PROSPERO International prospective register of systematic reviews
Comparative efficacy and acceptability of pharmacological treatments in the acute phase treatment of bipolar depression: a multiple treatment meta-analysis
Tomofumi Miura, Toshiaki Furukawa, Shigenobu Kanba, Hisashi Noma, Shiro Tanaka, Keisuke Motomura, Hiroshi Mitsuyasu, Satomi Katsuki, Andrea Cipriani, Sarah Stockton, John Geddes, Georgia Salanti
Tomofumi Miura, Toshiaki Furukawa, Shigenobu Kanba, Hisashi Noma, Shiro Tanaka, Keisuke Motomura, Hiroshi Mitsuyasu, Satomi Katsuki, Andrea Cipriani, Sarah Stockton, John Geddes, Georgia Salanti. Comparative efficacy and acceptability of pharmacological treatments in the acute phase treatment of bipolar depression: a multiple treatment meta-analysis.
Available from http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42012002744
To compare the efficacy and acceptability of different drugs or combinations of drugs in the acute phase treatment of bipolar depression in adults.
We will search MEDLINE, PsycINFO, EMBASE and the Cochrane Central Register of Controlled Trials (CENTRAL).
In order to identify randomized trials, we will use the search term of the Cochrane highly sensitive search strategy for identifying randomized trials in each databases with sensitivity-maximizing version (Cochrane handbook). Together with RCT filters, we will search generic terms for bipolar depression and individual drug names.
References to trials are also sourced from international trials registers via the World Health Organization’s trials portal (http://apps.who.int/trialsearch/); regulatory agencies; drug companies; the hand-searching of key journals, conference proceedings and other (non-Cochrane) systematic reviews and meta-analyses. No language restriction will be applied.
Types of study to be included
Double-blind randomized controlled trials (RCTs) comparing any psychotropic agent with placebo, or one another in the treatment of acute major depressive episode in bipolar I disorder or bipolar II disorder will be included. Studies which include both unipolar and bipolar participants will be accepted if data are available for bipolar participants separately. We will accept studies that focused on specific conditions like rapid cycling.
Quasi-randomized controlled trials, in which treatment assignment is decided through methods such as alternate days of the week, will be excluded. We will exclude open-label RCTs.
Condition or domain being studied
Bipolar disorder is a complex disorder, which is characterized by recurrent episodes of depression and mania (bipolar I disorder) or hypomania (bipolar II disorder)(American Psychiatric Association, 1994). The lifetime prevalence of any bipolar disorders, bipolar I and II disorders have been estimated at 1.1%, 0.7% and 0.5% respectively using the World Mental Health Survey version of the WHO Composite International Diagnostic Interview (Suppes et al. , 2001).
The mean age at onset of bipolar disorder is reported to be in the early 20s, but its complex clinical features make its diagnosis difficult and there is a difference of about 8 years between age at onset and age at first treatment (Suppes, Leverich, 2001). Moreover, bipolar disorder has a chronic course of illness. The long-term prospective follow-up studies revealed that the percentages of bipolar I patients who remained in remission for years are substantially low, 28% for 4 years and about 10% for 5 years (Goodwin and Jamison, 2007, Keller et al. , 1993, Tohen et al. , 1990).
Bipolar disorder is associated with lower health-related quality of life, lower social functioning, unemployment and lower productivity than the general non-ill population (Dean et al. , 2004). Altogether, bipolar disorder is estimated to be the 30th leading cause of disability-adjusted life years lost for the human kind according to the latest WHO Global Burden of Disease study (WHO, 2008).
The long-term follow-up natural history studies for bipolar disorder show that the amounts of time periods that bipolar I and bipolar II patient have been in depressive episode were estimated at 31.9% and 50.3%, respectively (Judd et al. , 2003, Judd et al. , 2002). The impact of the depressive episodes on the course of bipolar disorder and on the social disability of the patient is revealed to be greater than those of manic episodes (Calabrese et al. , 2004).
Participants aged 18 or older, of both sexes with a primary diagnosis of acute major depressive episode in bipolar I disorder or bipolar II disorder, diagnosed according to any of the following operationalized criteria: Research Diagnostic Criteria, DSM-III, DSM-III-R, DSM-IV, DSM-IV-TR or ICD-10. Operationalized criteria essentially resembling these official ones will also be eligible.
Bipolar disorder in children and adolescents is difficult to diagnosis because of its atypical symptoms. It also occurs with common child-onset mental disorders, including attention deficit/hyperactivity disorder (ADHD). We will exclude childhood bipolar disorder because special considerations are needed for its pharmacological treatment.
We will include all the pharmacological interventions with prescription drugs in the treatment of acute major depressive episode in bipolar I, or II disorder, even if they are not licensed in any countries.
We will exclude the interventions with over-the-counter drugs, herbal medicine or nutritional supplement. Psychological therapy will not be the focus for this review. However, if the same type and amount of psychosocial intervention is provided to two arms which compared two drug treatments, such studies will be included.
(1) Treatment efficacy:
number of patients who respond to treatment, based on changes on Montgomery-Asberg Depression Rating Scale (MADRS) (Montgomery 1979) or Hamilton Rating Scale for Depression (HAM-D) (Hamilton 1960), or any other validated depression scale at the end of acute phase treatment (8 weeks, range 4-12 weeks). Many studies define response by 50% or greater reduction on the rating scale; we will accept the study authors' original definition. If the original authors report several outcomes corresponding with our definition of response, we will give preference to MADRS. Any version of HAM-D will be accepted.
(2) Treatment acceptability:
number of participants who drop out of treatment for any reasons during the first 8 weeks of treatment (range: 4-12 weeks).
The first 8 weeks of treatment (range: 4-12 weeks).
Remission: number of patients who remit on treatment, based on the endpoint absolute status of the patients, as measured by MADRS, HAM-D, or any other validated depression scale. Examples of definitions of remission include 7 or less on 17-item HAM-D (Furukawa et al. , 2007, Tohen et al. , 2009). The definitions of remission on MADRS differ according to the primary diagnosis. It is defined as 11 or less on the score in major depressive disorder (Bandelow et al. , 2006) and as 7 or less in bipolar disorder (Tohen, Frank, 2009); we will accept the study authors' original definition. If the original authors report several outcomes corresponding with our definition of remission, we will give preference to MADRS.
Severity of depression symptoms, based on a continuous outcome of group mean scores at the end of treatment using MADRS, HAM-D, or any other validated depression scale.
The number of participants who switched their mood episode to mania/hypomania association with the treatment.
The number of participants who completed suicide or made a suicide attempt.
Frequency, severity and category of other clinically significant adverse events.
Risk of bias (quality) assessment
Risk of bias will be assessed for each included study using the Cochrane Collaboration 'risk of bias' tool (Higgins and Green, 2011). The following 7 domains will be considered:
1. Sequence generation: was the allocation sequence adequately generated?
2. Allocation concealment: was allocation adequately concealed?
3. Blinding of participants, personnel and outcome assessors for each main outcome or class of outcomes: was knowledge of the allocated treatment adequately prevented during the study?
4. Incomplete outcome data for each main outcome or class of outcomes: were incomplete outcome data adequately addressed?
5. Selective outcome reporting: are reports of the study free of suggestion of selective outcome reporting?
We also assessed
6. Sponsorship bias.
7. Other sources of bias included but are not limited to:
- Suboptimal randomization, such as recruiting additional patients to one arm which had a large number of dropouts<br/>
- Stopped early due to some data-dependent process (including a formal-stopping rule)
- Had extreme baseline imbalance
- Differential treatment duration among the arms
- Insufficient delivery of treatment or insensitive scales to measure outcomes, leading to null results
A description of what was reported to have happened in each study will be provided, and a judgment on the risk of bias will be made for each domain, based on the following three categories:
- High risk of bias
- Low risk of bias
- Unclear risk of bias.
Two independent review authors will assess the risk of bias in selected studies. Degree of agreement between the two independent raters will be reported. Any disagreement will be resolved through discussion and in consultation with the principal investigators. Where necessary, the authors of the studies will be contacted for further information.
Strategy for data synthesis
We will generate descriptive statistics for trial and study population characteristics across all eligible trials, describing the types of comparisons and some important variables, either clinical or methodological (such as year of publication, age, severity of illness, sponsorship, clinical setting).
For each pair-wise comparison between treatments, the odds ratio will be calculated with a 95% CI. A standard, pair-wise meta-analysis will be conducted for each pair-wise comparison of treatments. We plan to use a random-effects model to incorporate the assumption that the different studies are estimating different, yet related, treatment effects (DerSimonian and Laird, 1986). Where there are <3 studies we will combine in a fixed effect analysis (Borenstein et al. , 2009, Mantel and Haenszel, 1959).
A prediction interval, which captures the uncertainty in the summary estimate, the estimate of the between study standard deviation (Tau) and the uncertainty in Tau (Higgins et al. , 2009), will also be estimated.
To ensure that the network is connected, a network diagram will be constructed for all the outcomes. Note that MTM is only possible for a connected set of treatments.
Random-effects MTM, taking into account the heterogeneity of treatment effects across studies will be conducted in a Bayesian framework using Markov Chain Monte Carlo methods in OpenBUGS 3.2.1 (http://www.openbugs.info/w/FrontPage). MTM combines direct and indirect evidence for any given pair of treatments, and takes into account correlation induced by multi-arm trial. Results for the comparative efficacy and acceptability are presented by OR estimates and 95% confidence intervals (approximately computed by posterior means and 95% probability intervals). We also evaluate the ranking of efficacy and tolerability using posterior probability which treatment is the most efficacious regimen, the second best, the third best, and so on. The goodness of fit of the model to the data will be measured by the posterior mean of the residual deviance. This is defined as the difference between the deviance for the fitted model and the deviance for the saturated model, where deviance measures the fit of the model to the data points using the likelihood function. We will examine leverage plots to help identify any specific data points (trial arms) that were fitting poorly in each model. A leverage plot displays the leverage (a measure of influence equal to the contribution of each trial arm to PD, the effective number of parameters) versus the signed, square root of the residual deviance (a measure of fit) for each data point. Points with a high leverage are influential, which means that they have a strong influence on the model parameters that generate their fitted values.
Analysis of subgroups or subsets
We will conduct subgroup analyses with the following variables.
1. Bipolar disorder subtype:
The efficacy and tolerability to pharmacological treatment will be different between bipolar I and II disorder.
2. Rapid-cycling bipolar disorder:
Rapid-cycling bipolar disorder will be specified when the patient with bipolar disorder experienced more than four episodes per year. It appears to have less response to pharmacological treatment.
Contact details for further information
3-1-1 Maidashi Higashi-ku Fukuoka 812-8582, Japan
Organisational affiliation of the review
Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University
Dr Tomofumi Miura, Kyushu University Professor Toshiaki Furukawa, Kyoto University Professor Shigenobu Kanba, Kyushu University Dr Hisashi Noma, The Institute of Statistical Mathematics Dr Shiro Tanaka, Kyoto University Dr Keisuke Motomura, Kyushu University Dr Hiroshi Mitsuyasu, Kyushu University Dr Satomi Katsuki, Kyushu University Dr Andrea Cipriani, University of Oxford and University of Verona Dr Sarah Stockton, University of Oxford Professor John Geddes, University of Oxford Dr Georgia Salanti, University of Ioannina
Anticipated or actual start date
26 July 2012
Anticipated completion date
30 September 2013
Conflicts of interest
TM has received honoraria for lecturing from Eli Lilly, GlaxoSmithKline, Meiji, Otsuka, Pfizer and Shionogi. The Japan Society for the Promotion of Science and the Japanese Ministry of Health, Labor and Welfare have funded his research
TAF has received honoraria for speaking at CME meetings sponsored by Asahi Kasei, Eli Lilly, GlaxoSmithKline, Mochida, MSD, Otsuka, Pfizer, Shionogi and Tanabe-Mitsubishi. He has received royalties from Igaku-Shoin, Seiwa-Shoten and Nihon Bunka Kagaku-sha. He is on advisory board for Sekisui Chemicals and Takeda Science Foundation. The Japanese Ministry of Education, Science, and Technology, the Japanese Ministry of Health, Labor and Welfare, and the Japan Foundation for Neuroscience and Mental Health have funded his research projects.
SK has received honoraria for lecturing from Asahi Kasei Pharma, Eli Lilly, GlaxoSmithKline, Otsuka, Pfizer and Shionogi. He is on advisory board for Astellas and Otsuka. The Japanese Minstry of Health, Labor and Welfare, Astellas, Dainippon Sumitomo Pharma, GlaxoSmithKline, Ono and Shionogi have funded his research projects.
KM has received honoraria for lecturing from Dainippon Sumitomo Pharma, GlaxoSmithKline, Mochida, Otsuka and Takeda. Ono has funded his research projects.
HM has received honoraria for lecturing from Meiji, Otsuka. The Japan Society for the Promotion of Science has funded his research projects.
Formal screening of search results against eligibility criteria
Risk of bias (quality) assessment
PROSPERO This information has been provided by the named contact for this review. CRD has accepted this information in good faith and registered the review in PROSPERO. CRD bears no responsibility or liability for the content of this registration record, any associated files or external websites.