Table VI.
Posterior probabilities* of the best fitting statistical model for estimating associations between adherence and viral load including adherence from each timeframe using a Bayesian model averaging approach among HIV-infected patients in clinical care (N=1099)
| Timeframe | Undetectable HIV viral load (binary) |
HIV viral load (continuous variable, log) |
||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| All | Patients reporting: | All | Patients reporting: | |||||||
| current substance use |
current marijuana use |
current depression |
current hazardous alcohol use |
current substance use |
current marijuana use |
current depression |
current hazardous alcohol use |
|||
| % in model |
% in model | % in model | % in model | % in model |
% in model |
% in model | % in model | % in model | % in model |
|
| 4-day | 0 | 5 | 6 | 9 | 24 | 0 | 5 | 4 | 5 | 22 |
| 7-day | 9 | 10 | 39 | 62 | 26 | 0 | 5 | 7 | 89 | 49 |
| 14-day | 59 | 90 | 45 | 20 | 27 | 100 | 100 | 100 | 16 | 47 |
| 30-day | 39 | 15 | 22 | 13 | 14 | 9 | 14 | 8 | 5 | 12 |
| 60-day | 6 | 7 | 7 | 10 | 10 | 5 | 23 | 17 | 5 | 50 |
*
Posterior probabilities are directly interpretable as the probability of a predictor being in the model given the data