TITLE: Allowing for non-adherence to treatment in a randomized controlled trial of
two antidepressants (citalopram 

  ABSTRACT.BACKGROUND:
Meta-analyses suggest that reboxetine may be less effective than other antidepressants.
Such comparisons may be biased by lower adherence to reboxetine and subsequent handling
of missing outcome data. This study illustrates how to adjust for differential
non-adherence and hence derive an unbiased estimate of the efficacy of reboxetine
compared with citalopram in primary care patients with depression.

ABSTRACT.METHOD:
A structural mean modelling (SMM) approach was used to generate adherence-adjusted
estimates of the efficacy of reboxetine compared with citalopram using GENetic and
clinical Predictors Of treatment response in Depression (GENPOD) trial data.
Intention-to-treat (ITT) analyses were performed to compare estimates of effectiveness
with results from previous meta-analyses.

ABSTRACT.RESULTS:
At 6 weeks, 92% of those randomized to citalopram were still taking their medication,
compared with 72% of those randomized to reboxetine. In ITT analysis, there was only
weak evidence that those on reboxetine had a slightly worse outcome than those on
citalopram [adjusted difference in mean Beck Depression Inventory (BDI) scores: 1.19,
95% confidence interval (CI) –0.52 to 2.90, p = 0.17]. There was no
evidence of a difference in efficacy when differential non-adherence was accounted for
using the SMM approach for mean BDI (–0.29, 95% CI –3.04 to 2.46,
p = 0.84) or the other mental health outcomes.

ABSTRACT.CONCLUSIONS:
There was no evidence of a difference in the efficacy of reboxetine and citalopram when
these drugs are taken and tolerated by depressed patients. The SMM approach can be
implemented in standard statistical software to adjust for differential non-adherence
and generate unbiased estimates of treatment efficacy for comparisons of two (or more)
active interventions.

BODY.INTRODUCTION:
Antidepressants are often prescribed in primary care as the first-line treatment for
depression. In England in 2011, 46 million prescriptions for antidepressants were issued at
a cost of £270 million (HSCIC, 2012). Selective
serotonin reuptake inhibitors (SSRIs) are the most commonly prescribed (54% of prescriptions
in 2011), with tricyclic antidepressants (TCAs) accounting for a further 29% of
prescriptions issued (HSCIC, 2012).  Data on the comparative effectiveness of the various antidepressants suggest that there is
little difference between different antidepressants (Freemantle et al.
2000; Cipriani et al.
2009). Two meta-analyses suggest that reboxetine may
be less effective (Cipriani et al.
2009; Eyding et al.
2010) but others have reported no such differences
(Papakostas et al.
2008).  Reboxetine is a selective noradrenaline reuptake inhibitor (NaRI), and is the only drug of
this class of antidepressants currently licensed in the UK. It is prescribed infrequently
(0.1% of total prescriptions for antidepressants in 2011) (HSCIC, 2012). Notably, meta-analyses have highlighted a lower adherence to
treatment with reboxetine compared with other antidepressants (Cipriani et al.
2009; Eyding et al.
2010). This differential non-adherence poses
problems when examining the results of randomized controlled trials (RCTs) comparing two
active treatments because commonly used methods to handle missing data may lead to biased
estimates of effectiveness. In the meta-analysis by Cipriani et al. (2009), it was assumed that those patients who were
missing outcome data had not responded to treatment. However, as reboxetine was less well
tolerated than SSRIs, this imputation has the potential to introduce bias such that the
outcome for those on reboxetine may seem less favourable. Similarly, meta-analysis of trials
that have used a last observation carried forward (LOCF) approach to handling missing
outcome data (Eyding et al.
2010) may be biased in a similar direction. However,
neither study explored the potential for bias based on their approach to dealing with
missing data.  Importantly, these meta-analyses have focused on treatment effectiveness, that is the
average outcome of the 'offer' of treatment obtained from intention-to-treat (ITT) analyses,
irrespective of adherence to the allocated treatment. However, once it has been established
that a medication can be tolerated by a patient, clinicians are often interested in knowing
the benefit conferred by that drug when taken as prescribed. There is therefore clinical
utility in estimating the efficacy of the drug under 'ideal conditions' (Last, 1995), which includes full adherence to treatment.
Estimates of treatment efficacy from 'per-protocol' analyses may be biased (Fleming, 2008), and are further complicated in trials of two (or
more) active interventions when there is differential adherence to the allocated treatments.
A structural mean modelling (SMM) approach to deal with the issue of non-adherence in trials
of two active treatments has been proposed by Fischer et al. (2011).  The current study had two aims. First, to test whether two commonly used approaches to
dealing with missing data introduce bias in estimates of effectiveness derived in the
presence of differential non-adherence between treatment arms. Second, to use data from the
GENetic and clinical Predictors Of treatment response in Depression (GENPOD) trial (Lewis
et al.
2011; Wiles et al.
2012) to illustrate how to adjust for differential
non-adherence in an RCT of two active interventions and hence to derive an unbiased estimate
of the efficacy of reboxetine compared with citalopram in the treatment of primary care
patients with a new episode of depression.

BODY.METHOD.THE GENPOD TRIAL:
The GENPOD trial (Thomas et al.
2008) was designed to test two primary hypotheses
regarding (1) genetic and (2) clinical predictors of response to antidepressant
medication. There was no evidence that the genetic serotonin polymorphism 5-HTTLPR (Lewis
et al.
2011) or severity of depression (Wiles et
al.
2012) was associated with response to
antidepressant medication. Secondary analysis of these trial data can provide information
on the comparative efficacy of an SSRI (citalopram) and an NaRI (reboxetine).

BODY.METHOD.PARTICIPANTS:
Following agreement that an antidepressant should be prescribed, general practitioners
(GPs) referred patients to the research team. Those eligible were aged 18–74 years, had a
Beck Depression Inventory (BDI; Beck et al.
1996) score of ⩾15 and met ICD-10 criteria for a
depressive episode (F32) using the computerized Clinical Interview Schedule – Revised
(CIS-R; Lewis et al.
1992; Lewis, 1994). Those who gave written informed consent were randomized to receive either
the SSRI citalopram (20 mg daily) or the NaRI reboxetine (4 mg twice daily).  Patients with psychosis, bipolar disorder or major substance or alcohol abuse problems
were excluded, as were those who had taken antidepressants in the 2 weeks prior to
baseline or who could not complete self-administered questionnaires.

BODY.METHOD.BASELINE MEASURES:
In addition to age, gender, BDI score and CIS-R score, the following data were recorded
at baseline: ethnicity, marital status, employment status, financial strain [based on
questions from the Breadline Britain survey (Gordon et al.
2000) and a single question asking about how they
were managing financially (five response options)], details of home ownership (home owner,
tenant, other), whether they had any longstanding illness, disability or infirmity, total
number of physical symptoms (based on a list of 28 symptoms), history of depression
(self/family) and prior treatment for depression, personality – conscientiousness [Big
Five Inventory (BFI); John et al.
1991], Hospital Anxiety and Depression Scale
(HADS; Zigmond & Snaith, 1983) score,
life events, social support, alcohol use (Alcohol Use Disorders Identification Test for
Primary Care, AUDIT-PC; Piccinelli et al.
1997), and scores on the 12-item Short Form
Health Survey (SF-12) mental and physical subscales (Jenkinson & Layte, 1997).

BODY.METHOD.RANDOMIZATION PROCEDURE:
Randomization was conducted by means of a computer-generated code, administered centrally
and communicated by telephone and hence concealed from the recruiting researcher.
Allocation was stratified by severity of overall symptoms (CIS-R score < 28 or ⩾28)
and centre. The researcher gave the allocated medication to the participant. Neither
patients nor researchers were blind to treatment allocation.

BODY.METHOD.ALLOCATED TREATMENTS:
Patients randomized to citalopram were prescribed 20 mg daily. Citalopram taken at this
dose has been shown to occupy about 80% of serotonin transporter reuptake sites, which is
reported to be the level of occupancy needed to produce reliable antidepressant effects
(Meyer et al.
2001).  Those randomized to reboxetine were advised to start on 2 mg twice daily and increase to
4 mg twice daily after 4 days. This stepped approach to starting reboxetine treatment was
used on the advice of psychopharmacologists to minimize problems with lack of tolerance of
this drug. Acute doses of 4 mg of reboxetine increase cortisol levels indicative of
increased noradrenergic function (Hill et al.
2003) and this dose of drug also produces
peripheral autonomic effects consistent with noradrenaline reuptake blockade (Szabadi
et al.
1998). GPs could increase the dose of either
allocated treatment if deemed clinically appropriate.

BODY.METHOD.MEASURES OF TREATMENT ADHERENCE:
Participants were asked about their use of antidepressant medication in the follow-up
questionnaires (six closed response options: I have not taken any of my tablets; I have
taken hardly any of my tablets; I have taken less than half of my tablets; I have taken
more than half of my tablets; I have taken nearly all my tablets; I have taken my tablets
every day).

BODY.METHOD.OUTCOME MEASURES:
Self-reported outcome data were collected 6 and 12 weeks after randomization. For the
purpose of this study, which demonstrates the approach to adjusting for differential
non-adherence between the two treatments, we used the 6-week outcome data. The (original)
primary outcome was the total BDI score at 6 weeks. Secondary outcomes were the HADS total
and subscale scores and the SF-12 mental and physical subscale scores.

BODY.METHOD.DATASET:
The 6-week follow-up was completed by 91% of participants (n = 546)
[citalopram: 274/298 (92%) and reboxetine: 272/303 (90%)]. Younger individuals, those with
more life events and less social support were more likely to have missing data (Lewis
et al.
2011). Adjustment for these variables made no
difference to the main trial findings (Lewis et al.
2011) and there was no evidence that these
factors were associated with adherence to medication (data not shown). Therefore, for the
present analyses, the dataset comprised the 546 participants with 6-week follow-up data
(complete cases).

BODY.METHOD.STATISTICAL ANALYSIS:
All analyses were conducted in Stata version 11.1 (Stata Corporation, USA). To compare
the data from the GENPOD trial with the previous literature on the comparative
effectiveness of antidepressants (Papakostas et al.
2008; Cipriani et al.
2009; Eyding et al.
2010), we first conducted analyses on the
effectiveness of reboxetine versus citalopram according to the ITT
principle. We then examined the effect of two approaches to handling missing data that
have been used in the previous meta-analyses to illustrate the potential for bias in such
estimates of effectiveness in the presence of differential non-adherence. Finally, we
focused on the application of the novel SMM approach to estimating treatment efficacy in
the presence of differential non-adherence.

BODY.METHOD.ESTIMATES OF EFFECTIVENESS:
The primary comparative ITT analysis compared the BDI score at 6 weeks between the two
groups as randomized, with adjustment for baseline BDI score and the stratification
variables. To estimate treatment effectiveness, data from all participants followed up at
6 weeks were included in these analyses, irrespective of adherence to the allocated
medication.

BODY.METHOD.EFFECT OF IMPUTING MISSING OUTCOMES AS ‘NON-RECOVERY’ OR USING AN LOCF APPROACH TO
HANDLING MISSING OUTCOME DATA ON ESTIMATES OF EFFECTIVENESS:
Previous studies comparing outcomes for those taking citalopram and reboxetine (Cipriani
et al.
2009; Eyding et al.
2010) analysed data on an ITT basis but either:
(1) assumed that those who were missing outcome data (which frequently equates to all
those who had stopped the trial medication in psychopharmacology trials) had not responded
to treatment (Cipriani et al.
2009) or (2) summarized data from publications
that used an LOCF approach to handle missing data (Eyding et al.
2010). The effect of these two different
approaches to handling missing data was examined by artificially constraining the GENPOD
dataset such that only those who had continued to take their medication at 6 weeks were
regarded as having outcome data.

BODY.METHOD.ADHERENCE-ADJUSTED EFFICACY ESTIMATES:
The final set of analyses generated unbiased estimates of treatment efficacy in the
presence of differential non-adherence between treatment arms. The SMM method assumes that
the mean outcomes in the two arms would be equal in the absence of treatment, and that
each treatment has a (separate) linear causal effect on outcome. To estimate the two
causal effects of treatment, the approach developed by Fischer et al.
(2011) relies on identifying baseline variables
that predict adherence differently in the two arms (i.e. they interact with a randomized
group in a model for adherence) but that do not predict the causal effect of treatment
(i.e. they do not interact with treatment in a causal model for clinical outcome).
Baseline variables that predict adherence and/or outcome (as main effects) are also useful
in improving precision. The following procedure was used to identify these baseline
variables.

BODY.METHOD.ADHERENCE-ADJUSTED EFFICACY ESTIMATES.IDENTIFYING PREDICTORS OF OUTCOME:

(1)
  All baseline variables that were possible predictors of outcome [age, gender,
ethnicity, marital status, employment status, housing status, financial strain, history
of depression (self/family), prior treatment for depression, longstanding illness,
disability/infirmity, social support, life events, alcohol score, BDI score, HADS
total/anxiety/depression subscale scores, SF-12 mental and physical subscale scores, and
number of physical symptoms] were examined in univariable linear regression models with
the BDI score at 6 weeks as a continuous outcome. Those variables that were identified
as predictors of outcome at p < 0.20 were entered into a
multivariable model. The most parsimonious model was identified using backwards
selection and the likelihood ratio test until all remaining variables were retained at
p < 0.10. Any variables not selected in the initial phase
(univariable model: p ⩾0.20) were included in the final multivariable
model one by one and retained if p < 0.10. This modelling
process was repeated for each of the additional outcomes (HADS total and subscale scores
and SF-12 mental and physical subscale scores). All models were adjusted for
stratification variables and treatment allocation to improve precision.  This liberal modelling approach ensured that all potentially influential variables were
included. Omission of a potentially important predictor of outcome from the SMM model
would result in a loss of precision.

BODY.METHOD.ADHERENCE-ADJUSTED EFFICACY ESTIMATES.IDENTIFYING PREDICTORS OF ADHERENCE:

(2)
  GENPOD relied upon self-reported use of antidepressant medication. A quantitative
measure of adherence is required for the SMM approach. Therefore, a pragmatic decision
was made to rescale the six response options using increments of 0.2 to generate an
adherence score scaled from zero to one, where zero represented total non-adherence and
one indicated 'perfect' adherence. This rescaling of the adherence measure assumed that
a 0.2 point increase in adherence had the same meaning across the scale.  The following baseline variables were possible predictors of adherence:
sociodemographic factors (age, gender, ethnicity, marital status, employment status,
housing status, financial strain), social support, history of depression
(self/family)/prior treatment for depression, longstanding illness/disability/infirmity,
personality – conscientiousness, life events, alcohol use, SF-12 physical subscale
score, and eight physical symptoms (rapid heartbeat, agitation, dry mouth, sweating,
constipation, diarrhoea, daytime drowsiness, and hot flushes). The total number of
physical symptoms at baseline was excluded from the list because it was thought that
individual physical symptoms may be more relevant to the question of adherence. For
example, if someone was already experiencing a dry mouth, taking a drug likely to affect
this may differentially affect adherence. The possible predictors of adherence were
initially examined in univariable linear regression models with adherence score as the
outcome, with adjustment for treatment allocation and predictors of outcome (identified
using the process described earlier). All variables that were identified as predictors
of adherence (either as a main effect or an interaction with treatment allocation in the
univariable models at p < 0.20) were entered into a
multivariable model with the variable specified in the appropriate form (main effect or
main effect and interaction). Interactions were evaluated one at a time using the
likelihood ratio test. Those variables for which the main effect or interaction was
significant at p < 0.10 were retained in the final multivariable
model.  In GENPOD, the primary hypotheses were about differential response to antidepressant
treatment dependent on severity of depression and genotype. To be consistent with this
hypothesis, it was deemed inappropriate to examine severity as a predictor of adherence
to medication because severity may have predicted the effect of treatment other than
through adherence. Therefore, all measures of severity of depressive symptoms (CIS-R,
BDI, HADS and SF-12 mental subscale score) were excluded from the list of potential
predictors of adherence.

BODY.METHOD.ADHERENCE-ADJUSTED EFFICACY ESTIMATES.GENERATING ADHERENCE-ADJUSTED ESTIMATES:

(3)
  The SMM approach (Fischer et al.
2011) was implemented using an instrumental
variable (IV) model approach in Stata [ivregress command: two-stage least-squares (2sls)
approach] for each of the outcomes (BDI, HADS and SF-12 mental and physical subscale
scores). Each model was specified in the following format:where y = outcome, x1 = list of
predictors of outcome (identified in stage 1), x2 = list of predictors
of adherence (identified in stage 2), x3 = stratification variables
(centre and CIS-R severity stratum), c1 = adherence score for those randomized to
treatment group 1 (citalopram), c2 = adherence score for those randomized to treatment
group 2 (reboxetine), r = treatment allocation, and * denotes an interaction, e.g.
r*x1= interaction between treatment allocation and predictors of
outcome.  The SMM method requires identification of baseline variables that predict adherence
differentially in the two arms (Fischer et al.
2011). These variables were included in
x2 and not in x1, so the interaction
r*x2 was an essential part of the model specification whereas the
interaction r*x1 is unlikely to be important and could be omitted.
Variables that may modify the causal effect of treatment should not be included in
x1 or x2.  Taking outcome as BDI score at 6 weeks as an example, the IV model estimated the causal
effects of full adherence to the two treatments (citalopram and reboxetine); that is,
the difference in mean BDI scores for full adherence with the treatment compared to no
adherence with any treatment. The difference between the two treatments was then tested
formally using the lincom command (lincom c2 – c1), which estimates an
adherence-adjusted difference in mean BDI scores between the two treatment groups and
its 95% confidence interval (CI).  Sensitivity analyses were conducted removing predictors of adherence from the list of
x2 variables one by one to examine the robustness of the findings
from the SMM IV approach for each of the outcomes.

BODY.RESULTS.TRIAL PARTICIPATION AND FOLLOW-UP:
The Consolidated Standards Of Reporting Trials (CONSORT) flowchart and baseline
comparability of the randomized groups have been published previously (Lewis et
al.
2011). In total, 601 participants were randomized
to receive either citalopram (n = 298) or reboxetine
(n = 303). The mean age of participants was 38.8 years
(s.d. = 12.4) and 68% (n = 408) were female. More than 90% of
participants had moderate (n = 305) or severe depression
(n = 245) according to ICD-10 criteria. The 6-week follow-up was
completed by 91% (n = 546) of participants (citalopram:
n = 274 and reboxetine: n = 272).

BODY.RESULTS.ADHERENCE TO, AND DOSE OF, MEDICATION:
Of those randomized to citalopram, 90% (n = 246) were still taking their
medication at the time of the 6-week follow-up, compared with 72%
(n = 195) of those randomized to reboxetine (difference: 18.4%, 95% CI
12.0–24.8, p < 0.001). At the 6-week follow-up, 149 (55%) of those
randomized to receive citalopram reported having taken their tablets 'every day', 90 (33%)
had taken 'nearly all' their tablets, and 34 (12%) had taken 'less than half', 'hardly
any' or none of their tablets. The comparable figures for those randomized to receive
reboxetine were 113 (42%), 89 (33%) and 70 (26%). As reported previously (Lewis et
al.
2011), the dose of the allocated medication was
increased by the GP only for a minority of participants [citalopram:
n = 55 (20%); reboxetine: n = 13 (5%)] during the
trial.

BODY.RESULTS.ESTIMATES OF EFFECTIVENESS:
Among the 546 participants who completed the 6-week follow-up, their mean BDI score at
baseline was 33.6 (s.d. = 9.7). The corresponding figures by trial arm are given
in Table 1. In an ITT analysis (Table 2), there was only weak evidence to suggest
that those randomized to reboxetine had a worse outcome. On average, those on reboxetine
scored one point higher on the BDI, although the 95% CI included no difference between
groups. The results of the effectiveness analyses for the other mental health outcome
measures (HADS total and anxiety/depression subscales; SF-12 mental subscale) were
consistent with this (Table 2). Hence, those
randomized to receive reboxetine had, on average, a higher score on the HADS (total and
subscales) and a lower score on the SF-12 mental subscale, indicative of a worse outcome.
Indeed, for the SF-12 mental health subscale, those randomized to reboxetine had a mean
score that was, on average, two points lower compared to those randomized to receive
citalopram. The CI surrounding this estimate excluded the possibility of no difference.
There was little evidence for any difference in outcome in terms of physical health (SF-12
physical subscale score) between those randomized to receive reboxetine compared to
citalopram (Table 2). Table 1.Baseline and 6-week follow-up scores on the outcome measures according to allocated
treatment group, in those who completed the 6-week follow-upOutcomeTime pointCitalopram
(n = 274)
a
Reboxetine
(n = 272)Mean
s.d.
Mean
s.d.
BDIBaseline34.29.333.110.06 weeks18.910.819.611.1HADS TotalBaseline26.16.025.56.46 weeks17.28.318.08.4HADS AnxietyBaseline13.33.413.13.86 weeks9.34.29.74.4HADS DepressionBaseline12.83.812.44.16 weeks7.94.98.35.0SF-12 MentalBaseline23.97.624.37.86 weeks39.211.937.212.4SF-12 PhysicalBaseline47.211.547.411.16 weeks48.210.647.810.2BDI, Beck Depression Inventory; HADS, Hospital Anxiety and Depression Scale;
SF-12, 12-item Short-Form Health Survey; s.d., standard deviation.an = 273 for SF-12 scores.
Table 2.Differences in outcomes at 6 weeks from analysis of treatment effectiveness and
estimates of efficacy from SMM models that account for differential non-adherence to
allocated treatmentEstimates of effectiveness from
analyses conducted according to the ITT principleAdherence-adjusted efficacy
estimatesOutcome
n
Adjusted difference in means
a
95% CIp value
n
Adjusted difference in means
a
95% CIp valueBDI5461.19−0.52 to 2.900.17541−0.29−3.04 to 2.460.84HADS Total5461.16−0.16 to 2.470.095431.23−0.71 to 3.170.22HADS Anxiety5460.56−0.09 to 1.210.095430.57−0.43 to 1.570.27HADS Depression5460.60−0.17 to 1.370.135430.62−0.50 to 1.740.28SF-12 Mental545−2.22−4.21 to –0.220.03543−2.53−5.55 to 0.500.10SF-12 Physical545−0.54−1.86 to 0.770.42543−0.61−2.52 to 1.310.53ITT, Intention-to-treat; BDI, Beck Depression Inventory; HADS, Hospital Anxiety
and Depression Scale; SF-12, 12-item Short-Form Health Survey; CI, confidence
interval.aAdjusted for centre, baseline severity strata (Clinical Interview
Schedule – Revised, CIS-R) and baseline score for outcome measure.Difference is reboxetine minus citalopram. A positive difference for BDI and HADS
(and a negative difference for SF-12 outcomes) indicates that those on reboxetine
have a worse outcome than those on citalopram.

BODY.RESULTS.EFFECT OF IMPUTING MISSING OUTCOMES AS ‘NON-RECOVERY’ OR USING AN LOCF APPROACH TO
HANDLING MISSING OUTCOME DATA ON ESTIMATES OF EFFECTIVENESS:
There was little evidence of a difference in the binary outcome of 'recovery' (BDI
score < 10 at 6 weeks) using observed data collected (irrespective of adherence to
allocated medication) for 91% of GENPOD participants at 6 weeks when data were analysed
using an ITT approach (Table 3). Table 3.Examining the effect of different approaches to handling missing outcome data on
the difference between treatment groups (estimates of effectiveness) in the presence
of differential adherence to treatmentOutcomeAnalysisCitalopramReboxetineDifference between
groups OR (95% CI)
a
p value
N
n (%)
N
n (%)'Recovery' (BDI score < 10)ITT on observed data (n = 546)27460 (21.9)27261 (22.4)0.95 (0.62 to 1.44)0.79Using observed outcome data for those who were
continuing to take their allocated medication at 6 weeks and assuming that
those who had stopped their allocated medication had not recovered
(n = 546)27455 (20.1)27244 (16.2)0.70 (0.45 to 1.10)0.12As above and assuming those with missing outcome
data were also non-responders (n = 601)29855 (18.5)30344.9 (14.5)0.70 (0.45 to 1.09)0.11
N
Mean (s.d.)
N
Mean (s.d.)Difference in means (95% CI)
a
BDI scoreITT on observed data (n = 546)27418.9 (10.8)27219.6 (11.1)1.19 (−0.52 to 2.90)0.17Using observed outcome data for those who were
continuing to take their allocated medication at 6 weeks and using an LOCF
approach to carry forward the baseline BDI score for those who had stopped
their allocated medication (n = 546)27420.2 (11.8)27222.6 (12.0)3.01 (1.18 to 4.85)0.001BDI, Beck Depression Inventory; ITT, intention-to-treat; LOCF, last observation
carried forward; OR, odds ratio; CI, confidence interval; s.d., standard
deviation.aAdjusted for centre, baseline severity strata (Clinical Interview
Schedule – Revised, CIS-R) and baseline BDI score.Difference is reboxetine minus citalopram. An OR < 1 for 'recovery' or a
positive difference for differences in BDI scores indicates that those on
reboxetine have a worse outcome compared to those on citalopram.  Applying the assumption that those who stopped their medication had a poor outcome to the
GENPOD data demonstrated that differential adherence to medication between arms introduced
bias such that the outcome for those randomized to reboxetine appeared worse [odds ratio
(OR) for response 0.70, 95% CI 0.45–1.10)]. Additional imputation of a poor outcome for
those individuals not followed up at 6 weeks had little effect (Table 3).  Similarly, using an LOCF approach to impute missing outcome data for those who had
stopped their medication at 6 weeks suggested that, on average, the outcome for those
randomized to reboxetine was three points higher on the BDI (more depressed) compared with
those randomized to citalopram. Analysis of the observed outcome data at 6 weeks provided
only weak evidence for a difference in outcome between the groups (Table 3).

BODY.RESULTS.ADHERENCE-ADJUSTED EFFICACY ESTIMATES:
The analyses identified several predictors of outcome and adherence within the GENPOD
dataset (see the online Appendix). As expected, for all outcomes, the strongest predictor
of outcome was the baseline measurement. In terms of predictors of adherence, those from a
non-white ethnic background were less likely to adhere to medication, whereas those who
reported a rapid heartbeat were more likely to adhere to medication. Interactions with
treatment allocation were found for three variables: marital status, prior history of
depression and the personality trait of conscientiousness. Those who were married, those
with a previous history of depression and those who were more conscientious were less
likely to adhere to reboxetine. The full specification of the IV models that generated the
adherence-adjusted estimates can be found in the online Appendix.  The adherence-adjusted differences in mean outcomes between the treatment groups are
presented in Table 2. There was weak evidence
that reboxetine was less efficacious than citalopram in terms of outcome on the SF-12
mental subscale, although the CI included the possibility of no difference. However, there
was no evidence of a difference in efficacy between the two treatments based on the other
outcomes including the BDI.

BODY.RESULTS.SENSITIVITY ANALYSES FOR THE ADHERENCE-ADJUSTED EFFICACY ESTIMATES:
The results of the sensitivity analyses examining the effect of removing predictors of
adherence from the final SMM IV models for all outcomes are summarized in Table 4. Although the adjusted difference in means
between treatment groups varied according to the list of predictors of adherence included
in the SMM model (for some outcomes more than others), the estimates were broadly
consistent when the CIs were compared. Table 4.Sensitivity analyses around adherence-adjusted instrumental variable (IV) efficacy
estimates of the mean difference in outcome between treatment groupsOutcome 〈Y〉Method
n
Adjusted difference
a
 in mean 〈Y〉 score at 6 weeks95% CIp valueBDI'Adherence-adjusted' efficacy estimate541−0.29−3.04 to 2.460.84
Excluding predictors of adherence from list of x2's one
by one
Ethnicity541−0.24−3.03 to 2.540.87Rapid heart beat541−0.17−2.94 to 2.600.90Marital status541−0.77−3.88 to 2.330.62History of depression541−0.46−3.34 to 2.420.76BFI conscientiousness score5410.29−2.59 to 3.160.84HADS Total'Adherence-adjusted' efficacy estimate5431.23−0.71 to 3.170.22
Excluding predictors of adherence from list of x2's one
by one
Ethnicity5431.34−0.64 to 3.310.18Rapid heart beat5431.13−0.82 to 3.080.26Marital status5430.94−1.17 to 3.040.38History of depression5431.32−0.67 to 3.320.19BFI conscientiousness score5431.29−0.76 to 3.340.22HADS Anxiety'Adherence-adjusted' efficacy estimate5430.57−0.43 to 1.570.27
Excluding predictors of adherence from list of x2's one
by one
Ethnicity5430.66−0.35 to 1.680.20Rapid heart beat5430.52−0.48 to 1.520.31Marital status5430.49−0.58 to 1.560.37History of depression5430.57−0.47 to 1.600.28BFI conscientiousness score5430.55−0.50 to 1.600.31HADS Depression'Adherence-adjusted' efficacy estimate5430.62−0.50 to 1.740.28
Excluding predictors of adherence from list of x2's one
by one
Ethnicity5430.63−0.50 to 1.760.28Rapid heart beat5430.56−0.57 to 1.690.33Marital status5430.43−0.77 to 1.630.48History of depression5430.72−0.44 to 1.870.22BFI conscientiousness score5430.64−0.54 to 1.830.29SF-12 Mental'Adherence-adjusted' efficacy estimate543−2.53−5.55 to 0.500.10
Excluding predictors of adherence from list of x2's one
by one
Ethnicity543−2.80−5.89 to 0.290.08Rapid heart beat543−2.29−5.32 to 0.750.14Marital status543−2.85−6.11 to 0.410.09History of depression543−2.97−6.10 to 0.170.06BFI conscientiousness score543−2.01−5.21 to 1.190.22SF-12 Physical'Adherence-adjusted' efficacy estimate543−0.61−2.52 to 1.310.53
Excluding predictors of adherence from list of x2's one
by one
Ethnicity543−0.83−2.80 to 1.130.41Rapid heart beat543−0.65−2.57 to 1.270.51Marital status5430.13−1.95 to 2.210.90History of depression543−0.96−2.97 to 1.050.35BFI conscientiousness score543−0.82−2.84 to 1.200.43BDI, Beck Depression Inventory; HADS, Hospital Anxiety and Depression Scale;
SF-12, 12-item Short-Form Health Survey; BFI, Big Five Inventory; CI, confidence
interval.aAdjusted for centre, baseline severity strata (Clinical Interview
Schedule – Revised, CIS-R) and baseline score for outcome measure.  There was no evidence to support an interaction between severity of depression (or
genotype) and response to antidepressant in the GENPOD trial (Lewis et al.
2011; Wiles et al.
2012). Therefore, excluding severity as a
predictor of adherence may be questioned. However, there was no evidence for an
interaction between severity of depression and adherence to medication (for test of
equality of coefficients: interaction between severity and adherence to
citalopram/reboxetine, p = 0.27).

BODY.DISCUSSION:
We have demonstrated how to implement the SMM approach described by Fischer et
al. (2011) in a standard statistical
software package to obtain an unbiased estimate of treatment efficacy for a trial comparing
two active treatments. Analysis was straightforward once suitable covariates for the SMM
approach were identified. Data from the GENPOD trial of the two antidepressants citalopram
and reboxetine were used as an exemplar.  The results of an effectiveness analysis (conducted according to the ITT principle) found
only weak evidence that those randomized to reboxetine had a slightly worse outcome than
those randomized to citalopram in terms of depressive symptoms (on the BDI/HADS). This is in
contrast to previous meta-analyses (Cipriani et al.
2009; Eyding et al.
2010) that suggested that reboxetine was less
effective than other antidepressants.  It is common practice in psychopharmacology trials for participants who stop taking their
allocated medication not to be followed up. Outcomes are then imputed by assuming that those
who stopped their allocated medication had a poor outcome or by carrying forward an earlier
observation (LOCF). When we applied these approaches to the GENPOD data, by artificially
assuming that outcomes were observed only for those who continued on their medication, we
found stronger evidence of a poor outcome for those randomized to reboxetine compared with
the results of analyses using all observed data. This clearly demonstrates that these common
approaches to handling missing data may generate biased estimates of effectiveness when
there is differential non-adherence between treatment arms.  Using the SMM approach to account for differential non-adherence to treatment between trial
arms, we found no evidence of a difference in efficacy in terms of depressive symptoms (BDI)
between reboxetine and citalopram at 6 weeks. The adherence-adjusted estimate (based on the
difference in causal effects for full adherence to the treatment) was close to the null.
There was weak evidence for a difference in efficacy between treatment with reboxetine and
citalopram for the SF-12 mental subscale. In discussing these differences, it is important
to consider whether these are clinically relevant. Although there is no consensus regarding
a 'minimum clinically important difference' on these outcome scales, a change of
0.33 s.d. is often used as the target difference in primary care depression
trials (Baxter et al.
2010). Hence, we would regard a three-point change
in BDI score, a two-point change in HADS score (one point on subscales) and a three- to
four-point change in SF12 scores to be clinically important. The differences and CIs
observed in terms of estimates of efficacy from analyses using the SMM approach are smaller
than these and, except for the results for the SF-12 mental subscale, we can therefore
exclude the possibility of a clinically important difference between citalopram and
reboxetine in those who can tolerate the medications.

BODY.DISCUSSION.STRENGTHS AND LIMITATIONS:
The SMM approach used depends on finding baseline covariates that predict adherence
differently in the two randomized groups but that may be assumed not to modify the causal
effect of treatment. Bias would occur if the latter assumption failed. In addition, it is
assumed that the average outcome does not depend on treatment assignment (the 'exclusion
restriction'). In a non-blinded trial such as GENPOD, there is a theoretical possibility
that this assumption could be violated given prior beliefs about the treatment. However,
there is little evidence to suggest that patients had different expectations of outcome
for the two antidepressants.  Predictors of adherence were removed from the final SMM IV models one at a time to
examine the robustness of the findings. The results of these sensitivity analyses show
that the estimates were broadly consistent with the final SMM model incorporating all
predictors of adherence.  GENPOD relied upon a self-report measure of adherence to medication. Use of electronic
monitoring bottles would provide a more accurate measure of adherence. Such data would
also provide a continuous adherence score as required for application of the SMM
methodology. We rescaled the self-report adherence data to generate a continuous measure
of adherence to apply this methodology, albeit therefore introducing some modelling
assumptions. At the same time, there was no reason for participants to be motivated to
mislead the researchers about their use of medication and we therefore have no reason to
suppose that this measure was biased.  In total, 601 participants were recruited into the GENPOD trial, making this one of the
largest primary care depression trials conducted. Nonetheless, despite its large size, it
is of note that estimates obtained from models based on instrumental variables methods
remain imprecise.

BODY.DISCUSSION.COMPARISONS WITH EXISTING LITERATURE FOR COMPARATIVE EFFECTIVENESS OF ANTIDEPRESSANTS:
Meta-analyses have suggested that reboxetine may be less effective than other
antidepressants (Cipriani et al.
2009; Eyding et al.
2010). However, in effectiveness analyses of data
from the GENPOD trial, we found only weak evidence of very small differences in mental
health outcomes (that were unlikely to be clinically significant) at 6 weeks for those
randomized to reboxetine compared with citalopram. Both meta-analyses (Cipriani et
al.
2009; Eyding et al.
2010) reported that patients randomized to
reboxetine were more likely to discontinue treatment compared with those randomized to
SSRIs, which is consistent with the findings from GENPOD. However, as we have
demonstrated, the assumption that individuals with missing outcome data have not responded
to treatment may introduce bias in estimates of effectiveness, such that those on
reboxetine seem to do worse. It is therefore important to continue to follow-up trial
participants to collect outcome data even if they stop taking the trial medication.

BODY.DISCUSSION.EXTENSIONS TO THE SMM METHODOLOGY:
We have described the SMM approach for estimating efficacy for a singly-measured
quantitative outcome. For a repeated-measured quantitative outcome, a structural nested
mean model could be used (Robins, 1994). For a
binary outcome, the SMM approach can be used to estimate risk differences, but if interest
lies in risk ratios or ORs then a multiplicative SMM or a generalized SMM is needed
(Vansteelandt & Goetghebeur, 2003). For
time-to-event outcomes, rank-preserving structural nested failure time models could be
used (Robins & Tsiatis, 1991).  The methods we have described are especially appropriate for equivalence and
non-inferiority trials because ITT analysis is known to be anti-conservative in such
trials (Jones et al.
1996) whereas per-protocol analyses are
potentially biased (Fleming, 2008). An alternative
approach to handling non-adherence is the complier average causal effect (CACE; Dunn
et al.
2003) model, but this is not well defined in
trials comparing two active treatments and also requires adherence to be binary.
Dichotomizing a continuous adherence measure is usually undesirable (White et al.
2011).

BODY.DISCUSSION.IMPLICATIONS AND FURTHER RESEARCH:
It is common practice in RCTs of pharmacological interventions for participants not to be
followed up if they stop taking the trial medication. Such a policy is at odds with
conducting primary trial analyses according to the principle of ITT, and assumptions that
are then made regarding missing data frequently bias estimates of effectiveness.  Differential non-adherence between treatment arms presents a particular challenge for
trialists. However, as illustrated, it is possible to implement the analytical methods
described (Fischer et al.
2011) in a standard statistical software package
to take account of non-adherence to treatment when comparing two (or more) active
interventions. Such methods will generate an unbiased estimate of the difference in
treatment efficacy that is of value to the clinician in terms of describing the likely
outcomes when drugs are both taken, and tolerated, by patients.

BODY.SUPPLEMENTARY MATERIAL:
Supplementary MaterialSupplementary information supplied by authors.Click here for additional data file.

Question: Compared to citalopram what was the result of reboxetine on Using structural mean modelling (SMM) approach for Beck Depression Inventory (BDI) scores?
Evidence: In ITT analysis, there was only
weak evidence that those on reboxetine had a slightly worse outcome than those on
citalopram [adjusted difference in mean Beck Depression Inventory (BDI) scores: 1.19,
95% confidence interval (CI) –0.52 to 2.90, p = 0.17]. 
Answer: no significant difference