Preventing Drug Abuse Among Sexual-Minority Youth
Phase: NA
Conditions: Substance Abuse
Sponsor: Columbia University

Preventing Drug Abuse Among Sexual Minority Youth 
 
NCT03954535 
 
June 20, 2025 
Study Protocol  
 
Background and Objectives: 
Sexual minority youths’ rates of drug use too often exceed those of their heterosexual 
peers. Sexual-minority adolescents are youth who self-identify as gay, lesbian, bisexual, 
or unsure of their sexual orientation. Sexual minority youth and heterosexual youth 
share many of the same risk factors, however, sexual minority youth have additional risk 
factors. Thus, tailored interventions to prevent sexual minority youth drug use are 
warranted.  
 
This study developed and tested a web-based, prevention program aimed at addressing 
the general and specific risk factors for drug use among sexual minority youth in the 
United States. The goals of the study were: 
1. Recruit a sample of sexual-minority youth, ages 15 & 16 years, from across the 
United States through online ads (e.g., Facebook, Instagram) 
2. Develop and program a tailored, skills-based drug abuse prevention program for 
online delivery to sexual-minority youth; develop the attention-placebo website. 
3. Deliver the prevention program to youth randomly assigned to the intervention arm; 
implement attention-placebo website procedures to youth randomly assigned to the 
control arm. 
4. Test the efficacy of the prevention program to: a) prevent drug use, and b) reduce 
rates of drug use initiation at posttest and 1-, 2-, and 3-year follow-ups, relative to 
youth in the attention placebo control arm. 
5. Test the efficacy of the program to change the mediators targeted in the intervention: 
skills related to goal setting, problem solving, drug refusal, and assertiveness; peer 
drug use; self-efficacy; normative beliefs; coping with stress; psychological distress; 
self-worth; and social supports at posttest and 1-, 2-, and 3-year follow-ups, relative 
to youth in the attention placebo control arm. 
 
Study Design 
The nationwide sample of 15- and 16-year-old youth (N = 1,216) was recruited via 
Instagram and Facebook. Enrolled youth were randomly assigned to the intervention or 
attention-placebo condition. All youth completed pretest measures online. Following 
pretest, intervention youth interacted with a 9-session, skills-based drug use prevention 
program online. The program aimed to reduce youths’ drug use and associated risk 
factors by improving their cognitive and behavioral skills around such areas as coping 
with stressors, enhancing self-worth, and refusing drug use offers. Youth in both 
conditions completed measures at posttest, 1-, 2-, and 3-year follow-up. Youth received 
$25 for pretest, $30 for posttest, $35 for 1-year follow-up, $40 for 2-year follow-up and 
$50 for 3-year follow-up; each survey required approximately 15 minutes to complete. 
 

Sample retention from baseline was 97% at posttest, 97% at 1-year follow-up, 95% at 2-
year follow-up, and 96% at 3-year follow-up 
 
Intervention 
Following completion of the online pretest, youth in the intervention condition were 
directed to the online program, Free2b. The program consisted of two components: the 
homepage and the intervention sessions. The homepage for the program was 
accessible at any time and included extraneous, but potentially engaging, features, 
including: horoscopes, fortunes, quotes, lifehacks, popular LGBTQ+ icons, a quote of 
the day, and a resource page. Guided by social learning theory and minority stress 
theory (Fig. 2), the intervention consists of nine sessions: goal setting, decision making, 
self-esteem, coping with stress, reframing distorted thinking, two sessions on assertive 
communication, drug use knowledge and norms, and a review.  
 
Youth assigned to the attention-placebo condition also had access to the Free2b 
website―identical to the website described above―but without the nine intervention 
sessions. 
 
The 9-session intervention program was completed by 68% of youth; 3% of youth in the 
intervention arm completed zero sessions. On average, youth completed a session 
every 2.5 weeks, requiring an average of 23 weeks for all 9 sessions. During 
intervention delivery, the percent of youth who required additional reminders to 
complete the sessions varied from 33% (for session 3) to 1% (for session 9). Additional 
reminders were conducted via telephone, text, and social media direct messages.  
 
Youth assigned to the attention-placebo arm also had access to a secure 
website―identical to the intervention website described above―but without the 9-
session intervention. As intervention-arm youth were invited to participate with the 
intervention sessions, control-arm youth were similarly invited to access the attention-
placebo website on the same delivery schedule as intervention-arm youth. All attention-
placebo arm youth visited the site at least once. Specifically, 12% of youth visited only 
one time, 77% of youth visited 2 to 5 times, 9% of youth visited 6-9 times, and 2% of 
youth visited their site 10 or more times. 
 
Statistical Analysis Plan 
 
Data were cleaned and analyzed using R (R Core Team, 2024). 1201 cases constitute 
the analytic sample after three cases were removed for failure to complete the pretest 
measures and twelve cases were removed due to implausible extreme response 
patterns. Our approach to evaluating pretest comparability across groups involves 
hypothesis testing of independence or equality of means across treatment arms (e.g., 
chi-square tests for categorical demographics and two-sample t-tests for numeric risk 
factors). For numeric variables we also calculate standardized mean difference (Cohen, 
1988) across groups, which gives the number of pooled standard deviations by which 
the group means differ. Values below 0.10 are considered acceptably balanced (Austin, 
2009; Resa & Zubizarreta, 2016), while values between 0.10 and 0.25 are considered 

imbalanced but may be corrected by statistical adjustment (What Works Clearinghouse, 
2022). 
 
Drug use outcomes were recorded as past 30-day frequency counts. Count variables 
are not suitable as outcomes in OLS regression analyses because count variables are 
typically skewed to the right and have heteroskedastic errors. Poisson regression, in 
which the natural log of the count outcome is regressed on a linear combination of the 
predictors, is a sensible starting point for count outcome data. However, the validity of 
Poisson regression relies on the assumption that the conditional mean and variance of 
the count outcome are identical. In practice, this assumption is often violated; when it is, 
we say the Poisson model is “over-dispersed.” Negative binomial regression is an 
extension of Poisson regression that accounts for over-dispersion by permitting the 
conditional mean and variance of the outcome to differ (Hilbe, 2014). 
 
After finding over-dispersion with Poisson regression models for drug use outcomes, we 
followed up by fitting negative binomial models in package MASS (Venables & Ripley, 
2002). The negative binomial models fit the data better, as evidenced by likelihood ratio 
tests and the Bayesian information criterion (Schwarz, 1978). The impacts of the 
intervention on past-month drug use at posttest, 1-, 2-, and 3-year follow-up were 
analyzed via negative binomial models, controlling for pretest score, age, average letter 
grade in school, and parents’ education level. 
 
In each model, we regress the outcome (past 30-day count) on treatment indicator (0 = 
control, 1 = intervention) and covariates, age (in years), average letter grade in school 
(1 = mostly A’s to 5 = mostly F’s), and parents’ education level (0 = less than 2 years of 
college, 1 = 2 or more years of college).  
 
The impacts of the intervention on risk factors for drug use at posttest, 1-, 2-, and 3-year 
follow-up were analyzed via ordinary least squares multiple regression, controlling for 
pretest score, age, average letter grade in school, and parents’ education level. 
 
References 
Austin, P. C. (2009). Balance diagnostics for comparing the distribution of baseline 
covariates between treatment groups in propensity-score matched samples. 
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Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). 
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Hilbe, J. (2014). Modeling count data. New York: Cambridge University Press. 
R Core Team (2024). R: A Language and Environment for Statistical Computing. R 
Foundation for Statistical Computing, Vienna, Austria. <https://www.R-
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Resa, M., & Zubizarreta, J. R. (2016). Evaluation of subset matching methods and 
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Schwarz, G. E. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 
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Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (4th ed). New 
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What Works Clearinghouse. (2022). What Works Clearinghouse procedures and 
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