STUDY PLANS FOR THE INDIVIDUAL PARTICIPANT DATA META-ANALYSIS OF
ANTIDEPRESSANT TRIALS FOR MAJOR DEPRESSION IN JAPAN
Steering Committee
Prof Shigeto Yamawaki (Hiroshima University, Former President of CINP)
Prof Emeritus Jun Ishigooka (Tokyo Women’s College of Medicine, Former President of JSNP)Dr Kazutaka Ikeda (Tokyo Metropolitan Institute of Medical Science, President of JSNP)
Prof Toshiaki Furukawa (Kyoto University)
Prof Shiro Tanaka (Kyoto University)
A/Prof Norio Watanabe (Kyoto University)
A/Prof Hisashi Noma (The Institute of Statistical Mathematics)
Dr Kazushi Maruo (National Center of Neurology and Psychiatry)
A/Prof Andrea Cipriani (University of Oxford)(Data Manager)
Dr Hissei Imai (Kyoto University)

Principles of data sharing and data analyses
1.

Data will be securely managed and only members of the Steering Committee (SC) may haveaccess to individual data. A medium (either a hard disk or a USB memory) containing theindividual participant data will be stored in a locked room and data will be analyzed on acomputer that is not connected to the internet, except for the data from GSK which will beanalyzed online.
2. All analyses will follow the study plans as specified and agreed-upon below. A new contract will
be required if any other analyses are to be undertaken.
3. The members of SC will conduct the analyses, write up and publish the papers.
4. When any doubt arises, SC will request clarification from the relevant manufacturer. All data
used in the PMDA submission and CSR will be used as such.
5. SC retains the right to publish all the results of the analyses below, both primary and secondary,that have been conducted according to the following study plans.
6. None of the following analyses, primary or secondary, will contain any direct, overall comparisonof efficacy or acceptability among active drugs, as the network is a so-called star-shaped network
and will therefore not allow any informative analyses allowing such comparisons. Specific drug
names will be mentioned in the section of the included studies but not in the results section for
global efficacy or acceptability comparison. However, some subgroups may be identified who areparticularly responsive or unresponsive to particular drugs.
7. A number of trials have active drug comparators but the data for these active comparators may
not be shared due to the license agreement with the manufacturer of these drugs. Because thenumber of participants allocated to such active drug comparators is usually small, the present
study can proceed without these data.
8. The names of the members of the Working Group and of the manufactures will be acknowledgedin the acknowledgement section of the publications but they will not be co-authors orinvestigators.
9. All manufacturers agreeing to the above principles promise to provide all data, if present, thatwill be requested in the data template accompanying the study plans.
10. Primary analyses #1-#2 will be registered in PROSPERO. Primary analysis #3 and secondary
analyses will be more exploratory and will not be pre-registered. We intend to publish all of theseanalyses in the end.

Primary analyses
The main purposes of the present study are threefold:
1A) Identify subgroups of patients that show greater differentiation between antidepressantsand placebo, in order to contribute to the planning of more efficient trials in the futureB) Identify subgroups of patients that show smaller differentiation between antidepressantsand placebo, in order to identify unmet medical needs① It would be meaningful to examine biomarkers for this subset of patients too.
C) Specify placebo responders (narrowly defined), in order to distinguish between true drug
responders and placebo responders among those who apparently respond to drugs and thus
help identify bio-markers of drug responses
and we will conduct the following primary analyses to address these questions. None of the following
analyses will contain any direct, overall comparison of efficacy or acceptability among active drugs.
1

The first study will focus on baseline depression severity as effect modifier for efficacy, because
this is an unresolved, hot issue in clinical psychopharmacology and will have a great clinicalimplication.
1.1

The primary outcome will be the Hamilton Rating Scale for Depression (HAMD) or
Montgomery-Asberg Rating Scale (MADRS) as specified as primary in the original trial.
When different versions of HAMD or MADRS are used, the scores will be converted using
the conversion table based on the item response theory 1.

1.2

All arms within the following licensed dose range (FDA, EMA, PMDA) will be treated as
effective dose.

bupropion
desvenlafaxine
duloxetine
escitalopram
mirtazapine
paroxetine
paroxetine CR
venlafaxine

1.3

2

licensed dosevery low

low

300-400
50-10040-120
10-20
15-45
20-50
25-62.5
75-375

150
25

300
50
40, 60
10
15
20
25
75

moderate
to highvery high20
30, 4540
50
75-225(mean=20
2)

We will then conduct IPD-MA to examine the relationship between baseline symptom
severity and the differences in change scores between the drugs and placebo using a threelevel mixed-effects model with maximum likelihood or restricted maximum likelihood
estimation 2,3. The levels will account for the data structure such that: level 1 represented
time, level 2 the participant, and level 3 the trial. The following competing models withincreasing complexity will be applied: (Model 1) time and treatment and the two-way timeby treatment interaction; (Model 2) Model 1 plus baseline symptom score and all two-way
interactions among time, treatment and baseline score; (Model 3) Model 2 plus the threeway interaction of linear time by treatment by baseline score; and (Model 4) Model 3 plus
the two-way and three-way interactions among quadratic time, treatment and baseline
score. The time variable will be treated as the categorical or continuous variable. Thesemodels will be tested unadjusted and adjusted for confounders. 4

The second study will look at all other effect predictors and effect modifiers for efficacy anddropouts.
22.1

IPD-MA of efficacy
2.1.1 The primary outcome will be the Hamilton Rating Scale for Depression (HAMD) or
Montgomery-Asberg Rating Scale (MADRS) as specified as primary in the original trial.
When different versions of HAMD or MADRS are used, the scores will be converted
using the conversion table based on the item response theory 1.
2.1.2 The literature suggests many candidates for effect predictors (variables associatedwith response regardless of the treatment) and for effect modifiers (variables
associated with differential response depending on the treatment) in the treatment ofdepression 5. We have listed the possible candidate variables for effect predictors andeffect modifiers based on the literature in the following. The variables to be actually
examined will first be limited by their availability in the included original studies butwhen several variables that measure similar concepts are available, the research teamwill discuss which ones we believe are the most important predictors and which should
be included in the model.
Demographics
1) Age 6
Life and social history
2) Childhood maltreatment 73) Education 84) Employment 9,105) Marital status 9-11
6) Recent life events and difficulties 9,10
7) Social adjustment/function 12
History of present illness8) Age at onset 139) Chronicity 610) No of previous episodes 8,14
11) Prior treatments with antidepressants 10
Present illness: symptomatology
12) Baseline severity 15-17
13) Baseline psychomotor symptoms 12,18
14) Baseline anxiety symptoms 18,19
15) Comorbid personality disorder 1016) Comorbid substance use/abuse 18
Therapeutic process17) Early response 20
18) Co-prescriptions other than antidepressants
2.1.3 We will then conduct IPD-MA to examine the relationship between each independent
variable and the differences in change scores between the drugs and placebo using alinear mixed-effects model with maximum likelihood or restricted maximum likelihood
estimation as delineated above. The between-studies and between-drugs
heterogeneities will be modelled by random effects. The effect modifications areevaluated by involving treatment-by-covariates interaction terms in the mixed-effects
model analysis. The strategy of modeling variables will be explanatory determinedbecause there have been only limited prior information what variables have predictive
abilities and how these variables should be modelled in the statistical models. However,
we will use the candidate variables noted above and these analyses would be conductedwithin the framework of linear mixed model analyses. We expect to assemble 4000
participants’ individual data which will provide enough power to examine the available
variables.
32.1.4 In addition, we would attempt other data-driven subgroup identification methods
which might detect predictive factors and responder subgroups more efficiently, if
necessary. We will adopt the SIDES (subgroup identification based differential effectsearch) procedure and its companion methods 21-23 that effectively identify responder
subgroups. The SIDES and its companion procedures are flexible searching algorithms
that split the patient population for exploring the subpopulations that the treatment
effects would be particularly different. These algorithms can also control the random
errors effectively in the comprehensive explanatory analyses. The candidate variables
and the analysis statistical models will be limited as the same with Section 2.1.3.
2.1.5 The obtained model will be used to build subgroups of those who are likely to respondon drug but not on placebo, those who are likely to respond both on drug and on placebo,and those who are likely not to respond to drug or placebo.
2.1.6 The following sensitivity analyses will be conducted using meta-regression in order toexamine the robustness of the obtained results.
1) Use of placebo run-in phase2) Probability to be allocated to placebo24
2.2

IPD-MA of response, dropouts for inefficacy, dropouts for side effects, and dropouts forremission on antidepressants and placebo
2.2.1 The average dropout rate in antidepressant trials hovers around 35-40% 25 and casts
serious doubt on the validity of their analyses. Identifying predictors and moderatorsof dropouts will contribute to recruitment of subjects less likely to dropout and thusincrease the internal validity of the future trials of antidepressants.
2.2.2 We expect predictors and modifiers for dropouts due to inefficacy or remission to be
similar across drugs. Those for dropouts due to side effects will likely be different acrossdrugs.
2.2.2.1 Cytochrome P450 subtypes were measured for 2C19 in escitalopram and 2D6 forvenlafaxine only.
2.2.3 The IPD-MA should be conducted using logistic mixed effects model that accommodates
between-studies and between-drugs heterogeneities. The outcome variables are
response, dropouts for inefficacy, dropouts for side effects, and dropouts for remission
at the follow-up periods. The effect modifications are evaluated by involving treatmentby-covariates interaction terms and the strategy of selection of modeling variables will
be explanatory determined as the same with the analyses for efficacy in Section 2.1.3.

3Studies #1 and #2 will be registered in PROSPERO.

Predictors of response on placebo
3.1

The primary outcome will be response defined as 50% or greater reduction on any observerrated depression scale specified as primary in the original study.

3.2

Logistic regression with random effects for explaining between-studies heterogeneity will
be used to predict response on placebo. Candidates of covariates in the model (we will use
the same list as above) will be screened through a backward variable selection with thecritical value of p = 0.15.

3.3

In addition, we would attempt other data-driven subgroup identification methods which4might detect predictive factors and responder subgroups more efficiently, if necessary. 21-23
3.4

External validity of the final multi-variate logistic regression will be evaluated with theuse of leave-one-trial-out cross-validation. Hosmer-Lemeshow test will be used as ameasure for calibration and C-statistics will be used as a measure for discrimination.

3.5

If the model is proven to be stable and efficient, it will then be used to distinguish true drug
responders and placebo responders among those who apparently respond on drugs.

Secondary analyses
Because the data that we envision to obtain in this study is so rich in contents, we would like toconduct the following additional analyses to advance the clinical trial methodology and theunderstanding of depression symptomatology. The following analyses are mainly exploratory innature. None of the following analyses will contain any direct, overall comparison of efficacy or
acceptability among active drugs.
4

Based on pre-post data
4.1

Will raising the baseline eligibility threshold decrease the standard deviation and thereby
increase the effect size of the trial? We have already undertaken a similar analysis in
schizophrenia antipsychotic trials and found that it cannot. We will use the same
methodology and examine if the results are different or similar for antidepressants.
--Furukawa TA & Leucht S (2013) Can we inflate effect size and thus increase chances of
producing "positive" results if we raise the baseline threshold in schizophrenia trials?
Schizophrenia Research, 144, 105-108.

4.2

Do effect size estimates differ depending on the definitions of response and remission? Wehave already conducted a similar study in schizophrenia antipsychotic trials and shownthat relative indices of effect remain constant, regardless of the definitions of response andremission. We will use the same methodology and examine if the results are different orsimilar for antidepressants.
--Furukawa TA, Akechi T, Wagenpfeil S & Leucht S (2011) Relative indices of treatment
effect may be constant across different definitions of response in schizophrenia trials.
Schizophrenia Research, 126, 212-219.

4.3

Change scores vs endpoint scores: do they lead to different estimates in individual RCTs
and can they be mixed in meta-analyses? There are conflicting reports on thismethodological question important in evidence synthesis. Using the IPD we can examine
the possible differences in the pooled results of change scores and endpoint scores.
--Fu R & Holmer HK (2016) Change score or follow-up score? Choice of mean difference
estimates could impact meta-analysis conclusions. Journal of Clinical Epidemiology.
--da Costa BR, Nuesch E, Rutjes AW, Johnston BC, Reichenbach S, Trelle S, Guyatt GH &Juni P (2013) Combining follow-up and change data is valid in meta-analyses of continuous
outcomes: a meta-epidemiological study. Journal of Clinical Epidemiology, 66, 847-855.

4.4

Which items in HAMD or MADRS are most sensitive to change in depression trials? A
recent re-analysis based on IPD has revealed that the first item of HAMD may be the mostsensitive. We will use the same methodology and examine which items of HAMD or MADRS(or QIDS-SR and IDS-SR) are most sensitive to change.
--Hieronymus F, Emilsson JF, Nilsson S & Eriksson E (2016) Consistent superiority of
selective serotonin reuptake inhibitors over placebo in reducing depressed mood in patientswith major depression. Molecular Psychiatry, 21, 523-530.
5

4.5

The old “myths” of antidepressant responsiveness can now be more accurately addressedfrom the view point of (i) absolute response rates (i.e. are the response rates in the following
subgroups higher than those in the others) and (ii) relative response (i.e. are the RR of
response in the following subgroups higher than in the others). The “myths” to be addressedare
4.5.1 Antidepressants are ineffective in depression due to exogenous stressors (stressors
were measured in escitalopram trials)
4.5.2 Antidepressant are particularly effective in endogenous depression (DSM-IV
melancholic features were measured in almost all trials, diurnal variation is measured
in SIGH-D)

4.6

Variability in response rates, dropout rates, and efficacy across sites/region
4.6.1 It would be impossible to identify the same sites across studies.

5

Based on longitudinal, repeated measurements
5.1

Trajectory of symptoms of depression: Each symptom and symptom factors of HAMD and
MADRS will be examined as to its time course. Similar items of self-rated symptoms fromQIDS and IDS may also be compared.

5.2

What early signs within the first 1 or 2 weeks predict later response and remission on
antidepressants and on placebo? e.g. Early non-response has been shown to predict later
non-response. Those who complain of typical side effects, such as nausea on SSRI/SNRI
and sleepiness on mirtazapine, may show higher response than those without any such side
effects.
This is also a hot topic in recent literature and the following studies have each examined
related but different predictors.
--Mitchell AJ (2006) Two-week delay in onset of action of antidepressants: new evidence. Br
J Psychiatry, 188, 105-106.
--Tylee A & Walters P (2007) Onset of action of antidepressants. BMJ, 334, 911-912.
--Henkel V, Seemuller F, Obermeier M, Adli M, Bauer M, Mundt C, Brieger P, Laux G,
Bender W, Heuser I, Zeiler J, Gaebel W, Mayr A, Moller HJ & Riedel M (2009) Does early
improvement triggered by antidepressants predict response/remission? Analysis of datafrom a naturalistic study on a large sample of inpatients with major depression. J Affect
Disord, 115, 439-449.
--Lam RW (2012) Onset, time course and trajectories of improvement with antidepressants.
European Neuropsychopharmacology, 22 Suppl 3, S492-498.

5.3

Sustainability of response on drug and placebo: What happens to early responders toplacebo (with or without placebo run-in) and drug? We will identify early responders on
placebo, defined as >25% decrease within the first one week and some similar but variable
definitions, and describe their later course in comparison with the other patients.
--Quitkin FM, McGrath PJ, Rabkin JG, Stewart JW, Harrison W, Ross DC, Tricamo E,
Fleiss J, Markowitz J & Klein DF (1991) Different types of placebo response in patients
receiving antidepressants. The American Journal of Psychiatry, 148, 197-203.
--Quitkin FM, Stewart JW, McGrath PJ, Nunes E, Ocepek-Welikson K, Tricamo E, Rabkin
JG & Klein DF (1993) Further evidence that a placebo response to antidepressants can be
identified. The American Journal of Psychiatry, 150, 566-570.

5.4

Methodological comparison of LOCF, MI and MMRM
Several studies have already examined the results of the above-mentioned analyses inclinical trial data. However, no similar comparison has been conducted with antidepressant6

trials.
-- Mallinckrodt CH, Clark WS & David SR (2001) Type I error rates from mixed-effects
model repeated measures versus fixed effects analysis of variance with missing values
imputed via last observation carried forward. Drug Information Journal, 35, 1215-1225.
--Cook RJ, Zeng L & Yi GY (2004) Marginal analysis of incomplete longitudinal binary data:a cautionary note on LOCF imputation. Biometrics, 60, 820-828.
--Leucht S, Engel RR, Bauml J & Davis JM (2007) Is the superior efficacy of new generation
antipsychotics an artifact of LOCF? Schizophrenia Bulletin, 33, 183-191.
--Siddiqui O, Hung HM & O'Neil R (2009) MMRM vs LOCF: a comprehensive comparison
based on simulation study and 25 NDA datasets. Journal of Biopharmaceutical Statistics,
19, 227-246.
-- Grobler AC, Matthews G & Molenberghs G (2014) The impact of missing data on clinical
trials: a re-analysis of a placebo controlled trial of Hypericum perforatum (St Johns wort)and sertraline in major depressive disorder. Psychopharmacology, 231, 1987-1999.
5.5

Low dose vs high dose: is high dose clinically meaningful? The following new study is thefirst study ever to show dose-response in antidepressants. Because the studies to be
included in the current study has often examined two different dosages of the same drug inthe fixed regimen, it will be possible to examine the difference between the lower dose vs
the upper dose within the licensed dosage. We will conduct a three-node IPD network metaanalysis of placebo vs low dose vs moderate to high dose in order to estimate the relative
efficacy of placebo vs low dose vs moderate to high dose arms.
--Jakubovski E, Varigonda AL, Freemantle N, Taylor MJ & Bloch MH (2016) Systematic
Review and Meta-Analysis: Dose-Response Relationship of Selective Serotonin Reuptake
Inhibitors in Major Depressive Disorder. American Journal of Psychiatry, 173, 174-183.

5.6

Methodological research on new methods for discovering subgroup differences within IPDMA: In the literature, several effective data-driven subgroup identification methods havebeen proposed, which will be used in Section 2. However, these methods generally focus on
applications within a randomized clinical trial, which has limited statistical powers for
detecting treatment-by-covariates interactions. IPD-MA are expected to gain statisticalpowers via synthesizing much larger statistical information for multiple randomized trials.
However, there is ample room of improving such methods and we would like to develop new
methods to identify subgroup differences within IPD-MA, and their effectiveness will be
evaluated via applications to this IPD-MA. These analyses will be conducted within theframework of Section 2.
-- Lipkovich I, Dmitrienko A, B. R. & D' Agostino S (2017) Tutorial in biostatistics: datadriven subgroup identification and analysis in clinical trials. Statistics in Medicine, 36, 136196.

list of available studies

7

Overview of Placebo-controlled MDD studies conducted in Japan (based on the published data)
Fluoxetine
Phase
RegionStudy
PeriodPrimary
Measure% ofPatients
Received
Placebo
ActiveControlPrimary
ResultAge (SD)/placebo

8)

Venlafaxine ER

10)

Vortioxetine

9)

Bupropion SR

Ph3
Japan

Ph3
Japan

Ph3
Japan

Mar. 2013Jul. 2014
HAMD21 /GRID-HAMD

Nov. 2011Mar. 2014

May 2011Dec. 2012

Ph3
Japan (80%)
Korea (20%)
Jun. 2010Sep. 2012

HAMD17

MADRS

MADRS

51%
(259/510)34%(184/535)34%(123/364)33%(186/564)

7)

Paroxetine CR
Ph3
Japan (89%)
Korea (11%)
Apr. 2009Feb. 2010
HAMD17 /SIGH-D

2)

Escitalopram

4)

Ph3
Japan

Desvenlafaxine

5)

Duloxetine

1)

Miltazapine

6)

Escitalopram

Ph3
Japan

Ph3
Japan

Ph2
Japan

Apr. 2008Dec. 2010

Ph3
Japan (52%)
US (48%)Dec. 2008Apr. 2010

Jun. 2006Sep. 2007

MADRS

HAMD17

HAMD17

Nov. 2004Dec. 2005
HAMD17 /SIGH-D

Nov. 2004Dec. 2005
HAMD17 /SIGH-D42%(171/412)

26%(124/484)33%(231/699)34%(148/440)26%(70/270)34%(100/297)

N

N

N

N

N

Negative

NegativeY(Paroxetine)
Positive

PositiveY(Paroxetine)
Positive

N

PositiveY(Paroxetine)
Positive

N

Negative

Positive

Negative10)

38.5 (11.7)

38.6 (11.1)

37.6 (10.7)

37.9 (11.1)

36.8 (10.07)

36.4 (10.8)

40 (12)38.7 (10.5)

39.9 (12.8)

34.7 (8.6)

first orreccurent
eposode/placebo

N.A.

N.A.

F: 61.3%
R: 38.7%

F: 53%
R: 46%

F: 53%
R: 47%

F: 57.3%
R: 42.8%

N.A.

F: 61.4%
R: 38.7%

F: 64.3%
R: 27.2%
unknown 8.6%

F: 59.0%
R: 41.0%

Current
MDD
Episode/placebo

N.A.

N.A.

N.A.

Mean:26.8
Mean:33.5 (23.65) Mean: 13.2 (22.8)(19.4)week
week
monthsMedian: 21.0 week

Mean: 18 (28)monthsN.A.

Mean: 12.4
(17.7) months

Mean: 5.8 (11.0)
monthsMean total
score (SD)at Baseline/placebo

N.A.

N.A.

HAMD1722.6 (2.75)

MADRS
29.0 (5.6)

HAMD1723 (3)

HAMD1722.7 (3.3)

HAMD1722.5 (3.6)

HAMD1722.5 (3.6)

Total
HAMD21 ≥ 20
CGI-S ≥ 4

Total
MADRS ≥ 26
CGI-S ≥ 4

Total
HAMD17 ≥ 20

Total
MADRS ≥ 22
CGI-S ≥ 4

Total
HAMD17 ≥ 20
CGI-S ≥ 4
Depressed
mood ≥ 2

Total
HAMD17 ≥ 19

Total
HAMD17≥ 18

Total
HAMD17 ≥ 18

MADRS
32.5(4.5)
HAMD1721.5 (4.48)
Total
MADRS ≥ 26
CGI-S ≥ 4

N.A.
Total
HAMD17 ≥ 20
CGI-S ≥ 4

Depressed
mood ≥ 2
Key
Inclusion
Criteria

Current
single episode≥ 90 days
Recurrent
episode ≥ 28 days
QIDS16-J-SR≥ 16

Current
episode≥ 3 months

Current episode≥ 8 weeksand< 24 months

Current
episode≥ 4 weeks

Depressed
mood ≥ 2

Depressed
mood ≥ 2

Current
episode≥ 30 days

IDS-SR ≥ 25
Subscale (Item 5)
IDS-SR ≥ 7*We expect to obtain IPD from all the studies except for fluoxetine and vortioxetine.

Time schedule
December 21, 2016
January 18, 2017
By end of
February, 2017

By end of April,2017
By end of June,2017

1st meeting for the workgroup
2nd meeting for the workgroup, with A/Prof Andrea Cipriani
The study plan will be finalized in English. Japanese abstract will be
prepared.
The non-disclosure agreement will be signed off by respective manufacture
and the Steering Committee.
For drugs by Pfizer, we will need to make requests at Pfizer Inspirehttps://iirsubmission.pfizer.com/_layouts/InspiirePortal/（TAF has checkedthat NCT01441440 (venlafaxine trial) and NCT00798707 (desvenlafaxine
trial) are available as IPD at this site. The web page provides the email
address to send the study protocol to.）For drugs by GSK, we will make requests throughclinialstudydatarequest.com as “CINP/JSNP PPP Joint Task Force study
plans”.
We would like to obtain the following data.
 Individual participant data as used in the PMDA submission and CSR(in csv format)
 List of definitions of variables
Finish formatting the obtained data in the common format, ask for any
necessary additional clarifications and fix the dataset
8July 2017 -Analyses and publications

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