absence of the primary outcome measure as the dependent variable

(1)

The Effect of Antidepressant Treatment on HIV and Depression Outcomes: Results from the SLAM DUNC Randomized Controlled Trial

Supplemental Materials: Details of Analytic Approach to Missing Data

Following the study’s Statistical Analysis Plan, approved by the Data Safety and Monitoring Board prior to the examination of any unblinded data, missing data for the primary outcome and other continuous secondary outcome measures were addressed using the direct modeling approach presented by Carpenter and Kenward.¹ This approach is appropriate for a continuous outcome measure when

observations are missing at random conditional on measured covariates (i.e., all important determinants of missingness have been measured) and those determinants are roughly normally distributed.

Determinants of missingness and measures of the outcome at different time points (i.e., months 1-5) are modeled as additional outcome variables along with the primary outcome using a multivariate normal distribution. Under the assumption that the missingness mechanism is correctly specified, this modeling approach yields a marginal rather than conditional effect estimate (as desired from a randomized trial) that is corrected for any selection bias induced by the missing observations. This approach was

implemented using PROC MIXED in SAS version 9.3 (Cary, NC), specifying random intercepts for providers and for patients within providers and fixed effects for design characteristics (site and provider depression treatment experience level).

The determinants of missing primary outcome observations were identified once all data collection was complete. A set of demographic, physical and mental health, and psychosocial characteristics, including baseline values of outcome variables, was evaluated as possible determinants of missingness. In order to identify the minimum set of variables necessary to capture the missing data mechanism, all

characteristics associated with missingness of the primary outcome at a P value < 0.10 were entered into a single multivariable logistic regression model with presence vs. absence of the primary outcome measure as the dependent variable. Predictors were removed if a likelihood ratio test supported their removal and their removal improved or did not change the area under the receiver-operator

characteristic (ROC) curve for the model.

Baseline factors associated with presence of a valid 6-month ARV adherence measure were higher mean self-reported ARV adherence, lower log10 HIV RNA viral load, higher CD4 count, higher kept HIV visit proportion in the year preceding study enrollment, lower depressive severity, higher self-efficacy with medications and provider communication, higher adaptive coping styles, and fewer alcohol and

(2)

substance abuse and dependence diagnoses (Table, below). Importantly, retention did not differ by study arm (p=0.53) or site (p=0.89). Predictors of retention did not differ between study arms. The optimal subset of predictors of retention for modeling purposes was identified as kept visit proportion, log10 HIV RNA viral load, depressive severity, and self-efficacy.

Table. Predictors of retention

Completed 6- month pill

count

Did not complete 6-

month pill count P value

Retained in multivariable

model of predictors Baseline values of outcome measures

Self-reported ARV adherence 90.0 (1.4) 81.1 (2.6) 0.00 No

HIV RNA viral load, log-10 1.6 (9.1) 2.4 (6.1) 0.00 Yes

CD4 count, cells/mm³ 639.4 (32.8) 552.0 (37.3) 0.08 No

HIV appointment adherence 0.9 (0.4) 0.8 (5.3) 0.02 Yes

Depressive severity (HAM-D) 19.4 (2.5) 21.3 (2.7) 0.03 Yes

Depressive severity (PHQ-9) 15.7 (6.3) 16.6 (7.4) 0.08 No

Self efficacy with medications &

communication 9.4 (5.1) 9.0 (5.1) 0.00 Yes

Adaptive coping 2.7 (9.2) 2.6 (3.1) 0.03 No

Alcohol/substance use disorders 0.3 (0.6) 0.5 (0.7) 0.03 No

For secondary binary outcomes, since the linear mixed model approach was not appropriate, correction for missing data was implemented using inverse probability of observation weighting. A logistic

regression model was fit with an indicator variable indicating observed vs. missing outcome data as the dependent variable and predictors of missingness as independent variables. The optimal set of

predictors of missingness was identified as described above. The predicted probability of having an observed outcome conditional on covariates was calculated for each participant. An individual’s stabilized weight was then calculated as the marginal probability of being observed divided by the conditional predicted probability. Risk differences corrected for missing data were estimated from generalized linear models controlling for design characteristics (fixed effects for site and provider depression treatment experience level and clustering by provider) and weighted by the stabilized inverse probability of observation weight.

(3)

1. Carpenter JR, Kenward MG. Missing data in randomised controlled trials — a practical guide.

London: London School of Hygiene & Tropical Medicine; 2007.

(4)

The Effect of Antidepressant Treatment on HIV and Depression Outcomes: Results from the SLAM DUNC Randomized Controlled Trial

Supplemental Figures

Figure S1. Self-reported ARV adherence (past 30 days, visual analog scale), over time by study arm (uncorrected for design or missing data).

Figure S2. Virologic suppression (viral load <50 c/mL), over time by study arm (uncorrected for design or missing data).

Observations

145132 91

94 98

92 77

71 81

69

020406080100Self-reported ARV adherence

0 3 6 9 12

Months

Intervention (n=149) Usual care (n=155) 95% CI

(5)

Figure S3. Virologic suppression (viral load <50 c/mL), over time by study arm (uncorrected for design or missing data).

Figure S4. Number of HIV symptoms in past 6 months, over time by study arm (uncorrected for design or missing data).

Observations

145132 98

79 77

67

020406080100Viral load < 50 copies (%)

0 3 6 9 12

Months

Observations

152148 151

146

020406080100Appointment adherence

0 3 6 9 12

Months

(6)

Observations 151 142

92 95

98 92

77 71

81 69

024681012HIV symptom score

0 3 6 9 12

Months

(7)

Figure S5. Physical health-related functioning (SF-12), over time by study arm (uncorrected for design or missing data).

Figure S6. Any emergency department visit in past 3 months, over time by study arm (uncorrected for design or missing data).

Observations

153145 98

92 81

67

020406080100SF-12 physical composite

0 3 6 9 12

Months

Observations

153147 92

95 96

92 77

71 80

69

020406080100Any ED visit (%)

0 3 6 9 12

Months

(8)

Figure S7. Any hospitalization in past 3 months, over time by study arm (uncorrected for design or missing data).

Figure S8. Mental health-related functioning (SF-12), over time by study arm (uncorrected for design or missing data).

Observations

153147 92

96 96

92 77

71 80

69

020406080100Any hospitalization (%)

0 3 6 9 12

Months

Observations

153145 98

92 81

67

020406080100SF-12 mental composite

0 3 6 9 12

Months