Peer support for schizophrenia

  • Protocol
  • Intervention



This is the protocol for a review and there is no abstract. The objectives are as follows:

To assess the effects of peer-support interventions for people with schizophrenia or schizophrenia-like disorders in the community, compared to standard care and other psychosocial interventions.


Description of the condition

Schizophrenia is a chronic, disruptive, mental illness that frequently contributes to a wide variety of functional disabilities, especially within social and occupational domains (Harvey 2012). The worldwide estimate for the life-time prevalence of schizophrenia ranges from 1.4 to 4.6 per 1000 persons; the annual incidence rate lies between 0.16 and 0.42 per 1000 persons, with onset often occurring in adolescence and early adulthood (Jablensky 2000). The psychopathology of schizophrenia is often described in terms of the severity of positive (e.g. hallucinations and disorganised speech) and negative (e.g. blunted affect and social withdrawal) symptoms. While antipsychotic medications remain the core treatment for controlling the symptoms of schizophrenia, they are associated with a range of undesirable side effects on cardiovascular, endocrine and other bodily systems, resulting in poor treatment adherence (Kane 2010).
About 30% of people with schizophrenia have persistent and severe negative symptoms that tend to be resistant to medication. Termed ‘deficit syndrome', persistent negative symptoms are characterised by lack of initiative, interests and social fluency, poor verbal communication, concentration and loss of interpersonal function (Nasrallah 2011; Tandon 2009). Together with progressive deterioration in various cognitive functions (e.g. problems in working memory and information processing, reasoning and problem solving, and social cognition), there are considerable and wide varieties of functional impairments which can severely compromise overall psychosocial functioning, social integration and quality of life (Mohamed 2008). These factors may all eventually reduce treatment efficacy in individuals with schizophrenia.

The total societal costs of schizophrenia, including treatment, rehabilitation, community care services, and loss of productivity, were estimated at more than USD 60 billion per annum in the USA, UK and other developed countries in the 20th century (Mangalore 2007; Wu 2005). People with schizophrenia have severe social and occupational disability (30%) and are at higher risks of other mental (e.g. 25% to 30% have depression) and physical health (e.g. 20% to 25% have cardiovascular disease) problems (De Hert 2009), have a two-to-three-times higher all-cause mortality rate and are 12 times more likely to die by suicide than the general population (Goff 2005; Wildqust 2010).

Description of the intervention

Peer support is broadly defined as “a system of giving and receiving help founded on key principles of respect, shared responsibility, and mutual agreement of what is helpful” (Mead 2001). Dennis 2003 defined 'peer support' within a healthcare context as ".... the provision of emotional, appraisal and informational assistance by a created social network member who possesses experiential knowledge of a specific behaviour or stressor and similar characteristics as the target population" (Doull 2005). Peers can be referred to those people who share common characteristics with a specific individual or group, affiliating and empathising with and supporting each other to promote health and deal with life problems. The emphasis is on the idea that 'peers' are considered to be equal (Dennis 2003); in contrast to the traditional healthcare system of mental health services, which distinguishes between providers (i.e. trained professionals) and consumers (e.g. people with schizophrenia and families/friends), peer-support programmes are built on collaborative, mutual and equal partnerships of participants who share their experiences (or expertise) in different stages of recovery.  

Peer-support programmes for individuals with schizophrenia are mainly classified into three main categories, according to how they run the services and the roles played by their coordinators or facilitators (Ahmed 2012). One category of support programmes is led by peer specialists who are employed in the healthcare setting to advocate for the consumers, provide supportive services to the consumers and their families, and offer advice to the mental healthcare team. The other two categories are the mutual/self-help groups and the consumer-led services, respectively, which share similarities in operating the principles of peer support to service other consumers outside of formal healthcare settings, working with relatively fewer resources and less professional support from the mental healthcare system. The latter is the more structured programme in terms of its system, structure and group sessions, and involves itself more with leadership of the coordinators and facilitators, or both who are often either volunteers and committed or hired to be the service providers. However, all categories of peer-support programmes emphasise interactive mutual peer or social learning. In response to individual groups’ and group members’ needs, their content can range from psychoeducation about schizophrenia and its symptom management, medication adherence, stress reduction and coping strategies, to problem solving approaches, and the strengthening of family and community support resources, as well as vocational and social skills training (Chien 2009).

How the intervention might work

Peer support has become an increasingly important strategy in healthcare systems that are encountering limited manpower and resources on one hand and, on the other, continuously increasing costs of managing complex and chronic illnesses such as severe mental disorders. Peer support has been widely used to improve physical and psychosocial health and enhance behavioural change and self-care in diverse chronic illness conditions, as well as in population groups in need of support (Cheah 2001). A peer-support programme can provide a platform where fellow patients and those already recovered from schizophrenia, or another mental illness, can share their individual experiences of the illness and management strategies in everyday life in a way that is not commonly offered in traditional healthcare settings where mental health professionals may often dominate services. In contrast to traditional healthcare settings, often stigmatised by the general public, the environment of a peer-support group fosters a sense of emotional support, information exchange, companionship, and reassurance and appraisals among group members (Ahmed 2012, Dennis 2003). Through interpersonal sharing, modelling and assistance within or outside of group sessions, it is believed that these supportive strategies can effectively combat hopelessness and behavioural problems relating to schizophrenia, and empower participants to continue treatment and resume key roles in real life (Chien 2009; Davidson 1999). However, research has shown inconsistent findings on whether social or peer support enhances self-care ability and medication adherence in people with mental illness (Pistrang 2008) and other chronic illnesses such as diabetic mellitus (Toljamo 2001). 

While most peer-support groups mainly target those who are in the early stages of recovery, the benefits of these group programmes are not limited only to those who receive the peer-support service, but also extend to those who provide peer support to others (Miyamoto 2012). The peer-support providers who are assigned the roles of coordinator or facilitator of the group can successfully rebuild their self-efficacy through having the chance to serve other people with similar conditions. They may even collaborate with professionals to deliver appropriate services to other group members in need. Through active participation in service provision, they themselves increase their knowledge of disease management and enhance various skills that are important to daily functioning (Arnstein 2002). 

Why it is important to do this review

Recent systematic reviews and practice guidelines have recommended that, in adjunction to psychopharmacological treatment, psychosocial interventions designed to support people with schizophrenia and their families should also be used to improve individual's rehabilitation, reintegration into the community and recovery from the illness (Pharoah 2010, NICE 2009). There is now an increasing body of evidence concerning the effects of a range of psychosocial interventions for schizophrenia, including psychoeducation (Xia 2011), cognitive-behavioural therapy (Turkington 2004, Morrison 2009) and family intervention (Pharoah 2010). While psychosocial intervention have indicated significant positive effects on reducing relapse and readmission rates, and enhancing medication compliance, most have not demonstrated consistent and conclusive results in improving other psychosocial health conditions of people with schizophrenia. Therefore, the design or testing of alternative approaches to psychosocial intervention for these individuals should be considered. Guided by the consumer movement and recovery model in mental health care, peer support is one such approach to psychosocial intervention that places emphasis on promoting the overall wellness and empowerment of people with schizophrenia through establishing partnerships between those with the condition throughout the whole journey of recovery (Ahmed 2012). While there are few controlled trials investigating physical health outcomes after peer-support group interventions, the results of a few quasi-experimental and cohort studies have indicated that peer support is associated with significant improvements in body weight, level of physical activity and general physical health in individuals with schizophrenia and other severe mental illnesses (Davidson 1999; Lawn 2008), and diabetes (Dale 2012) and other chronic illnesses (Rowe 2007; Stice 2004).

With its emphasis on the experiences of people with schizophrenia, their needs and perspectives in treatment planning, peer-support programmes have led to growing interest in the role that those who are experiencing difficulties with recovery can play in enlightening the social reintegration and enhancing the rehabilitation process of others with similar mental health problems (Ahmed 2012). Recently, the number of peer-support programmes for schizophrenia care has increased rapidly in developed countries such as the USA and Canada. Nevertheless, there is no systematic review on the impetus for this alternative treatment approach and its effects on mental condition and relapse, medication adherence, and a wide variety of outcomes such as psychosocial and occupational functioning, social skills, self-efficacy, overall wellness and quality of life in people with schizophrenia (Miyamoto 2012).

This review focuses on peer-support programmes and their use varies across cultures. There are no systematic reviews on this topic in the area of schizophrenia; and only one literature review has been published on the effects of support groups for various kinds of mental health problems (Pistrang 2008). The findings of this review will enhance our knowledge of the effectiveness of peer-support interventions and the various models for the delivery of peer-support interventions across cultures. The costs and benefits of these programmes can then be systematically evaluated.


To assess the effects of peer-support interventions for people with schizophrenia or schizophrenia-like disorders in the community, compared to standard care and other psychosocial interventions.


Criteria for considering studies for this review

Types of studies

All relevant randomised controlled trials (RCTs), including cluster randomised trials, that have evaluated the effects of peer-support interventions on at least one of the outcomes listed below (refer to ‘Types of outcome measures’) will be included. Studies that do not include a control or comparison group will be excluded. Where the participants were given additional types of treatments within peer support programme, we will only include data if the adjunct treatment was applied equally to all study groups and it was only peer support that was randomised and allocated to the treatment or intervention group(s).

If a trial is described as ’double blind’ but only implies randomisation, we will include such trials in a sensitivity analysis (see 'Sensitivity analysis'). If their inclusion does not result in a substantive difference to the main findings, they will remain in the analyses. If their inclusion results in statistically significant difference, we will not add the data from these lower-quality studies to the results of the higher-quality trials, but will present such data within a subcategory. We will exclude quasi-randomised studies, such as those allocating participants by alternate days of the week. Where people are given additional treatments within a peer-support programme, we will include data only if the adjunct treatment is evenly distributed between groups and it is only the peer support that is randomised.

Types of participants

We will require that a majority of participants should be within the adult age range and be diagnosed with schizophrenia, schizophrenia-like disorders, bipolar disorder or serious affective disorders, preferably as defined by National Institute of Mental Health (NIMH) criteria (NIMH 1987). If a trial includes participants with a range of serious mental illnesses we will include it only if a majority have schizophrenia; we will not include trials that randomise only people with bipolar or serious affective disorders. We will not consider substance abuse to be serious mental illnesses in its own right; however we feel that studies should remain eligible if they deal with people with dual diagnoses (i.e. those with serious mental illnesses plus substance abuse). We will not include studies focusing on dementia, personality disorder or mental retardation, as they are not covered by our definition of serious mental illnesses. Despite the fact that personality disorder is now included in the NIMH definition of serious mental illnesses, we plan to exclude this from our review on the basis that the diagnosis of personality disorders has low interrater reliability (Zimmerman 1994), the duration of treatment can be assessed much more precisely than duration of illness (Schinnar 1990), and that insufficient information is given on how to diagnose disability criterion in both the original NIMH (NIMH 1987) definition and in the further work of Schinnar 1990.

Types of interventions

We will include any intervention described as 'peer support' for people with schizophrenia.
1. Peer support

We define a 'peer' as someone selected to provide support because they have similar or relevant health experience (Dale 2008).

We will exclude studies where the effects of the peer support element cannot be isolated. 

1. Other defined intervention

Any psychosocial intervention or any supportive intervention that does not involve a 'peer' individual/group(s).

2. Standard care

Care that would normally be received in the area where the trial is taking place.

Types of outcome measures

Outcomes will be divided into short term (one month), medium term (one or more to six months) and long term (more than six months).

Primary outcomes
1. Service outcomes

1.1 Specialist community services (i.e. early interventions, assertive outreach and crisis teams)
1.2 Hospital admission
1.3 Time to hospitalisation

2. Global state

2.1 Relapse
2.2 Time to relapse

Secondary outcomes
1. Service outcomes

1.1 Duration of hospital stay
1.2 Number of admissions
1.3 Clinically important engagement with services

2. Global state

2.1 Change in global state – improved/not improved
2.2 Average change or endpoint score in global state
2.3 Leaving the study early
2.4 Compliance with medication

3. Mental state and behaviour

3.1 Change in general mental state
3.2 Positive symptoms
3.3 Negative symptoms
3.4 Changes in general and specific behaviour
3.5 Average change or endpoint score

4. Psychosocial functioning

4.1 Change in general functioning
4.2 Average change or endpoint score in general functioning
4.3 Social and life skills
4.4 Employment status and work related activities
4.5 Independent living
4.6 Imprisonment

5. Peer outcomes

5.1 Impact on the service user and peer supporter (e.g. anxiety and perceived social support)
5.2 Coping ability/self-efficacy of service user and peer supporter
5.3 Expressed emotion of family, peer supporter or both
5.4 Quality of life for service user and peer supporter
5.5 Satisfaction with care for service user and peer supporter

6. Adverse events

6.1 Suicide and all causes of mortality
6.2 Other adverse events/effects

7. Economic outcomes

7.1 Cost of care
7.2 Direct costs
7.3 Indirect costs

8. 'Summary of findings' table

We will use the GRADE approach to interpret findings (Schünemann 2008) and will use the GRADE profiler (GRADEPRO) to import data from RevMan 5.1 (Review Manager) to create 'Summary of findings' tables. These tables will provide outcome-specific information concerning the overall quality of evidence from each included study in the comparison, the magnitude of effect of the interventions examined and the sum of available data on all outcomes we rated as important to the care of people with schizophrenia and to decision making. We aim to select the following main outcomes for inclusion in the 'Summary of findings' table:

  • Service outcomes (hospital admission, days in hospital)

  • Global state - important clinical response

  • Peer outcomes - quality of life for service user and peer supporter- improved to an important extent

  • Adverse events - any important adverse event

  • Economic (increased cost to society).

Qualitative peer-support studies may not meet the inclusion criteria for this review. We propose to present these qualitative study findings in a brief summary table.

Search methods for identification of studies

Electronic searches

1. Cochrane Schizophrenia Group's Trials Register

The Trials Search Co-ordinator will search the Cochrane Schizophrenia Group’s Trials Register applying the following search strategy based on the terms recommended by Doull 2005:

peer*:ti or "self help":ti.or (social NEXT (support* or network* advis* or advice* or counsel*)):ti or peer*:ab or "self help":ab or (social NEXT (support* or network* advis* or advice* or counsel*)): ab

The Cochrane Schizophrenia Group’s Trials Register is compiled by systematic searches of major databases and their monthly updates, handsearches and conference proceedings (see the Cochrane Schizophrenia Group Module).

Searching other resources

1. Reference searching

We will inspect references of all included studies for further relevant studies.

2. Personal contact

We will contact the first author of each included study for information regarding unpublished trials.

Data collection and analysis

Selection of studies

Two review authors (SL, WTC) will screen the results of the electronic search. WTC will inspect all abstracts of studies identified through screening and identify potentially relevant reports. Once identified, to ensure reliability, WTC and AVC will inspect a random sample of these abstracts, comprising 10% of the total. Where disagreement occurs, we will resolve this by discussion, and where there is still doubt, we will acquire the full article for further inspection. We will then request the full articles of relevant reports for reassessment and carefully inspect them for a final decision on inclusion (see the Cochrane Schizophrenia Group Module). In turn, WTC and SL will inspect all full reports and independently decide whether they meet inclusion criteria. We will not be blinded to the names of the authors, institutions or journal of publication. Where difficulties or disputes arise, we will ask author AVC for help; if it is impossible to decide, we will add these studies to those awaiting assessment and contact the authors of the papers for clarification.

Data extraction and management

1. Extraction 

Review authors will independently extract data from included studies. Again, we will discuss any disagreement, document our decisions and, if necessary, we will contact the authors of studies for clarification. We will extract data presented only in graphs and figures whenever possible, but include such data only if two authors independently reach the same result. We will attempt to contact authors through an open-ended request in order to obtain any missing information or for clarification whenever necessary. Where possible, we will extract data relevant to each component centre of multicentre studies separately (see the Cochrane Schizophrenia Group Module).

2. Management
2.1 Forms

We will extract data into standard, simple forms.

2.2 Scale-derived data

We will include continuous data from rating scales only if:

  • the psychometric properties of the measuring instrument have been described in a peer-reviewed journal (Marshall 2000); and

  • the measuring instrument has not been written or modified by one of the trialists for that particular trial.

Ideally, the measuring instrument should either be i. a self-report or ii. completed by an independent rater or relative (not the therapist). We realise that this is not often reported clearly; we will note if this is the case or not in the 'Description of studies' section.

2.3 Endpoint versus change data

There are advantages of both endpoint and change data. Change data can remove a component of between-person variability from the analysis. On the other hand, calculation of change needs two assessments (baseline and endpoint), which can be difficult in unstable and difficult-to-measure conditions such as schizophrenia. We have decided to primarily use endpoint data, and use change data only if the former are not available. Endpoint and change data will be combined in the analysis as we will use mean differences (MD) rather than standardised mean differences throughout (Higgins 2011).

2.4 Skewed data

Continuous data on clinical and social outcomes are often not normally distributed. To avoid the pitfall of applying parametric tests to non-parametric data, we aim to apply the following standards to all data before inclusion:

  • standard deviations (SD) and means should be reported in the paper or obtainable from the authors;

  • when a scale starts from the finite number zero, the SD, when multiplied by two, should be less than the mean (as otherwise the mean is unlikely to be an appropriate measure of the centre of the distribution (Altman 1996);

  • if a scale starts from a positive value (such as the Positive and Negative Syndrome Scale (PANSS) (Kay 1986), which can have values from 30 to 210), the calculation described above will be modified to take the scale starting point into account. In these cases, skew is present if 2 SD > (S − S min), where S is the mean score and 'S min' is the minimum score.

Endpoint scores on scales often have a finite start and endpoint, and these rules can be applied. Skewed data pose less of a problem when looking at means if the sample size is large (> 200) and we will enter such data into the syntheses. We will present skewed endpoint data from studies of fewer than 200 participants as 'other data' within the data and analyses section rather than enter such data into statistical analyses.

When continuous data are presented on a scale that includes a possibility of negative values (such as change data), it is difficult to tell whether data are skewed or not. We will present and enter change data into analyses.

2.5 Common measure

To facilitate comparison between trials, we intend to convert variables that can be reported in different metrics, such as days in hospital (mean days per year, per week or per month) to a common metric (e.g. mean days per month).

2.6 Conversion of continuous to binary

Where possible, efforts will be made to convert outcome measures to dichotomous data. This can be done by identifying cut-off points on rating scales and dividing participants accordingly into 'clinically improved' or 'not clinically improved'. It is generally assumed that if there is a 50% reduction in a scale-derived score such as the Brief Psychiatric Rating Scale (Overall 1962) or the PANSS (Kay 1986), this could be considered a clinically significant response (Leucht 2005, Leucht 2005a). If data based on these thresholds are not available, we will use the primary cut-off presented by the original authors.

2.7 Direction of graphs

Where possible, we will enter data in such a way that the area to the left of the line of no effect indicates a favourable outcome for peer support. Where keeping to this makes it impossible to avoid outcome titles with clumsy double-negatives (e.g. 'not improved') we will report data in such a way that the area to the left of the line indicates an unfavourable outcome. This will be noted in the relevant graphs.

Assessment of risk of bias in included studies

Review authors SL and AVC will work independently to assess risk of bias by using criteria described in the Cochrane Handbook for Systematic Reviews of Interventions to assess trial quality (Higgins 2011). This set of criteria is based on evidence of associations between an overestimation of effect and high risk of bias in an article, such as due to sequence generation, allocation concealment, blinding, incomplete outcome data and selective reporting. If the raters disagree, the final rating will be made by consensus, with the involvement of another member of the review group. Where inadequate details of randomisation and other characteristics of trials are provided, authors of the studies will be contacted in order to obtain further information. Non-concurrence in quality assessment will be reported but, if disputes arise as to which category a trial is to be allocated to, again resolution will be made by discussion. The level of risk of bias will be noted in both the text of the review and in the 'Summary of findings’ table.

Measures of treatment effect

1. Binary data

For binary outcomes we will calculate a standard estimation of the risk ratio (RR) and its 95% confidence interval (CI). It has been shown that the RR is more intuitive (Boissel 1999) than the odds ratio, and that odds ratios tend to be interpreted as RR by clinicians (Deeks 2000). The number needed to treat for an additional harmful outcome statistic with its CI is intuitively attractive to clinicians but is problematic both in its accurate calculation in meta-analyses and its interpretation (Hutton 2009). For binary data presented in the 'Summary of findings' table/s, we will calculate illustrative comparative risks where possible.

2. Continuous data

For continuous outcomes, we will estimate MD between groups. We prefer not to calculate effect size measures (standardised mean difference). However, if scales of very considerable similarity are used, we will presume there is a small difference in measurement, and we will calculate effect size and transform the effect back to the units of one or more of the specific instruments.

Unit of analysis issues

1. Cluster trials

Studies increasingly employ 'cluster randomisation' (such as randomisation by clinician or practice), but analysis and pooling of clustered data pose problems. First, authors often fail to account for intraclass correlation in clustered studies, leading to a 'unit of analysis' error (Divine 1992), whereby P values are spuriously low, CI unduly narrow and statistical significance overestimated. This causes type I errors (Bland 1997, Gulliford 1999).

Where clustering is not accounted for in primary studies, we will present data in a table, using a symbol (*) to indicate the presence of a probable unit of analysis error. In subsequent versions of this review we will seek to contact first authors of studies to obtain intraclass correlation coefficients (ICC) for their clustered data and to adjust for this using accepted methods (Gulliford 1999). Where clustering has been incorporated into the analysis of primary studies, we will present these data as if from a non-cluster randomised study, but adjusting for the clustering effect.

We have sought statistical advice and have been advised that the binary data as presented in a report should be divided by a 'design effect'. This is calculated using the mean number of participants per cluster (m) and the ICC) (design effect = 1 + (m − 1) * ICC) (Donner 2002). If the ICC is not reported, it will be assumed to be 0.1 (Ukoumunne 1999).

If cluster studies have been appropriately analysed, taking into account ICC and relevant data documented in the report, synthesis with other studies will be possible using the generic inverse variance technique.

2. Cross-over trials

A major concern of cross-over trials is the carry-over effect. This occurs if an effect (e.g. pharmacological, physiological or psychological) of the treatment in the first phase is carried over to the second phase. As a consequence, on entry to the second phase, participants can differ systematically from their initial state in spite of a washout phase. For the same reason cross-over trials are not appropriate if the condition of interest is unstable (Elbourne 2002). As both effects are very likely in severe mental illness, we will use data only from the first phase of cross-over studies.

3. Studies with multiple treatment groups

Where a study involves more than two treatment arms, the additional treatment arms will be presented in comparisons, if relevant. If data are binary, these will be simply added and combined within the two-by-two table. If data are continuous, we will combine data following the formula in Section  (Combining groups) of the Cochrane Handbook for Systemic reviews of Interventions (Higgins 2011). Where the additional treatment arms are not relevant, we will not use these data.

Dealing with missing data

1. Overall loss of credibility

At some degree of loss of follow up, data must lose credibility (Xia 2009). We intend that, for any particular outcome, should more than 50% of data be unaccounted for, we will not reproduce these data or use them within analyses. If, however, more than 50% of data in one arm of a study are lost, but the total loss is less than 50%, we will address this within the 'Summary of findings' table/s by down-rating quality. Finally, we will also downgrade quality within the 'Summary of findings' table/s should data loss be 25% to 50% in total.

2. Binary

In cases where the attrition for a binary outcome is between 0% and 50%, and where these data are not clearly described, we will present data on a 'once-randomised-always-analyse' basis (an intention-to-treat analysis). Participants leaving the study early are all assumed to have the same rates of negative outcome as those who completed, with the exception of the outcome of death and adverse effects. For these outcomes, the rate of those who stay in the study - in that particular arm of the trial - will be used for those who did not. We will undertake a sensitivity analysis testing how prone the primary outcomes are to change when data from only people who complete the study to that point are compared to the intention-to-treat analysis using the above assumptions.

3. Continuous
3.1 Attrition

In cases where the attrition for a continuous outcome is between 0% and 50%, and data only from people who complete the study to that point are reported, we will reproduce these.

3.2 Standard deviations

If SD are not reported, we will first try to obtain the missing values from the authors. If not available, where there are missing measures of variance for continuous data, but an exact standard error (SE) and CI available for group means, and either a P value or 't' value available for differences in mean, we will calculate SD according to the rules described in the Cochrane Handbook for Systemic reviews of Interventions (Higgins 2011). When only the SE is reported, SD will be calculated using the formula SD = SE * square root (n). Chapters 7.7.3 and 16.1.3 of the Cochrane Handbook for Systemic reviews of Interventions (Higgins 2011) present detailed formulae for estimating SD from P values, t or F values, CI, ranges or other statistics. If these formulae do not apply, we will calculate the SD according to a validated imputation method which is based on the SD of the other included studies (Furukawa 2006). Although some of these imputation strategies can introduce error, the alternative would be to exclude a given study’s outcome and thus to lose information. We nevertheless will examine the validity of the imputations in a sensitivity analysis excluding imputed values.

3.3 Last observation carried forward

We anticipate that in some studies the method of last observation carried forward (LOCF) will be employed within the study report. As with all methods of imputation to deal with missing data, LOCF introduces uncertainty about the reliability of the results (Leucht 2007). Therefore, where LOCF data have been used in the trial, if fewer than 50% of the data have been assumed, we will present and use these data, and indicate that they are the product of LOCF assumptions.

Assessment of heterogeneity

1. Clinical heterogeneity

We will consider all included studies initially, without seeing comparative data, to judge clinical heterogeneity. We will simply inspect all studies for clearly outlying people or situations that we had not predicted would arise. When such situations or participant groups arise, these will be fully discussed.

2. Methodological heterogeneity

We will consider all included studies initially, without seeing comparative data, to judge methodological heterogeneity. We will simply inspect all studies for clearly outlying methods that we had not predicted would arise. When such methodological outliers arise, these will be fully discussed.

3. Statistical heterogeneity
3.1 Visual inspection

We will visually inspect graphs to investigate the possibility of statistical heterogeneity.

3.2 Employing the I2 statistic

Heterogeneity between studies will be investigated by considering the I2 statistic method alongside the Chi2 statistic P value. The I2 statistic provides an estimate of the percentage of inconsistency thought to be due to chance (Higgins 2003). The importance of the observed value of the I2 statistic depends on: i. magnitude and direction of effects; and ii. strength of evidence for heterogeneity (e.g. P value from the Chi2 test, or a CI for the I2 statistic). I2 statistic estimates greater than or equal to around 50%, accompanied by a statistically significant Chi2 statistic, will be interpreted as evidence of substantial levels of heterogeneity (Section 9.5.2 - Higgins 2011). When substantial levels of heterogeneity are found in the primary outcome, we will explore reasons for heterogeneity (see 'Subgroup analysis and investigation of heterogeneity').

Assessment of reporting biases

1. Protocol versus full study

Reporting biases arise when the dissemination of research findings is influenced by the nature and direction of results. These are described in Section 10.1 of the Cochrane Handbook for Systemic reviews of Interventions (Higgins 2011). We will try to locate protocols of included randomised trials. If the protocol is available, outcomes in the protocol and in the published report will be compared. If the protocol is not available, outcomes listed in the methods section of the trial report will be compared with actually reported results.

2. Funnel plot

Reporting biases arise when the dissemination of research findings is influenced by the nature and direction of results (Egger 1997). These are again described in Section 10 of the Cochrane Handbook for Systemic reviews of Interventions (Higgins 2011). We are aware that funnel plots may be useful in investigating reporting biases but are of limited power to detect small-study effects. We will not use funnel plots for outcomes where there are ten or fewer studies, or where all studies are of similar sizes. In other cases, where funnel plots are possible, we will seek statistical advice in their interpretation.

Data synthesis

We understand that there is no closed argument regarding a preference for the use of fixed-effect or random-effects models. The random-effects method incorporates an assumption that the different studies are estimating different yet related intervention effects. To us, this often seems to be true and the random-effects model takes into account differences between studies even if there is no statistically significant heterogeneity. There is, however, a disadvantage to the random-effects model as it puts added weight onto small studies, which are often those most biased. Depending on the direction of effect, these studies can either inflate or deflate the effect size. We have chosen a random-effects model for all analyses. The reader will, however, be able to choose to inspect the data using afixed-effect model.

Subgroup analysis and investigation of heterogeneity

1. Subgroup analyses

We anticipate no subgroup analyses.

1.2 Clinical state, stage or problem

We propose to undertake this review and provide an overview of the effects of peer support for people with schizophrenia in general. In addition, however, we will try to report data on subgroups of people in the same clinical state and stage, and with similar problems.

2. Investigation of heterogeneity

If inconsistency is high, this will be reported. First, we will investigate whether data have been entered correctly. Second, if data are correct, the graph will be visually inspected, and outlying studies will be successively removed to see whether homogeneity is restored. For this review, we decided that, should this occur with data contributing to the summary finding of no more than around 10% of the total weighting, data will be presented. If not, data will not be pooled and issues will be discussed. We know of no supporting research for this 10% cut-off but are investigating the use of prediction intervals as an alternative to this unsatisfactory state.

When unanticipated clinical or methodological heterogeneity is obvious, we will simply state hypotheses regarding these for future reviews or versions of this review. We do not anticipate undertaking analyses relating to these.

Sensitivity analysis

1. Implication of randomisation

We aim to include trials in a sensitivity analysis if they are described in some way as to imply randomisation. For the primary outcomes, we will include these studies; and if there is no substantive difference when the implied randomised studies are added to those with a better description of randomisation, all data will be employed from these studies.

2. Assumptions for lost binary data

Where assumptions have to be made regarding people lost to follow up (see 'Dealing with missing data') we will compare the findings of the primary outcomes when we implement our assumption/s, or when we use data only from people who completed the study to that point. If there is a substantial difference, we will report and discuss the results but will continue to employ our assumption.

Where assumptions have to be made regarding missing SD data (see 'Dealing with missing data'), we will compare the findings of the primary outcomes when we implement our assumption/s, or when we use data only from people who complete the study to that point. A sensitivity analysis will be undertaken to test how prone the results are to change when completer-only data are compared with the imputed data using the above assumption. If there is a substantial difference, we will report and discuss the results but will continue to employ our assumption.

3. Risk of bias

We will analyse the effects of excluding trials that are judged to be at high risk of bias across one or more of the domains of randomisation (implied as randomised with no further details available), allocation concealment, blinding and outcome reporting for the meta-analysis of the primary outcome. If the exclusion of trials at high risk of bias does not substantially alter the direction of effect or the precision of the effect estimates, then data from these trials will be included in the analysis.

4. Imputed values

We will also undertake a sensitivity analysis to assess the effects of including data from trials where we used imputed values for the ICC in calculating the design effect in cluster randomised trials.

If substantial differences are noted in the direction or precision of effect estimates in any of the sensitivity analyses listed above, we will not pool data from the excluded trials with the other trials contributing to the outcome, but will present them separately.

5. Fixed and random effects

All data will be synthesised using a random-effects model. However, we will also synthesise data for the primary outcome using a fixed-effect model to evaluate whether the greater weights assigned to larger trials with greater event rates alter the significance of the results, compared with the more evenly distributed weights in the random-effects model.


The Cochrane Schizophrenia Group Editorial Base in Nottingham produces and maintains standard text for use in the Methods section of their reviews. We have used this text as the basis of what appears here and adapted it as required.

We would like to thank John Lally for peer reviewing this protocol.

Contributions of authors

Wai Tong Chien - project initiation, primary reviewer, protocol writing.

Steve Lui - primary reviewer, helped with writing the protocol.

Andrew Clifton - primary reviewer, helped with writing the protocol.

Declarations of interest


Sources of support

Internal sources

  • University of Huddersfield, UK.

  • The Hong Kong Polytechnic University, Hong Kong SAR, China.

External sources

  • No sources of support supplied