Table 4.
Exponentiated regression coefficients for models examining the association of cross-lagged primary substance use on weekly cannabis use.
| Variables | Unadjusted Model IRR (95% CI) | Final Adjusted Model IRR (95% CI) |
|---|---|---|
| Status at Week 2 of Treatment: | ||
| Intercept | 1.71 (0.85, 3.45) | 2.25 (1.42, 3.53) *** |
| Sex (female) | -- | 0.23 (0.15, 0.34) *** |
| Age | -- | 0.92 (0.89, 0.95) *** |
| Lagged Primary Substance Use | 0.83 (0.74, 0.93) ** | 0.86 (0.80, 0.93) *** |
| Lagged PTSD | 1.00 (1.00, 1.01) | NS |
| Rate of Change: | ||
| Time (linear) | 0.90 (0.81, 1.00) | 0.91 (0.86, 0.96) *** |
| Lagged Primary Substance use X Time (linear) | 1.00 (0.97, 1.02) | NS |
| Lagged PTSD X Time | 1.00 (1.00, 1.00) | NS |
Note: This model was specified with a negative binomial distribution. Negative binomial regression coefficients are expected differences in log counts. All coefficients presented here are exponentiated. When exponentiated, the intercept represents an expected count of days of cannabis use (when time = 0), and the slope represents a ratio of expected counts or an incident rate ratio. All predictors are also interpreted as ratios of expected counts or incident rate ratios (IRRs). For example, the IRR of 0.86 for lagged primary substance use is interpreted to mean that a one-unit change in lagged primary substance use was associated with a 14% lower expected count of cannabis use (when all other predictors were held constant). Sex was coded dichotomously with males equal to 0, while age was mean centered. ** p < 0.01; *** p < 0.001.