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Data analysis

Participants, all of whom had diagnoses of either an alcohol or drug use disorder, were first divided into three polysubstance use disorder categories based on

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Data analysis Participants, all of whom had diagnoses of either an alcohol or drug use disorder, were first divided into three polysubstance use disorder categories based on
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the number of types of substances they reported using in the past 30 days: one substance, two or three sub- stances, and four or five substances. This polysub- stance use disorder grouping was based on frequencies in the data and intuitive clinical meaning: 4,545 (55.1%) reported using one substance; 3,118 (37.8%) two or three substances; and 577 (7.0%) four or five substances. The choice of three groups allowed us to observe monotonic trends across the groups while preserving a degree of parsimony and respecting the frequencies in the data.

Next, bivariate analysis of variance and chi-square tests were used to compare these three groups on socio- demographic characteristics, comorbid psychiatric diag- noses, and indicators of functioning. Significance testing using a p value of less than .05 was used to iden- tify measures that differentiated the groups for inclu- sion in subsequent multivariate analyses.

Next, an analysis of covariance (ANCOVA) adjusted for significant variables from the prior analysis was applied to the baseline measures collected from the ASI including employment and medical composite indices in addition to the measure of violent behavior and PTSD symptomatology. If the overall type III sum of squares for the polysubstance use disorder classification term was significant at p < .005, the difference in least square means among polysubstance use disorder groups were compared using effect sizes as measured by Cohen’s d, the difference in means divided by the pooled standard deviation. In order to account for mul- tiple comparisons, we used a Bonferroni correction for the significance of the polysubstance use disorder classi- fication term. The standard p value of .05 was divided by 10 (the number of comparisons), which yields a sig- nificance p value threshold of .005. An effect size threshold of Cohens d greater than 0.20 or less than �0.20 was used to assess small effects (Cohen, 1988).

This analysis was then repeated with additional adjustment for the index measure of total reported days of substance use in the past 30 days, again with comparisons of least square means using Cohen’s d if the overall type III sum of squares analysis of the pol- ysubstance use disorder classification term was signifi- cant at p < .005.

Finally, follow-up data were used to reclassify the follow-up sample on the polysubstance use classifica- tion and to identify changes in polysubstance use after treatment. An additional set of ANCOVAs were used to compare outcomes 4 months after discharge from the program of the re-classified groups. The analysis was first conducted with adjustment only for baseline measures and then repeated with adjustment for the

38 I. P. BHALLA ET AL.

 

 

index of the total days of substance use assessed four months after discharge. As previously, a Bonferroni corrected p value < .005 was used for the overall test and followed by descriptive paired comparisons using Cohen’s d with an effect size difference of 0.20 used as the criterion for small effects. The study was approved by the Institutional Review Board committee of the VA Connecticut Healthcare System. A waiver of informed consent was obtained, as the study used administrative data and there were no patient identi- fiers included.

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