Table 3.

Results for the first regression, which focuses on the effects of the second-stage randomized treatment options for responders (A_2R), and non-responders (A_2NR); and estimated conditional effects of the second-stage randomized treatment options for responders

Parameter	Estimate	90% CI ii
Intercept	−0.06	−0.15	−0.03
Gender	−0.03	−0.08	0.02
O11: Baseline years of alcohol consumption	−0.003	−0.02	0.02
A1: Nonresponse criterion	0.01	−0.01	0.03
O21: Proportion of drinking days during stage 1	−1.07	−1.20	−0.96
A2R: Maintenance tactic for responders	0.003	−0.02	0.02
A2NR : Rescue tactic for non-responders	−0.02	−0.06	0.04
A2R × O21: Maintenance tactic for responders × Proportion of drinking days during stage 1	0.14	0.06	0.24
Estimated Conditional Effects of Maintenance Tactics	Estimate	90% CI ii
Percent drinking days during stage 1 = 0 (24% of responders had O21=0)	0.006	−0.04	0.04
Percent drinking days during stage 1 = 10% (43% of responders had 0< O21 ≤0.1)	0.04	−0.002	0.06
Percent drinking days during stage 1 = 20% (14% of responders had 0.1< O21 ≤0.2)	0.06	0.02	0.12
Percent drinking days during stage 1 = 30% (10% of responders had 0.2< O21 ≤0.3; and 9% had 0.3< O21)	0.10	0.04	0.16

ⁱⁱThe estimated regression coefficients and associated lower and upper limit of the 90% confidence intervals (CI) are summarized across ten imputed datasets. We set the Type I error rate to 0.10, rather than 0.05, given the illustrative nature of this analysis. Moreover, the aim of the analysis is to generate hypotheses about useful tailoring variables. Hence, from a clinical standpoint, it is sensible to tolerate a greater probability of detecting a false effect in order to improve the ability to detect true effects (see Collins et al. [50]; Dziak et al. [51]; McKay et al.,[52]).