Table 2.

Longitudinal growth models of marijuana use outcomes among patients with depression with and without AUD.

Variable	Marijuana Use
	B	95%CI	SE	p
Unconditional Growth Model
Time	−1.20	−1.924, −0.492	0.36	<0.001
Conditional Growth Modela
Ageb
30–39	−0.90	−1.286, −0.217	0.26	<0.001
40–49	−0.92	1.389, −0.158	0.30	<0.001
50+	−1.30	−1.773, −0.676	0.27	<0.001
Female	−0.38	−0.782, 0.103	0.22	0.091
Employed	−0.16	−0.647, 0.212	0.21	0.444
White	−0.33	−0.823, 0.162	0.25	0.193
Married	−0.35	−0.784, 0.070	0.22	0.105
MI Treatment	−0.10	−0.469, 0.333	0.20	0.609
AUD	−0.64	−1.080, −0.276	0.24	<0.001
Time	−0.67	−1.139, −0.413	0.18	<0.001
Time × Ageb
Time × 30–39	0.11	−0.205, 0.437	0.16	0.484
Time × 30–39	0.12	−0.245, 0.491	0.19	0.517
Time × 50+	0.39	0.070, 0.728	0.16	0.018
Time × Female	0.14	−0.123, 0.414	0.13	0.294
Time × Employed	0.27	0.017, 0.539	0.13	0.038
Time × White	0.21	−0.087, 0.513	0.15	0.168
Time × Married	0.25	−0.002, 0.510	0.13	0.055
Time × MI Treatment	0.03	−0.209, 0.276	0.13	0.789
Psychiatry Visitsc	−0.01	−0.057, 0.018	0.01	0.329
Depression Symptomsd	0.04	0.015, 0.060	0.01	<0.001
Time × AUD	0.25	0.012, 0.497	0.12	0.042

Note. N = 307. B = beta coefficient; SE = standard error; 95% CI = confidence intervals; p = p- values < .05 are presented in boldface; MI Treatment = Motivation interviewing treatment condition; AUD = alcohol use disorder.

^aConditional growth models were fit using penalized quasi likelihood estimation.

^breference = ages 18 – 29.

^cPsychiatry visits = Time-varying covariate estimating the average number of psychiatry visits prior to each interview.

^dDepression symptoms = Time-varying covariate estimating the average depression severity prior to each interview.