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Aim To review randomized controlled trials to assess efficacy of a prize-based contingency management procedure in reducing substance use (where a drug-free breath or urine sample provides a chance of winning a prize). Methods A meta-analysis was conducted on papers published from January 2000 to February 2013 to determine the effect size of studies comparing prize-based contingency management to a treatment-as-usual control condition (k = 19 studies). Parallel analyses evaluated the efficacy of both short- (k = nine studies) and long-term outcomes (k = six studies) of prize-based contingency management. Results The average end-of-treatment effect size (Cohen’s d) was 0.46 [95% confidence interval (CI) = 0.37, 0.54]. This effect size decreased at the short-term (≤3-month) post-intervention follow-up to 0.33 (95% CI = 0.12, 0.54) and at the 6-month follow-up time-point there was no detectable effect [d = −0.09 (95% CI = −0.28, 0.10)]. Conclusion Adding prize-based contingency management to behavioral support for substance use disorders can increase short-term abstinence, but the effect does not appear to persist to 6 months. [ABSTRACT FROM AUTHOR]

Background and Aims It has been proposed that more use should be made of Bayes factors in hypothesis testing in addiction research. Bayes factors are the ratios of the likelihood of a specified hypothesis (e.g. an intervention effect within a given range) to another hypothesis (e.g. no effect). They are particularly important for differentiating lack of strong evidence for an effect and evidence for lack of an effect. This paper reviewed randomized trials reported in Addiction between January and June 2013 to assess how far Bayes factors might improve the interpretation of the data. Methods Seventy-five effect sizes and their standard errors were extracted from 12 trials. Seventy-three per cent ( n = 55) of these were non-significant (i.e. P > 0.05). For each non-significant finding a Bayes factor was calculated using a population effect derived from previous research. In sensitivity analyses, a further two Bayes factors were calculated assuming clinically meaningful and plausible ranges around this population effect. Results Twenty per cent ( n = 11) of the non-significant Bayes factors were < ⅓ and 3.6% ( n = 2) were > 3. The other 76.4% ( n = 42) of Bayes factors were between ⅓ and 3. Of these, 26 were in the direction of there being an effect (Bayes factor > 1 and < 3); 12 tended to favour the hypothesis of no effect (Bayes factor < 1 and > ⅓); and for four there was no evidence either way (Bayes factor = 1). In sensitivity analyses, 13.3% of Bayes Factors were < ⅓ ( n = 20), 62.7% ( n = 94) were between ⅓ and 3 and 24.0% ( n = 36) were > 3, showing good concordance with the main results. Conclusions Use of Bayes factors when analysing data from randomized trials of interventions in addiction research can provide important information that would lead to more precise conclusions than are obtained typically using currently prevailing methods. [ABSTRACT FROM AUTHOR]

Background and Aims: Recovery management checkups (RMC) have established efficacy for linking patients to substance use disorder (SUD) treatment. This study tested whether using RMC in combination with screening, brief intervention, and referral to treatment (SBIRT), versus SBIRT alone, can improve linkage of primary care patients referred to SUD treatment. Design: A randomized controlled trial of SBIRT as usual (n = 132) versus SBIRT plus recovery management checkups for primary care (RMC‐PC) (n = 134) with follow‐up assessments at 3 months post‐baseline. Setting: Four federally qualified health centers in the United States serving low‐income populations. Participants: Primary care patients (n = 266, 64% male, 80% Black, mean age, 48.3 [range, 19–53]) who were referred to SUD treatment after SBIRT. Interventions: SBIRT alone (control condition) compared with SBIRT + RMC‐PC (experimental condition). Measurement The primary outcome was any days of SUD treatment in the past 3 months. Key secondary outcomes were days of SUD treatment overall and by level of care, days of alcohol and other drug (AOD) abstinence, and days of using specific substances, all based on self‐report. Findings At 3‐month follow‐up, those assigned to SBIRT + RMC‐PC (n = 134) had higher odds of receiving any SUD treatment (46% vs 20%; adjusted odds ratio = 4.50 [2.49, 8.48]) compared with SBIRT only, including higher rates of entering residential and intensive outpatient treatment. They also reported more days of treatment (14.45, vs 7.13; d = +0.26), more days abstinent (41.3 vs 31.9; d = +0.22), and fewer days of using alcohol (27.14, vs 36.31; d = −0.25) and cannabis (19.49, vs 28.6; d = −0.20). Conclusions: Recovery management checkups in combination with screening, brief intervention, and referral to treatment are an effective strategy for improving linkage of primary care patients in need to substance use disorder treatment over 3 months. [ABSTRACT FROM AUTHOR]