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
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
Baseline values of outcome measures
Self-reported ARV adherence
HIV RNA viral load, log-10
CD4 count, cells/mm3
HIV appointment adherence
Depressive severity (HAM-D)
Depressive severity (PHQ-9)
Self efficacy with medications &
communication
Adaptive coping
Alcohol/substance use disorders
Completed 6month pill
count
Did not
complete 6month pill count
P value
Retained in
multivariable
model of
predictors
90.0 (1.4)
1.6 (9.1)
639.4 (32.8)
0.9 (0.4)
19.4 (2.5)
15.7 (6.3)
81.1 (2.6)
2.4 (6.1)
552.0 (37.3)
0.8 (5.3)
21.3 (2.7)
16.6 (7.4)
0.00
0.00
0.08
0.02
0.03
0.08
No
Yes
No
Yes
Yes
No
9.4 (5.1)
2.7 (9.2)
0.3 (0.6)
9.0 (5.1)
2.6 (3.1)
0.5 (0.7)
0.00
0.03
0.03
Yes
No
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.
1.
Carpenter JR, Kenward MG. Missing data in randomised controlled trials — a practical guide.
London: London School of Hygiene & Tropical Medicine; 2007.
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
Observations
145
132
91
94
98
92
77
71
81
69
0
20
40
60
80
100
(uncorrected for design or missing data).
0
3
Intervention (n=149)
6
Months
9
Usual care (n=155)
12
95% CI
Observations
145
132
98
79
77
67
0
20
40
60
80
100
Figure S2. Virologic suppression (viral load <50 c/mL), over time by study arm (uncorrected for design
or missing data).
0
3
Intervention (n=149)
6
Months
9
Usual care (n=155)
12
95% CI
Figure S3. Virologic suppression (viral load <50 c/mL), over time by study arm (uncorrected for design
Observations
152
148
151
146
0
20
40
60
80
100
or missing data).
0
3
Intervention (n=149)
6
Months
9
Usual care (n=155)
12
95% CI
Figure S4. Number of HIV symptoms in past 6 months, over time by study arm (uncorrected for design
151
92
98
77
81
142
95
92
71
69
2
4
6
8
Observations
0
HIV symptom score
10
12
or missing data).
0
3
Intervention (n=149)
6
Months
9
Usual care (n=155)
12
95% CI
Figure S5. Physical health-related functioning (SF-12), over time by study arm (uncorrected for design
Observations
153
145
98
92
81
67
0
20
40
60
80
100
or missing data).
0
3
Intervention (n=149)
6
Months
9
Usual care (n=155)
12
95% CI
Figure S6. Any emergency department visit in past 3 months, over time by study arm (uncorrected for
60
40
92
95
96
92
77
71
80
69
20
Observations
153
147
0
Any ED visit (%)
80
100
design or missing data).
0
3
Intervention (n=149)
6
Months
9
Usual care (n=155)
12
95% CI
Figure S7. Any hospitalization in past 3 months, over time by study arm (uncorrected for design or
Observations
153
147
92
96
96
92
77
71
80
69
0
20
40
60
80
100
missing data).
0
3
Intervention (n=149)
6
Months
9
Usual care (n=155)
12
95% CI
Figure S8. Mental health-related functioning (SF-12), over time by study arm (uncorrected for design
Observations
153
145
98
92
81
67
0
20
40
60
80
100
or missing data).
0
3
Intervention (n=149)
6
Months
9
Usual care (n=155)
12
95% CI