Examining an Intervention to Reduce Underage DUI and Riding With Impaired Drivers
Phase: NA
Conditions: Underage Drinking, Driving Under the Influence, Drinking, Teen
Sponsor: Penn State University

Study Protocol and Statistical Analysis Plan 
Study Title: Examining an intervention to reduce underage DUI and riding with impaired drivers 
Clinical Trial registration: NCT03506880    
Funding: This study was funded by the National Institute on Alcohol Abuse and Alcoholism 
(NIAAA) of the National Institutes of Health (NIH) under award number R01AA025301 
Ethical Approval: Institutional Review Board at Penn State University, University Park, PA, US  
Document Date: June 8, 2023 
 
 

Background 
Drunk driving is a major public health problem. The National Highway Traffic Safety 
Administration reported nearly 10,000 people died from alcohol-related crashes in the U.S. in 
2014 (National Center for Statistics and Analysis, 2015). This translates into over 1 DUI related 
death per hour every day of the year. The problem is further magnified when one considers that 
each year over 1.3 million drivers in the U.S. are arrested for driving under the influence (DUI) 
(Department of Justice, 2013). This results in over 3.5 thousand DUI arrests per day. As 
alarming as these statistics are they pale by comparison to estimates indicating that they only 
represent 1 percent of the 121 million self-reported episodes of alcohol-impaired driving among 
U.S. drivers each year (Jewett et al., 2015). Drunk driving also does not only affect the driver. 
Almost 1 in 5 of all alcohol-related driving fatalities are passengers (NHTSA, 2011). Finally, 
youth under 21 are significantly over-represented in both alcohol-related driving and alcohol-
related passenger fatalities relative to the proportion of all licensed drivers (NHTSA, 2014; 
Shults et al., 2009; Chen et al., 2008). Together these statistics all underscore the need for 
immediate action to curb these alarming trends. 
 
There has been a wealth of research dedicated to identifying factors that influence DUI, riding 
with drinking drivers (RWDD), and under-age drinking to curb these alarming trends. Policies 
and regulatory actions that focus on maintaining the drinking age at 21, graduated licensing, 
sobriety check points, server training, and zero tolerance have all been shown to effectively 
reduce underage drinking and DUI (Hingson et al., 1994; Voas et al., 2000; Hingson et al., 
2004; Shults et al., 2001). Despite these efforts, youth continue to engage in DUI, RWDD, and 
abuse alcohol before they are 21. This indicates a great need to effect change on their 
motivations and risky behavior through efficacious prevention efforts.  
 
The research proposes to move the field forward by conducting a randomized controlled trial of 
MADD’s parent-based intervention’s (PBI) efficacy for all three categories of risky behavioral 
tendencies (underage drinking, DUI, and RWDD) on a nationally representative sample 
assessed at 3-waves (baseline, 6 mo., and 12 mo). To the extent that the research is 
successful, it will provide an easy to implement and low cost alternative that can be widely 
disseminated to address this important public health problem. 
 
Objectives 
Three objectives for the research: 1) Evaluate the efficacy of the PBI (short and long term); 2) 
examine the mediators of the PBI that directly influence drinking, DUI, and RWDD behaviors; 
and 3) identify the moderators of intervention efficacy (the subgroups for whom the intervention 
is more vs less effective).   
 
Study Design 
A randomized 3-arm intervention (PBI, Surgeon General, Active Control) with 3 waves of online 
survey data collection for parents (baseline, 6-month, 12-month), and 4 waves for teens 
(baseline, 1-month, 6-month, 12-month).  
 
Recruitment Strategy 
The participants were recruited by Ipsos using their proprietary KnowledgePanel. Parent-teen 
dyads who met inclusion criteria were invited to participate in the study via email. Up to 7 email 
reminders were sent to participants who did not complete the survey initially.  
 
Study Population 

The eligibility inclusion criteria included: Parent – must have a teenager aged 15-18 years old; 
Teen – must be 15-18 years old and have a parent/guardian agree to participate in the study. 
The exclusion criteria were: Parent – did not have a teenager aged 15-18; Teen – was not 15-
18 years old or did not have a parent/guardian agree to participate in the study.  
 
Participants were required to provide online consent to participate. Parent participants received 
a description of the research and Ipsos’ contact information for questions or to decline 
participation. Parents were presented a screen describing the research, their consent form, as 
well as a parental assent statement. Once parents provide consent, they were routed to the 
baseline survey; after parents provide assent for their teen, teens were also provided with an 
assent form, and then were routed to their baseline survey. 
 
Sample Size, Power, and Attrition 
Sample Size/Statistical Power: The sample sizes were chosen based on statistical analytic and 
theoretical considerations. For example, for Aim 1 examining efficacy of the PBI, we aimed to 
have final dyad sample Ns of 588 and 532 (and approximately equal n’s for our PBIs and control 
groups of 294 and 266) at the 6 and 12 month follow-ups, respectively. Using power estimation 
procedures described in Cohen (Cohen & Cohen, 1983) these sample sizes will yield power of 
greater than 0.80 for small effect sizes (e.g., 5-10%) in the MANCOVA-based analyses 
(omnibus tests, interactions, and planned comparisons). In our published papers with similar Ns 
comparing drinking for PBI and controls (Turrisi et al., 2013; Doumas et al., 2013; Ichiyama et 
al., 2009; Turrisi et al., 2009; Testa et al., 2010; Turrisi et al., 2001; Turrisi et al., 2004) we have 
detected effect sizes of .05 to .09 for main effects and .05 to .08 for interactions. Thus, with our 
final Ns we should have sufficient power to evaluate Aim 1. 
 
Attrition: We will first examine if the attrition is random by forming a dummy variable that 
indicates completion or drop-out at each assessment. The dummy variable will be correlated 
with key variables in the study (e.g., Group, drinking/DUI/RWDD outcomes). In the past, we 
have observed nonsignificant correlations suggesting that the attrition has been random and 
then used the most advanced multiple imputation techniques to impute missing data. We then 
analyze all cases with the imputed data (Turrisi et al., 2009). Although we have not observed 
differential attrition by condition in our previous work, we recognize the potential. We will reduce 
this possibility by providing the controls with an online link to the intervention at the completion 
of the 12-month assessment. Thus, all conditions will be identical except for the timing of 
intervention. However, if we should observe non-trivial correlations (e.g., > .2) between the 
dummy variable reflecting completers vs. drop-outs, we will include the dummy coded variable 
as a covariate in subsequent analyses and attempt to interpret significant effects. 
 
Randomization 
Participants meeting the inclusion criteria were randomly assigned to one of the three groups 
following eligibility screening.  
 
Intervention 
The study included two interventions (Turrisi’s PBI adapted for MADD; Surgeon General 
materials) and an active control (HPV materials). The PBI:  The first section provides an 
introduction to the problem of underage drinking, DUI, and RWDD. It provides parents with an 
overview of the problems, various basic facts, instructions on how to use the materials, and 
comments from parents about the consequences associated with underage drinking. It also 
helps motivate parents to talk with their teen by emphasizing that such discussions could make 
a difference in both improving their relationship and reducing their teen’s risk. The next two 
sections focus on developmental changes, relationship building, specific strategies that parents 

can use to improve communication channels with their teen, and parenting types. The final 
section is an in-depth discussion of the risks of underage drinking, DUI, RWDD, and all of the 
major variables in the proposed theoretical network. Parental reluctance to engage in 
discussions is also considered. Discussion is provided of whom is most at risk, making 
agreements, specific strategies for talking so that teens listen, decision making, peer influences, 
and positive and negative reasons why some teens drink. This section also addresses the 
issues of alternatives to drinking, DUI, and RWDD, issues for discussion, and additional 
resources. Parents who were randomized to this group, received a link following the baseline 
assessment to access the materials. The Surgeon General: These materials provided parents 
information on how to prevent and reduce underage drinking, with sections defining what a drink 
is, why underage drinking is a problem, the results of underage drinking, what families can do 
about underage alcohol use, etc. The Active Control (HPV materials) provided information 
regarding the HPV vaccination.  
 
Measures 
Data were collected online at baseline, 6-month, and 12-month for both parents and teens. 
Teens also had a 1-month follow-up. Ipsos provided demographic information on the parents 
based on panel demographics. Other measures included: DUI, riding with drinking drivers; 
alcohol use; intentions/willingness/attitudes/beliefs/norms to drink/DUI/RWDD; parental 
monitoring, permissiveness, modeling; intervention fidelity (Chen et al., 2008; Hultgren et al., 
2015; Kann et al., 2014; Poulin et al., 2007; Abar et al., 2014; Mallett et al., 2011; Cartwright & 
Asbridge, 2011; Dhami et al., 2011; Collins et al., 1985; Dimeff et al., 1999; Marlatt et al., 1998; 
Turrisi & Jaccard, 1992; Grube & Voas, 1996; Beck & Treiman, 1996; Abar et al., 2009).   
 
Primary Outcome Measure 
The primary outcome measures were typical weekend drinking, and declining to ride with 
impaired drivers.  
 
Secondary Outcome Measures  
The secondary outcome measure was willingness to ride in a car with an impaired driver.  
 
Intervention Variables 
Parents in the MADD and SG groups received a fidelity questionnaire to measure the useful, 
interesting, readable the sections of the intervention were.  
 
Statistical Analysis Plan 
Statistical analyses were performed using IBM SPSS Statistics version 28. Each primary and 
secondary outcome was examined using Tukey’s critical difference value to compare the means 
following Wilkinson et al., (1999) recommendations. This approach has been shown to have the 
most power and least Type I error rates with varying sample sizes to compare mean differences 
between conditions. 
 
Sample Characteristics 
Of those invited, 1,176 (28.5%) met the study eligibility criteria of being a parent or legal 
guardian to a 15-18 year old adolescent teen and consented for both themselves and their teen 
to participate in the study. There were 562 (13.6%) participants who responded to the study 
invitation but did not meet the study eligibility criteria. Eligible dyads were randomly assigned to 
one of three conditions: Active Control (AC; n=390, 33.1%), Surgeon General (SG; n=393, 
33.4%), or Mothers Against Drunk Drinking Parent-Based Intervention (MADD PBI; n=393, 
33.4%). 

At the T1 baseline survey, the average age of the teens was 16.33 (SD=1.09). About half of 
teens identified as female (49%) and most parents identified as female (54%). The majority of 
participants identified as White (78.2%), Black (8.7%), Asian (2.9%), Native Hawaiian or Pacific 
Islander (0.2%), American Indian or Alaska Native (1.2%), Multiracial (4.1%), Other (4%), and 
Hispanic (26.9%).   
 
 
 
 
Bibliography  
Abar, C., Abar, B., & Turrisi, R. (2009). The impact of parental modeling and permissibility on 
alcohol use and experienced negative drinking consequences in college. Addictive 
Behaviors, 34(6-7), 542-547. 
Abar, C. C., Turrisi, R., & Mallett, K. A. (2014). Differential trajectories of alcohol-related 
behaviors across the first year of college by parenting profiles. Psychology of Addictive 
Behaviors, 28(1), 53-61. 
Beck, K. H., & Treiman, K. A. (1996). The relationship of social context of drinking, perceived 
social norms, and parental influence to various drinking patterns of adolescents. 
Addictive Behaviors, 21(5), 633-644. 
Cartwright, J., & Asbridge, M. (2011). Passengers' decisions to ride with a driver under the 
influence of either alcohol or cannabis. Journal of Studies on Alcohol and Drugs, 72(1), 
86-95. 
Chen, M. J., Grube, J. W., Nygaard, P., & Miller, B. A. (2008). Identifying social mechanisms for 
the prevention of adolescent drinking and driving. Accident Analysis and Prevention, 
40(2), 576-585. 
Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the 
behavioral sciences. New Jersey: Lauren Erlbaum Publishers. 
Collins, R. L., Parks, G. A., & Marlatt, G. A. (1985). Social determinants of alcohol consumption: 
The effects of social interaction and model status on the self-administration of alcohol. 
Journal of Consulting and Clinical Psychology, 53(2), 189-200. 
Department of Justice (US), Federal Bureau of Investigation (FBI). Crime in the United States 
2012: Uniform Crime Reports. Washington (DC): FBI; 2013 [cited Nov 24 2015]. 
Available at URL https://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2012/crime-in-
theu.s.-2012/tables/29tabledatadecpdf. 
Dhami, M. K., Mandel, D. R., & Garcia-Retamero, R. (2011). Canadian and Spanish youths' risk 
perceptions of drinking and driving, and riding with a drunk driver. International Journal 
of Psychology, 46(2), 81-90. 
Dimeff, L. A. B., John, S., Kivlahan, D. R., & Marlatt, G. A. (1999). Brief Alcohol Screening and 
Intervention for College Students (BASICS): A harm reduction approach. New York, NY: 
Guilford Press 
Doumas, D. M., Turrisi, R., Ray, A. E., Esp, S. M., & Curtis-Schaeffer A. K. (2013). A 
randomized trial evaluating a parent-based intervention to reduce college drinking. 
Journal of Substance Abuse Treatment, 45(1), 31-37. 
Grube, J. W., & Voas, R. B. (1996). Predicting underage drinking and driving behaviors. 
Addiction, 91(12), 1843-1857. 
Hingson, R. W., Assailly, J. P., & Williams, A. F. (2004). Underage drinking: Frequency, 
consequences, and interventions. Traffic Injury Prevention, 5(3), 228-236. 
Hingson, R., Heeren, T., & Winter, M. (1994). Lower legal blood alcohol limits for young drivers. 
Public Health Reports, 109(6), 738-744. 

Hultgren, B., Scaglione, N. M., Cleveland, M. J., & Turrisi, R. (2015). Examination of a 
dualprocess model predicting riding with drinking drivers. Alcoholism: Clinical and 
Experimental Research, 39(6), 1075-1082. 
Ichiyama, M. A., Fairlie, A. M., Wood, M. D., Turrisi, R., Francis, D. P., Ray, A. E., & Stanger, L. 
A. (2009). A randomized trial of a parent-based intervention on drinking behavior among 
incoming college freshman. Journal of Studies on Alcohol and Drugs, Supp 16, 67-76. 
Jewett, A., Shults, R. A., Banerjee, T., & Bergen, G. (August 7, 2015) Alcohol-impaired driving 
among adults— United States, 2012. Morbidity and Mortality Weekly Report, 64(30), 
814-817. 
Kann, L., Kinchen, S., Shanklin, S. L., Flint, K. H., Hawkins, J., Harris, W. A., … Zaza, S. 
(2014). Youth risk behavior surveillance--United States, 2013. Morbidity and Mortality Weekly 
Report Surveillance Summaries, 63(Suppl 4), 1–168. 
Mallett, K. A., Turrisi, R., Ray, A. E., Stapleton, J., Abar, C., Mastroleo, N. R., Tollison, S., 
Grossbard, J., & Larimer, M.E. (2011). Do parents know best? Examining the 
relationship between parenting profiles, prevention efforts, and high-risk drinking in 
college students. Journal of Applied Social Psychology, 41(12), 2904-2927 
Marlatt, G. A., Baer, J. S., Kivlahan, D. R., Dimeff, L. A., Larimer, M. E., Quigley, L. A., Williams, 
E. (1998). Screening and brief intervention for high-risk college student drinkers: Results 
from a 2-year follow-up assessment. Journal of Consulting and Clinical Psychology, 
66(4), 604-615. 
National Center for Statistics and Analysis. (2015, November). 2014 Crash Data Key Findings 
(Traffic Safety Facts Crash•Stats. Report No. DOT HS 812 219). Washington, DC: 
National Highway Traffic Safety Administration. 
National Highway Traffic Safety Administration. (2011). Traffic safety facts 2010: A compilation 
of motor vehicle crash data from the fatality analysis reporting system and the general 
estimates system. Washington, DC. 
National Highway Traffic Safety Administration (NHTSA). Traffic Safety Facts 2013 Data: 
Alcohol-Impaired Driving. Washington (DC): NHTSA; 2014 [cited 2015 May 18]. 
Available at URL: http://www-nrd.nhtsa.dot.gov/Pubs/812102.p 
Poulin, C., Boudreau, B., & Asbridge, M. (2007). Adolescent passengers of drunk drivers: a 
multi-level exploration into the inequities of risk and safety. Addiction, 102(1), 51-51 
Shults, R. A., Elder, R. W., Sleet, D. A., Nichols, J. L., Alao, M. O., Carande-Kulis, V. G., ...& 
Task Force on Community Preventive Services. (2001). Reviews of evidence regarding 
interventions to reduce alcohol-impaired driving. American Journal of Preventive 
Medicine, 21(4S), 66-88. 
Shults, R. A., Kresnow, M.J., & Lee, K. C. (2009). Driver- and passenger-based estimates of 
alcohol-impaired driving in the U.S., 2001-2003. American Journal of Preventive 
Medicine, 36(6), 515-522. 
Testa, M., Hoffman, J. H., Livingston, J. A., & Turrisi, R. (2010). Preventing college women’s 
sexual victimization through parent based intervention: A randomized controlled trial. 
Prevention Science, 11(3), 308-318. 
Turrisi, R., Hillhouse, J., Heavin, S., Robinson, J., Adams, M., & Berry, J. (2004). Examination of 
the short-term efficacy of a parent-based intervention to prevent skin cancer. Journal of 
Behavioral Medicine, 27(4), 393-412. 
Turrisi, R., & Jaccard, J. (1992). Cognitive and attitudinal factors in the analysis of alternatives 
to drunk driving. Journal of Studies on Alcohol, 53(5), 405-414. 
Turrisi, R., Jaccard, J., Taki, R., Dunnam, H., & Grimes, J. (2001). Examination of the shortterm 
efficacy of a parent intervention to reduce college student drinking tendencies. 
Psychology of Addictive Behaviors: Special Issue on Understanding Binge Drinking, 
15(4), 366-372. 

Turrisi, R., Larimer, M. E., Mallett, K. A., Kilmer, J. R., Ray, A. E., Mastroleo, N. R., …Montoya, 
H. (2009). A randomized clinical trial evaluating a combined alcohol intervention for high-
risk college students. Journal of Studies on Alcohol and Drugs, 70(4), 555-567. 
Turrisi, R., Mallett, K. A., Cleveland, M. J., Varvil-Weld, L., Abar, C., Scaglione, N. & Hultgren, 
B. (2013). Evaluation of timing and dosage of a parent based intervention to minimize 
college students’ alcohol consumption. Journal of Studies on Alcohol and Drugs, 74(1), 
30-40. 
Voas, R. B., Tippetts, A. S., & Fell, J. (2000). The relationship of alcohol safety laws to drinking 
drivers in fatal crashes. Accident Analysis & Prevention, 32(4), 483-492.