1. A secondary analysis of a study of 152 subjects aged 15-21 seeking treatment for opioid dependence were randomized to 2 week detoxification with buprenorphine/naloxone (DETOX) or 12 weeks buprenorphine/naloxone (BUP) (Woody et al., 2008). Higher compensation amounts were associated with improved retention rates, especially in the DETOX group. These findings, and others, suggest that the amount of financial compensation given for completing assessments can minimize bias when treatment condition is associated with differential dropout rates. Effects of Participant Compensation Amounts on Missing Data and Urine Screen Results Among Adolescent and Young Adult Opioid Dependent Clinical Trial Participants C. E. Wilcox 1, M. P. Bogenschutz 1,2, M. Nakazawa 2, G. E. Woody 3 1 University of New Mexico Department of Psychiatry, Albuquerque, NM 2 University of New Mexico Center on Alcoholism, Substance Abuse and Addictions (CASAA) 3 University of Pennsylvania Department of Psychiatry, Philadelphia, PA INTRODUCTION METHODS RESULTS CONCLUSIONS ACKNOWLEDGEMENTS 152 subjects aged 15-21 seeking treatment for opioid dependence were randomized to 2 week detoxification with buprenorphine/naloxone (DETOX) or 12 weeks buprenorphine/naloxone (BUP) with a dose taper beginning in week 9 and ending in week 12, each with weekly individual and group drug counseling. Urine drug screens and self reported drug use were obtained weekly. Patients were paid $5 for completing the weekly assessments except for weeks 4, 8, and 12, where more extensive assessments were done and participants were reimbursed $75. GLMM was chosen as the statistical model for this secondary analysis of incentive effects, as it is effective for analysis of longitudinal dichotomous variables. Separate models were constructed for predicting missing, positive, and negative urine drug screen results. Compensation (High versus Low), Treatment (BUP versus DETOX) and Time (Time 1 = Weeks 3,4,5; Time 2 = Weeks 7,8,9; Time 3 = Weeks 11,12) were chosen as predictor variables. This research was supported by NIDA’s Clinical Trials Network REFERENCES NA’NIZHOOZHI CENTER A BRIDGE TO RECOVERY Festinger, D.S., Marlowe, D.B., Dugosh, K.L., Croft, J.R., Arabia, P.L., 2008. Higher magnitude cash payments improve research follow-up rates without increasing drug use or perceived coercion. Drug Alcohol Depend 96, 128-135. Lavori, P.W., Bloch, D.A., Bridge, P.T., Leiderman, D.B., LoCastro, J.S., Somoza, E., 1999. Plans, designs, and analyses for clinical trials of anti-cocaine medications: where we are today. NIDA/VA/SU Working Group on Design and Analysis. J Clin Psychopharmacol 19, 246-256. Lott, D.C., Jencius, S., 2009. Effectiveness of very low-cost contingency management in a community adolescent treatment program. Drug Alcohol Depend 102, 162-165. Meyers, K., Webb, A., Frantz, J., Randall, M., 2003. What does it take to retain substance-abusing adolescents in research protocols? Delineation of effort required, strategies undertaken, costs incurred, and 6-month post-treatment differences by retention difficulty. Drug Alcohol Depend 69, 73-85. Scott, C.K., 2004. A replicable model for achieving over 90% follow-up rates in longitudinal studies of substance abusers. Drug Alcohol Depend 74, 21-36. Stinchfield, R.D., Niforopulos, L., Feder, S.H., 1994. Follow-up contact bias in adolescent substance abuse treatment outcome research. J Stud Alcohol 55, 285-289. Winters, K.C., Stinchfield, R.D., Opland, E., Weller, C., Latimer, W.W., 2000. The effectiveness of the Minnesota Model approach in the treatment of adolescent drug abusers. Addiction 95, 601-612. Woody, G.E., Poole, S.A., Subramaniam, G., Dugosh, K., Bogenschutz, M., Abbott, P., Patkar, A., Publicker, M., McCain, K., Potter, J.S., Forman, R., Vetter, V., McNicholas, L., Blaine, J., Lynch, K.G., Fudala, P., 2008. Extended vs short-term buprenorphine- naloxone for treatment of opioid-addicted youth: a randomized trial. JAMA 300, 2003- 2011. Higher financial compensation amounts increase retention for follow-up assessments. Patients in control groups and actively using patients may be especially vulnerable to dropout if adequate financial compensation is not provided. Attrition increases over time, especially in the case of low financial compensation amounts. Outcomes data may be affected differently over time by financial compensation amount, depending on treatment group assignment, introducing the potential for further bias. Non-active users (those with negative urine screens) may be less influenced towards dropout overall by the length of time in the study than active users (those with positive urine screens). BUP assignment independently increased the rate of negative urine screen results, supporting the findings of the primary study showing that BUP assignment was associated with better outcomes. The especially strong effect of compensation amount on follow-up rates in active users could be understood to reflect coercion, and the possibility that high compensation biases analyses by preferentially retaining subjects who are more ‘down and out’ (Festinger 2008). However, given the markedly low retention rates during low financial compensation weeks, it is more likely that the increased retention rates favored by higher financial compensation minimizes bias by decreasing attrition to acceptable levels. Higher participant compensation amounts are essential for improving retention in research studies of substance abusing youth and likely minimize bias in outcomes analyses, especially when there are differences in retention as a function of treatment group assignment. ABSTRACT Attrition in substance abuse research is a major problem, with rates of data collection often falling well below 70% (Lavori et al., 1999). More than 30% dropout can result in considerable bias when analyzing outcomes data (Scott, 2004). Financial compensation can increase retention in research studies, raising collection rates of outcomes data to acceptable levels (Festinger et al., 2008). Follow-up rates in adolescent studies have been similarly problematic (Stinchfield et al., 1994; Winters et al., 2000). Studies in youth have shown that intensive follow up protocols improve research adherence (Meyers et al., 2003), and that financial compensation improves treatment adherence and outcomes (Lott and Jencius, 2009). However, to our knowledge, there has been no research on the effects of financial compensation amounts on adherence to research assessments in substance abusing adolescents and young adults. DISCUSSION FIGURE 1 TABLE 1: Summary of GLMM Analysis for Predicting Non-Missing Values (N=152) TABLE 3: Summary of GLMM Analysis for Predicting Negative Opioid Urine Screen with Missing Values Assigned as Non-Negative (N=152) TABLE 2: Summary of GLMM Analysis for Predicting Positive Opioid Urine Screen with Missing Values Assigned as Non-Positive (N=152) High compensation amount was associated with lower rates of missing data. DETOX assignment increased overall likelihood of missing data. The effect of compensation amount on missing data was greater in the DETOX group. Probability of positive urine screens was significantly higher during the high compensation weeks. The effect of compensation on the probability of positive urine screens was greater in DETOX subjects in comparison to BUP subjects. The rate of urine screens being positive significantly decreased over time. Probability of negative urine screens was significantly higher during the high compensation weeks. BUP assignment increased the rate of urine screens being negative. The effect of compensation amount on the probability of negative urine screens did not differ between DETOX and BUP subjects. There was no change in the rate of urine screens being negative over time. Attrition increased over time, with highest attrition at week 11. Dropout during the first two weeks was pronounced, especially among DETOX subjects. Notes: Results from Model 4 applied to the Missing vs. Non-Missing Variable; *p<0.05, **p<0.01. Non-Missing values are defined as positive or negative urine screen values. Notes: Results from Model 4 applied to the Positive vs. Non-Positive Variable; + p<0.10, **p<0.01. Non-Positive values are defined as missing or negative urine screen results. Notes: Results from Model 4 applied to the Negative vs. Non-Negative Variable; *p<0.05, **p<0.01. Non-Negative values are defined as missing or positive urine screen results.