannabis craving, food intake), ratings associated with medication abuse liability, cognitive performance, and (2) relapse to cannabis use after patients (now abstinent from cannabis) leave the inpatient setting maintained on clonazepam (or placebo) for 8 weeks (with a 4-week, medication-free follow up). This combined design will provide a comprehensive understanding of clonazepam's effects on individuals with cannabis use disorder across a range of outcome measures (safety, abuse liability, withdrawal symptoms) while also testing the medication's ability to prevent relapse in cannabis-abstinent patients. Patient's who are unable to complete the inpatient approach will complete the 12 week trial as an outpatient only.

Arms:
arm 1: Clonazepam will be taken twice per day in the morning and in the evening. Clonazepam is given in a "fixed flexible" dose schedule with the dose titrated to 2mg per day or the maximum tolerated dose. Clonazepam will be taken for the first 8 weeks of the trial. arm 2: Placebo will be taken twice per day in the morning and in the evening. Placebo will be taken for the first 8 weeks of the trial.

Interventions: intervention 1: Clonazepam intervention 2: Placebo
Intervention descriptions: intervention 1: fixed-flexible daily dose to a maximum of 2 mg (1 mg twice per day) for the first 8 weeks of the trial intervention 2: None

Protocol document:
Prot_000.pdf: 
Study: # 7343 entitled EFFECT OF CLONAZEPAM ON CANNABIS WITHDRAWAL AND RELAPSE IN 
TREATMENT-SEEKING PATIENTS: COMBINED INPATIENT/OUTPATIENT STUDY 
 
PI: John Mariani, M.D. 
NCT# NCT02913924 
Study Protocol Date: 8/30/2016 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 



















































SAP_001.pdf: 
Study: # 7343 entitled EFFECT OF CLONAZEPAM ON CANNABIS WITHDRAWAL AND RELAPSE IN 
TREATMENT-SEEKING PATIENTS: COMBINED INPATIENT/OUTPATIENT STUDY 
 
PI: John Mariani, M.D. 
NCT# NCT02913924 
 
Statistical Analysis Plan (Grant): 9/19/2013 
Amended 4/15/2016 (DSMP) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 


 
Grant Submission U54 DA037842: 9/19/2013 
 
 
3. Data Analysis a. Outcome Measures and Covariates  
i. Primary outcome measures: 1) Cannabis use: The time to sustained relapse defined as 5 days of 
cannabis use over a 7 day period as recorded by the Timeline Followback method and confirmed by 
creatinine-normalized quantitative urine THC levels, or time to dropout of study, whichever comes first 
(survival outcome).  
ii. Secondary outcome measures: These measures are designed to capture changes in cannabis 
consumption patterns and other symptoms not measured by the primary outcome measures.  
1) Abstinence during the final two weeks (study weeks 11 and 12) of the study (dichotomous)  
2) Retention in treatment (survival outcome)  
3) Days per week of cannabis use (continuous longitudinal)  
4) Cannabis withdrawal: measured by weekly Marijuana Withdrawal Checklist (continuous longitudinal) 
5) Cannabis craving: measured by weekly Marijuana Craving Questionnaire (continuous longitudinal)  
6) Sleep disturbance: measured by the Medical Outcomes Study—Sleep Scale (continuous longitudinal)  
iii. Covariates: 1) Demographic characteristics (e.g., gender, age) 2) Baseline severity of cannabis use 
(measured by the quantity of cannabis per using day at baseline)  
iv. Other measures: Adverse effects, as measured by the Systematic Assessment for Treatment and 
Emergent Events (SAFTEE), will be assessed, including potential effects on compliance and outcome.  
b. Sample Size and Randomization: The Research Core will supervise randomization procedures. A total 
of 72 patients will be recruited over a 2.5-year period and randomized to the double-blind treatment 
trial, with 36 patients randomized to each of the two treatment groups, nabilone/ XR-NTX arm and 
placebo arm. The randomization sequence will be balanced in blocks of random size (2, 4, 6) to prevent 
clinicians from guessing what the next patient’s treatment might be.  
c. Intent to Treat / Dropouts and Missing Data: The primary analyses in this study will be on the intent-
 
to treat (ITT) sample, i.e. on all randomized paƟents. If paƟents drop out of the study, they will be 
regarded as relapsed in the primary outcome analysis. This seems a safe assumption as it seems most 
likely dropouts will relapse. Cannabis consumption and cannabis abstinence, are assessed repeatedly 
over time and we will try to collect them at all study assessment points. We will account for unobserved 
data in secondary outcome variables using longitudinal mixed effects models (MEM) (Brown and 
Prescott 1999, Diggle et al. 2002) using PROC GLIMMIX in SAS®. MEMs do not require complete 
measurements data to estimate the outcome variable. The inferences from analyses with missing data 
are valid provided that they are “missing at random” (Little & Rubin, 2002). ‘Missing at random’ (i.e. the 
missing mechanism does not depend on the value of the unobserved outcome) is un-testable in most 


medical research and in our study as well. One can assume either parametric or semi-parametric models 
for the missingness that does depend on the unobserved outcome value and do the analysis (Diggle and 
Kenward 1994, Kenward 1998, Rotnitzky et al. 1998, Liu et al. 1999, Scharfstein et al. 1999). Comparison 
of the inferences from assuming various models for the missingness provides a measure of the validity 
of the efficacy estimate from the model that assumes missing ‘at random’. One can also compute a local 
sensitivity index which measures the change in the estimated treatment effect in a neighborhood of the 
‘missing at random’ model for missingness (Rotnitzky et al. 2001). We plan to perform a sensitivity 
analysis based on these two approaches to assess the effect of the assumption of missing ‘at random’ on 
the inference.  
d. Significance Testing and Preliminary Analyses: All tests for main effects will be performed at two-
tailed significance α=5%. Before performing specific analyses (described below), we will examine all 
variables for outliers. The distributions of all continuous variables will be checked for normality, and 
transformations will be employed, if necessary, before applying specific parametric techniques. The 
distribution of demographic variables (ethnicity, gender, age) and other covariate measures of at 
baseline in the treatment arms will be examined and described in terms of means, standard deviations, 
proportions and 95% confidence intervals. The covariates (specified in Section 3.a.iii) may be associated 
with treatment outcome. For this reason, we will adjust for these covariates in all models used to test 
the study hypotheses. These covariates will be included in all models as main effects regardless of their 
statistical significance or whether they differ between treatment groups. In the secondary analyses, we 
will adjust also for baseline value of the outcome variable where appropriate. This adjustment will be 
based on the inclusion of main effects for the baseline.  
e. Hypotheses Testing i. Primary hypotheses:  
Hypothesis 1: Nabilone and XR-NTX will significantly increase time to relapse as compared to placebo. 
The following Cox Proportional Hazards model will be used: (1) log{hi(t)} = log{h0(t)} + β1Ij + β2Uij where 
hij(t) is the hazard for individual i in the treatment group j at time t; h0(t) is the baseline hazard function; 
Uij is the vector of covariates; and Ij is the indicator variable for treatment with nabilone and XR-NTX. 
Significant Iij indicates that the hazard rate of each treatment group is different. ii. Secondary 
hypotheses:  
Hypothesis 2: Nabilone and XR-NTX will significantly promote abstinence from cannabis use as 
compared to placebo during the final 2 weeks of study (weeks 11 and 12). The following logistic 
regression model will be used: (2) logit(Yij = 1) = β0 + β1Ij + β2Uij + εij where Yij is a dichotomous 
measure indicating whether the i th subject in the treatment group j has used cannabis during the final 2 
weeks of study, with Yij = 1 for non-abstinent and Yij = 0 for abstinent during the final 2 weeks of study; 
Uij is the vector of all appropriate covariates; Ij is the indicator variable for treatment with nabilone and 
XR-NTX; and εij is a random error term. Significant Ij indicates that the odds of abstinence from cannabis 
during the final 2 weeks are different in each group.  
Hypothesis 3: Nabilone and XR-NTX will significantly increase time to dropout as compared to placebo. 
To test the secondary hypothesis 3, we will use the same model as in (1) to test whether retention is 
different between the two groups.  
Hypothesis 4: Over time, the days of weekly cannabis use will be significantly less in the Nabilone and 
 
XR NTX group as compared to the placebo group. This hypothesis will be tested using the following 


longitudinal mixed effects model: (3) Yijt = β0 + β1Ij + β2t + β3t*Ij +β4Uij + sij + εijt where Y ijt is the days 
of cannabis used over a one-week period by the i th subject in the treatment group j at week t (t =1, 2, 
…, 12); Uij is the vector of covariates; Ij is the indicator variable for treatment with nabilone and XR-NTX; 
sij is a random intercept for subject i and εijt is a random error term. Significant interaction t*Ij indicates 
that the effect of each treatment group is different over time (that corresponds to rejecting null 
hypothesis that β3= 0). If so, the effect of time will be estimated for each group separately and the 
groups will be compared (using contrast) in the last time point t=12. If the interaction term is not 
significant (i.e., the difference between the treatment arms does not change over time), we will refit the 
model without the interaction term and test the significance of the main effect of treatment (i.e. 
rejecting the null hypothesis that β1 = 0).  
Hypothesis 5: Over time, subjects in the nabilone and XR-NTX group will experience significant reduction 
in the pattern of cannabis withdrawal symptoms (measured as mean Marijuana Withdrawal Checklist 
score per week) compared to the subjects in the placebo group. 
Hypothesis 6: Over time, subjects in the nabilone and XR-NTX group will exhibit significantly reduced 
cannabis cravings (as measured by Marijuana Craving Questionnaire) compared to subjects in the 
placebo group.  
Hypothesis 7: Over time, subjects in the nabilone and XR-NTX group will experience significantly less 
sleep disturbance (as measured by the Medical Outcomes Study Sleep scale) compared to subjects in 
the placebo group. To test the secondary hypotheses 5, 6, and 7 we will use the same model as in (3) 
where Yijt is the appropriate outcome variable (as specified for each hypothesis) for the i th subject in 
the treatment group j at week t (t =1, 2, …, 12). We will first test the interaction effect of group and time 
as described for Hypothesis 4.  
iii. Exploratory Analyses We will explore whether there are differences between the two groups with 
respect to the number of adverse effects, side effects, and tolerability in relation to the primary 
outcome. We will also explore whether co-occurring psychiatric disorders, gender, concurrent treatment 
exposure, trauma history, or impulsivity are predictors of treatment outcome. 
 f. Power Analysis: The sample size of 72 was chosen to ensure sufficient power (at least 80%) of a two-
sided test with level of significance α=0.05 for detecting difference between the two experimental 
treatments with respect to time to relapse, based on the Cox proportional hazards model. We expect 
that the nabilone and XR-NTX group will have greater time to relapse than the placebo group. The rate 
of retention for the placebo group at 12 weeks can be expected to be 15% based on our prior studies. 
With 36 per arm, we will be able to detect a difference in retention (without relapse) rates between 
placebo versus nabilone and XR-NTX of approximately 25% (i.e. 15% on placebo vs 39.6% on nabilone 
and XR-NTX). This corresponds to a hazard ratio of 0.49 (Machin et al. 1997). The proposed sample size 
will also enable us to estimate the retention rate of the treatment group with a 95% confidence interval, 
±15%, in the worst case. The computation above was performed assuming that the covariates (i.e., 
baseline cannabis use and age) do not have a predictive power. The 25% rate difference is consistent 
with a medium to large sized effect, which is what we are seeking in this early Phase II trial. This 
represents an effect that would be clinically meaningful and likely to make a substantial impact on the 
success of XR-NTX treatment in clinical practice 
 


Amended for Data and Safety Monitoring Plan 4/15/2016 
 
A2. Primary and Secondary Outcome Measures Primary Outcome Measure: (1) Cannabis withdrawal 
(mood, sleep quality and duration, food intake) (2) Cannabis relapse defined as (1) time to any MJ use 
as recorded by the timeline follow back method and confirmed by urine metabolite levels, or (2) time 
to dropout, whichever comes first, and (3) medication compliance (% of pills taken) or time to 
discontinuation. Participants who relapse will continue in the trial to obtain secondary outcome 
measures. Secondary Outcome Measures: (1) Abuse liability (medication liking, desire to take again) 
(2) Cognitive task performance (3) Side effects and tolerability. 
A4. Power calculation and sample size Cannabis Withdrawal. We hypothesize that clonazepam will 
decrease symptoms of cannabis withdrawal (e.g. ,irritability, sleep efficiency). With a sample size of 
80, the test of a single contrast at the 0.050 level will have >90% power to detect a 25% decrease in 
these ratings under conditions of medication administration compared to placebo, assuming the same 
between-level correlation and SD than our earlier studies. Cannabis Relapse: The time to relapse as 
recorded by the Timeline Follow Back method and confirmed by creatinine-normalized quantitative 
urine THC levels, or time to dropout of study, whichever comes first (survival outcome). The sample 
size of 80 was chosen to ensure sufficient power (at least 80%) of a two-sided test with level of 
significance 5% for detecting difference between the two experimental treatments with respect to 
time to relapse, based on the Cox proportional hazards model. We expect that the clonazepam group 
will have greater time to sustained relapse than the placebo group. The rate of survival for the placebo 
group can be expected to be 15% based on our prior studies. With 40 per arm, we will be able to 
detect a difference in proportion of surviving in placebo versus clonazepam of approximately 27% (i.e. 
15% on placebo vs 41.81% on clonazepam). This corresponds to a hazard ratio of 0.46 (Machin et al. 
1997). The power computation was performed assuming that the covariates (i.e., baseline cannabis 


use and age) do not have any predictive power. If they do, the power of the primary hypothesis will 
be higher than 80%. The 26.81% rate difference is consistent with a medium to large sized effect, 
which is what we are seeking in this early Phase II trial. This represents an effect that would be 
clinically meaningful and likely to make a substantial impact on the success of clonazepam treatment 
in clinical practice. The proposed sample size n=40 for clonazepam group will also enable us to 
estimate the relapse and retention rates of the treatment group with a 95% confidence interval, 
±15.5%, in the worst case. 
C. DATA MANAGEMENT AND ANALYSIS C1. Data Acquisition and Transmission Research assistants 
trained in behavioral observation techniques will assist in data collection during laboratory sessions. 
Subjective responses, performance effects, food intake and sleep will be obtained electronically using 
our custom-designed software programs. Some data (e.g., sleep questionnaires) will be recorded 
manually, and then entered into an Excel spreadsheet. C2. Data Entry Methods As noted above, a 
portion of the data is automated and a portion is recorded on data sheets, and transferred manually 
to an Excel spreadsheet. C3. Data Analysis Plan Subjective effects, food intake, sleep, and task 
performance will be analyzed using repeated measures analysis of variance (ANOVA) models with 
planned comparisons calculated with SUPERANOVA, SPSS or SYSTAT packages. We will use a cluster 
 
analysis of peak subjecƟve eﬀects raƟngs to reduce the overall number of comparisons (Haney et al., 
2013a,b). Tests of differences will be based on F-statistics with degrees of freedom corrected, 
depending on the observed within-subject correlation of the measures, using the method of Huynh 
and Feldt. Planned contrasts will be single degree of freedom comparisons using the appropriate 
interaction error term. Results will be considered statistically significant at p < 0.05, using 2- tailed 
tests. The primary analyses in this study will be on the intent-to-treat (ITT) sample, i.e. on all 
randomized patients. Cannabis consumption and cannabis abstinence, are assessed repeatedly over 
time and we will try to collect them at all study assessment points. We will account for unobserved 


data by examining the primary outcome variables using longitudinal mixed effects models (MEM) 
(Brown & Prescott, 1999; Diggle, et al., 2002) using PROC GLIMMIX in SAS®. MEMs do not require 
complete measurements data to estimate the outcome variable. The inferences from analyses with 
missing data are valid provided that they are “missing at random” (Little & Rubin, 2002). ‘Missing at 
random’ (i.e the missing mechanism does not depend on the value of the unobserved outcome) is un-
testable in most medical research and in our study as well. One can assume either parametric or semi-
parametric models for the missingness that does depend on the unobserved outcome value and do 
the analysis (Diggle & Kenward, 1994; Kenward, 1998; Liu, et al., 1999; Rotnitzky, et al., 1998; 
Scharfstein, et al., 1999). Comparison of the inferences from assuming various models for the 
missingness provides a measure of the validity of the efficacy estimate from the model that assumes 
missing ‘at random’. One can also compute a local sensitivity index which measures the change in the 
estimated treatment effect in a neighborhood of the ‘missing at random’ model for missingness 
(Rotnitzky, et al., 2001). We plan to perform a sensitivity analysis based on these two approaches to 
assess the effect of the assumption of missing ‘at random’ on the inference.