Table 2.

Summary of Recommendations for Design and Analysis of Alcohol Treatment Studies.

Recommendation	Selected Relevant Citations
1. Careful consideration and reporting of exclusion criteria	Altman et al., 2001; Humphreys et al., 2008
2. Selection of a randomization procedure that reduces imbalances across treatment groups	Hedden et al., 2006; Matts and Lachin, 1988
3. Consider use of propensity score or instrumental variable methods when group imbalances exist	Berger, 2005b; Leyrat et al., 2013
4. Use of well-validated self-report measures and either biomarkers or carefully selected collateral informants to validate self-reported consumption	Litten et al., 2010; Miller, 1996; Sobell and Sobell, 1992; Sobell et al., 1997
5. Careful consideration of the timing of assessments to inform the durability of treatment effects and variation in outcomes over time	Collins and Graham, 2002; Stasiewicz et al., 2013
6. Investigation of data distributions and the use of either data transformation or alternative analytic techniques when data are non-normal	Atkins et al., 2013; Gueorguieva et al., 2012; Witkiewitz et al., 2010
7. An intention-to-treat (ITT) analytic approach that takes into account clustering of patients within sites, therapists, and/or groups and that controls for covariate adjustment in the randomization procedure	Del Re et al., 2013; Kahan and Morris, 2013; Moerbeek et al., 2003
8. An alternative approach to ITT, such as the complier average causal effect model, if there is unequal compliance across groups	Tucker et al., 2012; Ye et al., 2014
9. Minimizing missing primary outcome data by continuing to assess all individuals (including those who drop out of treatment) and maximum likelihood estimation or multiple imputation to accommodate missing data	Enders, 2010; Hallgren and Witkiewitz, 2014; Witkiewitz et al., 2014
10. Sensitivity analyses to evaluate the impact of missing data on the treatment effect estimates	Enders, 2011; Jackson et al., 2014