Meta‐cognitive training for schizophrenia

Objectives

This is a protocol for a Cochrane Review (intervention). The objectives are as follows:

To evaluate the benefits and harms of MCT and MCT+ for people with schizophrenia or related disorders compared to standard care or other psychosocial interventions.

Background

Cognitive behavioural therapy (CBT) has been suggested to be a useful add‐on to medication for schizophrenia, particularly for people who show active symptoms (Laws 2018; Turkington 2006). However, the evidence is far from conclusive (Jones 2018; Laws 2018). Meta‐cognitive training (MCT) has been developed as an enhancement of CBT in the treatment of schizophrenia. Its efficacy has been mixed. Some meta‐analyses showed that it can be effective on psychotic symptoms (Eichner 2016; Liu 2018; Philipp 2019), cognitive biases (Sauvé 2020), and insight (Lopez‐Morinigo 2020). One meta‐analysis reported no clinically significant effect of MCT (van Oosterhout 2016). Also, one network meta‐analysis showed that MCT has no effect on positive symptoms (Bighelli 2018). However, MCT may be effective, like other psychological therapies, on functioning in people with schizophrenia (Bighelli 2022). MCT is usually delivered in group format. However, an individualised form of MCT, called MCT+, and delivered on a one‐to‐one basis, has been developed that may complement the delivery of MCT (Moritz 2013). MCT+ also targets negative symptoms. The National Institute for Health and Care Excellence (NICE) in the UK has recommended that cognitive behavioural therapy for psychosis (CBTp) should be offered to all people with schizophrenia (NICE 2014). MCT represents an enhancement of CBTp and may offer advantages to the standard CBT model. Initially MCT was offered in a group‐only setting and mostly targeted positive symptoms and cognitive biases.

Description of the condition

Schizophrenia is a disabling psychotic disorder with a point prevalence of between 2.8 and 4.5 per 1000 population (Charlson 2018; Tandon 2008), and an annual incidence of 15 per 100,000 population (Tandon 2008). It is characterised by delusions, hallucinations, negative symptoms and disorganised behaviour (APA 2013). Although prognosis can be debilitating for some people, with a reduction in life expectancy (McCutcheon 2020), it is possible to achieve recovery (van Os 2009). Medication remains the mainstay of treatment (McCutcheon 2020; van Os 2009). However, not all patients respond well to medication and a significant number of patients remain symptomatic (McCutcheon 2020; Mueser 2004). Psychosocial interventions (individual or group) such as CBT, mindfulness‐based therapy, psychoeducation, and family therapy, can also be valid tools to improve symptoms and quality of life of people with schizophrenia (Bighelli 2021; Bighelli 2022; Guaiana 2022; Jones 2018; Lutgens 2017; Mc Glanaghy 2021).

The impact of schizophrenia on public health is considerably high. Schizophrenia contributes to 13 million years of life lived with disability to the burden of disease globally (Charlson 2018), in spite of having a low prevalence (about 1%: Stilo 2010). The peak incidence for schizophrenia in males is between 20 and 24 years of age, and in females is between 29 and 32 years of age (Stilo 2010).

Description of the intervention

MCT is a psychotherapy modality that has been developed from the CBTp model. It was developed by Steffen Moritz and collaborators in 2005 (Moritz 2010a). It is a manualised intervention that can be delivered in a group (called MCT) or individual (called MCT+) format (Moritz 2013). The manual has been translated into 15 languages and is available online for a fee from the developer's internet site (clinical-neuropsychology.de/metacognitive_training-psychosis/). MCT is based on the assumption that cognitive biases (e.g. jumping to conclusions) are an important part of the formation and maintenance of delusions in schizophrenia (Moritz 2010b). The developers of MCT chose the word "metacognitive" to describe their therapy approach because they wanted to emphasise the "thinking about one's thinking" aspect of their treatment (Moritz 2010a). MCT includes elements of cognitive remediation therapy (CRT) to target cognitive function, CBTp to target thinking patterns, and psychoeducation, to inform the patient about the nature of their illness. An additional element of MCT is the focus on social cognitive aspects (Moritz 2010a). The approach of MCT for dealing with psychotic symptoms is based on working on cognitive processes first and then proceeding to the symptom level, unlike standard CBTp (Moritz 2013). MCT is composed of eight modules (Moritz 2013; table from Moritz 2013):

MCT module	Exercises (examples)	Learning aim
1. Attribution (mono‐causal inferences)	Different causes (self, others, circumstances) for complex positive and negative events must be contemplated (e.g. you fail an examination).	Patients are taught to consider various causes instead of converging on mono‐causal explanations. The negative consequences of a self‐serving attribution are highlighted.
2. Jumping to conclusions I	Fragmented pictures are shown that eventually depict objects. Hasty decisions often lead to errors and new evidence discourages certain alternatives.	The disadvantage of jumping to conclusions is stressed.
3. Changing beliefs (bias against disconfirmatory evidence)	Cartoon sequences are shown in backwards order, which increasingly disambiguate a complex scenario. After each (new) picture, the plausibility of 4 interpretations has to be re‐rated. On some pictures, patients are 'led up the garden path' (misled).	Patients learn to withhold strong judgements until sufficient evidence has been collected, and to consider counter‐arguments and alternative views.
4. To empathise I	Pictures of human faces are presented. The group should guess what the depicted character(s) may feel. The correct solution often violates a first intuition.	It is demonstrated that facial expressions can be misleading for social decision‐making and that response confidence needs to be attenuated in case of scarce evidence.
5. Memory (overconfidence in errors)	Complex scenes (e.g. beach) are displayed prompting high‐confident false memories for typical items (e.g. memorising a ball, although it has not been presented).	The constructive nature of memory is emphasised. Patients are encouraged to decrease confidence when evidence is lacking.
6. To empathise II (theory of mind second order)	The perspective of 1 protagonist must be considered, which involves discounting knowledge available to the observer but not available to the protagonist.	Patients are taught that social situations often lack a clear‐cut solution and that multiple pieces of evidence have to be contemplated before a definite decision can be reached.
7. Jumping to conclusions II	Paintings are displayed, for which the correct title must be deduced from 4 response options. Many pictures elicit false responses.	The disadvantages of hasty decision‐making are emphasised.
8. Mood and self‐esteem	Typical depressive cognitive patterns are presented (e.g. overgeneralisation), and the group is asked to come up with more constructive and positive ones.	Strategies for raising and maintaining self‐esteem are conveyed.

MCT+ is the individualised format of MCT (Moritz 2013). In addition to the components described above, MCT+ also addresses negative symptoms and can lead to the treatment of individual symptoms through the generation of an illness model and a recovery plan (Moritz 2013).

How the intervention might work

MCT works by addressing the cognitive biases associated with positive symptoms of schizophrenia. It is an attempt to apply the knowledge on delusion formation in schizophrenia to a therapeutic approach. A particular cognitive distortion, jumping to conclusions, has been considered an important driver for the development of delusions, according to the MCT model. People with schizophrenia tend to "terminate data collection prematurely and weigh evidence insufficiently when arriving at strong conclusions." Jumping to conclusions seems to be a basic cognitive distortion in schizophrenia spectrum disorders, not only for delusion formation but also for other non‐delusional symptoms. This is also associated with a bias against disconfirmatory evidence. According to Moritz, jumping to conclusions is a precursor of psychosis. Another deficit that has been postulated by the MCT model is that people with schizophrenia tend to consider other people rather than themselves as the main cause for events. Furthermore, people with schizophrenia show some cognitive impairment such as deficits in memory recollection and social cognition. According to MCT model, the biases described above are beyond conscious reflection. MCT aims to explain those cognitive biases and their link to schizophrenia and delusions, and subsequently to demonstrate the negative consequences of such biases. The participant is encouraged to counter the biases by using personal examples. The way MCT works is to challenge psychotic symptoms, in particular delusions, by "indirectly altering the meta‐cognitive infrastructure that is thought to underlie psychosis," what Steffen Moritz call a "backdoor approach." This approach is different from the classic CBTp model where psychotic symptoms are challenged more directly (front‐door approach) (Moritz 2007).

Why it is important to do this review

Previous meta‐analyses have been published on MCT in people with schizophrenia, in 2016 (van Oosterhout 2016) and in 2018 (Liu 2018). Our meta‐analyses will update the studies and expand the search to all countries where MCT has been studied, for example China (Wang 2022). Moreover, there is a need to systematically assess the efficacy and tolerability of MCT+, which we will consider.

Objectives

To evaluate the benefits and harms of MCT and MCT+ for people with schizophrenia or related disorders compared to standard care or other psychosocial interventions.

Methods

Criteria for considering studies for this review

Types of studies

We will consider all relevant randomised controlled trials (RCTs). We will include RCTs, cluster‐RCTs, and cross‐over RCTs meeting our inclusion criteria and reporting data included in qualitative or quantitative analysis. We will exclude quasi‐randomised studies, such as those that allocate intervention by alternate days of the week. Where people are given additional treatments as well as MCT and MCT+, we will only include data if the adjunct treatment is evenly distributed between groups, and it is only the MCT or MCT+ that is randomised.

Where studies have multiple publications, we will collate the reports of the same study so that each study, rather than each report, is the unit of interest for the review, and such studies have a single identifier with multiple references.

We plan to include studies reported in full‐text, published as abstract only, and reported in conference proceedings. If we include studies reported in conference proceedings, we will ensure they include details of the participants, inclusion criteria, exclusion criteria, measurements used, and complete outcome data. We will contact study authors if details are missing.

Types of participants

We will include adults (aged 18 years and above) with schizophrenia, however defined, or related disorders, including schizophreniform disorder, schizoaffective disorder and delusional disorder, by any means of diagnosis, in any clinical setting (inpatients, outpatients or mixed samples).

If a study includes participants with other diagnoses, we will include the study only if participants with a diagnosis of schizophrenia or related disorders comprise at least 80% of the population. If details are missing, we will contact the study authors and, if they cannot provide data, we will exclude the study.

We are interested in making sure that information is as relevant as possible to the current care of people with schizophrenia, so we aim to highlight the current clinical state clearly (acute, early postacute, partial remission, remission), as well as the stage (prodromal, first episode, early illness, persistent), and whether the studies primarily focused on people with particular problems (e.g. negative symptoms, treatment‐resistant illnesses).

Types of interventions

1. Meta‐cognitive training (MCT and MCT+)

Defined as a structured manualised intervention (Moritz 2013). It combines CRT, CBTp and psychoeducation (Moritz 2014). For the CRT portion of MCT, people are presented with multiple cognitive tasks with the goal to reduce overconfidence in errors rather than accuracy. For the CBTp part of MCT for psychosis, MCT targets psychotic symptoms, but deals with cognitive processes first and then goes to the symptom level with the view to helping people distance themselves from their delusions also where positive symptoms actually foster self‐esteem (Moritz 2014). Psychoeducation is focused on providing information about schizophrenia as an illness (Moritz 2014). We will include group and individual interventions. We will also include MCT+ as the individualised format of MCT that also addresses negative symptoms and can lead to the treatment of individual symptoms through the generation of an illness model and a recovery plan (Moritz 2013). We will consider MCT as a stand‐alone psychological intervention. If the MCT arms include pharmacotherapy, we will include the study if pharmacotherapy is used in all arms of the trial.

2. Standard care or other psychosocial interventions

For this review we will consider 'standard care' as the care participants would normally receive in a standard mental health service and can consist of antipsychotic medication treatment alone or a combination of medication and additional psychological interventions of any type (other than MCT or MCT+). We will include interventions described as 'treatment as usual' and wait‐list control interventions as standard care.

We will also compare MCT with other psychosocial interventions.

We will consider the following comparisons.

• MCT and MCT+ versus another psychosocial intervention

• MCT and MCT+ versus standard care

Types of outcome measures

We will divide all outcomes into short term (three months or less), medium term (greater than three months to six months) and long term (greater than six months).

We chose the short‐term outcomes of three months or less as MCT and MCT+ may last up to three months, so the short‐term outcome can be considered the end of the treatment outcome. Medium‐term and long‐term outcomes may measure the persistence of the efficacy of MCT and MCT+ over time.

We generally prefer binary (e.g. improved/not improved) outcomes, because they are easier to interpret. We will also analyse continuous outcomes, but present them after the binary outcomes.

1. Mental state – general

1.1 Clinically important change in general mental state

Number of participants with a clinically important change in general mental state as defined by the individual studies (e.g. mental state much improved, or less than 50% reduction on a specified rating scale), short term.

2. Tolerability

2.1 Leaving the study early for any reason – overall tolerability

Number of participants who discontinued their participation from the study for any reason, short term.

1. Mental state – general

1.1 Mean endpoint or change score on general mental state scale

Mean endpoint or change score on any general psychopathological symptoms rating scale such as the Positive and Negative Symptoms Scale (PANSS; Kay 1986).

2. Mental state – specific

2.1 Clinically important change in positive symptoms

Number of participants with a clinically important change in positive symptoms as defined by the individual studies (e.g. mental state much improved, or less than 50% reduction on a specified rating scale, such as the PANSS Positive Symptoms subscale).

2.2 Clinically important change in negative symptoms

Number of participants with a clinically important change in negative symptoms as defined by the individual studies (e.g. mental state much improved, or less than 50% reduction on a specified rating scale, such as the PANSS Negative Symptoms subscale).

2.3 Clinically important change in insight

Number of participants with a clinically important change in insight as defined by the individual studies (e.g. mental state much improved, or less than 50% reduction on a specified rating scale, such as the Schedule for Assessment of Insight, Expanded Version; Kemp 1997).

2.4 Clinically important change in depressive symptoms

Number of participants with a clinically important change in depressive symptoms as defined by the individual studies (e.g. depressive symptoms much improved, or less than 50% reduction on a specified rating scale, such as the Calgary Depression Rating Scale for Schizophrenia; Addington 1993).

2.5 Mean endpoint or change score on positive symptoms scale

Mean endpoint or change score on any positive symptoms rating scale, such as the PANSS Positive Symptoms subscale.

2.6 Mean endpoint or change score on delusions symptoms scale

Mean endpoint or change score on any delusions symptoms rating scale such as the PANSS Delusions subscale.

2.7 Mean endpoint or change score on hallucinations symptoms scale

Mean endpoint or change score on any hallucinations symptoms rating scale such as the PANSS Hallucinations subscale.

2.8 Mean endpoint or change score on negative symptoms scale

Mean endpoint or change score on any negative symptoms rating scale such as the PANSS Negative Symptoms subscale.

2.9 Mean endpoint or change score on depressive symptoms scale

Mean endpoint or change score on any depressive symptoms rating scale, such as the Calgary Depression Rating Scale for Schizophrenia.

2.10 Mean endpoint or change score on clinical insight scale

Mean endpoint or change score on any clinical insight rating scale such as the Schedule for Assessment of Insight, Expanded Version.

3. Neuropsychological state

3.1 Clinically important change in neurocognitive state

Number of participants with a clinically important change in neurocognitive state as defined by the individual studies (e.g. neurocognitive state much improved, or less than 50% reduction on a specified rating scale, such as the Wisconsin Card Sorting Test; Berg 1948).

3.2 Mean endpoint or change score on neurocognitive testing scale

Mean endpoint or change score on any neurocognitive testing scale or test, such as the Wisconsin Card Sorting Test.

4. Cognitive biases

4.1 Jumping to conclusions

4.1.1 Clinically important change in jumping to conclusions

Number of participants with a clinically important change in jumping to conclusions as defined by the individual studies (e.g. mental state much improved, or less than 50% reduction on a specified rating scale such as the Fish Task; Moritz 2010c).

4.1.2 Mean endpoint or change score on any jumping to conclusion scale

Mean endpoint or change score on any jumping to conclusions scale or test, such as the Fish Task.

5. Satisfaction with care

5.1 Subjective acceptance of the intervention

5.1.1 Clinically important change in acceptance of the intervention

Number of participants who are satisfied with the treatment.

5.1.2 Mean endpoint or change score on subjective acceptance scale

Mean endpoint or change score on any subjective acceptance rating scale such the Client Satisfaction Questionnaire (Larsen 1979).

6. Quality of life

6.1 Clinically important change in quality of life

Number of participants with a clinically important change in quality of life as defined by the individual studies (e.g. quality of life much improved, or less than 50% reduction on a specified rating scale, such as the Quality of Life Scale; Heinrichs 1984).

6.2 Mean endpoint or change score on quality of life scale

Mean endpoint or change score on any quality of life rating scale, such as the Quality of Life Scale.

7. Service use

7.1 Hospital admissions

Number of days spent in hospital.

8. Global state

8.1 Clinically important change in global state

Number of participants with a clinically important change in quality of life as defined by the individual studies (e.g. global mental state much improved, or less than 50% reduction on a specified rating scale, such as the Symptom Checklist‐90‐Revised (SCL‐90‐R; Derogatis 1983)).

8.2 Mean endpoint or change score on general mental state scale

Mean endpoint or change score on any general psychopathological symptoms rating scale such as the SCL‐90‐R.

9. Adverse effects

9.1 At least one adverse effect/event

Number of participants experiencing at least one adverse event.

9.2 Incidence of specific adverse effects

Number of participants experiencing specific adverse effects.

9.3 Mean endpoint of change score on any adverse effects of psychotherapy scale

Mean endpoint or change score on any psychotherapy adverse effects scale, such as the Positive and Negative Effects of Psychotherapy Scale (Moritz 2019).

9.4 Mortality (by suicide or natural causes)

Number of participants who died by suicide or by natural causes.

Search methods for identification of studies

Electronic searches

The Cochrane Schizophrenia Information Specialist will search Cochrane Schizophrenia's register, using the following search strategy:

[(Metacogniti*) in Intervention Field of STUDY]

In such a study‐based register, searching the major concept retrieves all the synonyms and relevant studies. This is because the studies have already been organised, based on their interventions, and linked to the relevant topics (Shokraneh 2017). This allows rapid and accurate searches that reduce waste in the next steps of systematic reviewing (Shokraneh 2019). Following the methods from Cochrane (Lefebvre 2019), the Information Specialist compiles this register from systematic searches of major resources and their monthly updates (unless otherwise specified).

• Cochrane Central Register of Controlled Trials (CENTRAL)

• Cumulative Index to Nursing and Allied Health Literature (CINAHL)

• Embase

• MEDLINE

• PsycINFO

• PubMed

• US National Institute of Health Ongoing Trials Register (ClinicalTrials.gov; clinicaltrials.gov)

• World Health Organization International Clinical Trials Registry Platform (ICTRP) (www.who.int/ictrp)

• ISRCTN registry

• ProQuest Dissertations and Theses A&I and its quarterly update

The register also includes handsearches and conference proceedings (see Group's website; schizophrenia.cochrane.org/). It does not place any limitations on language, date, document type or publication status.

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. We will note the outcome of this contact in the 'Characteristics of included studies' table or 'Studies awaiting classification' tables.

Data collection and analysis

Selection of studies

After removing duplicates, at least two review authors (GG, VL, IE, AlC, MA, FT, AS, ArC, GGh, AP) will independently inspect citations from the searches and identify relevant abstracts using Covidence (www.covidence.org). Where disputes arise, we will acquire the full report for more detailed scrutiny. At least two review authors (GG, VL, IE, AlC, MA, FT, AS, ArC, GGh, AP) will then obtain and independently inspect full reports of the abstracts or reports meeting the review criteria. Where it is not possible to resolve disagreement by discussion, we will discuss with the senior review author of the team (AP) to resolve it and take a final decision. Where it is not possible to resolve disagreements because data are missing, we will attempt to contact the authors of the study concerned for clarification. We will document decisions to exclude studies in the 'Characteristics of excluded studies' table.

Data extraction and management

1. Extraction

At least two review authors (GG, VL, IE, AlC, MA, FT, AS, ArC, GGh, AP) will independently extract data from all included studies. We will attempt to extract data presented only in graphs and figures whenever possible, but will include only if two review authors independently obtain the same result. We will discuss any disagreements. Where it is not possible to resolve disagreements by discussion, we will discuss with the senior review author (AP), who will make a final determination. All decisions will be documented. If necessary, we will attempt to contact authors through an open‐ended request to obtain missing information or for clarification. The senior author AP will help clarify issues regarding any remaining problems, and we will document these final decisions.

For each included study we will extract the following study characteristics, and provide them in 'Characteristics of included studies' table: methods (allocation, blinding, duration, design, location, setting); participants (diagnosis, number, gender, age, history of illness); interventions (dose, administration, rescue medication); outcomes; notes.

2. Management

2.1 Forms

We will use Covidence to select the studies, enter the data and upload them immediately in Review Manager Web (www.covidence.org; RevMan Web 2022).

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);

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

• the instrument should be a global assessment of an area of functioning and not subscores which are not, in themselves, validated or shown to be reliable. However, we will include subscores of scales if these were validated or if these were predefined in a scale such as the Positive Symptom, Negative Symptom and General Symptom scores of the PANSS.

Ideally the measuring instrument should either be self‐report, or completed by an independent rater or relative (not the therapist). However, we realise that this is not often reported clearly.

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; however, calculation of change needs two assessments (baseline and endpoint) that can be difficult to obtain in unstable and difficult‐to‐measure conditions such as schizophrenia. We have decided primarily to use endpoint data, and only use change data if the former are not available. If necessary, we will combine endpoint and change data in the analysis. This procedure is possible when using mean differences (MDs) (Deeks 2020), and also when using standardised mean differences (SMDs). Although theoretically the combination of change and endpoint data when SMDs are used can be problematic, meta‐epidemiological research has shown that on average no major over‐ or underestimations can be expected (da Costa 2013).

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 will apply the following check to relevant continuous data before inclusion.

For endpoint data from studies including fewer than 200 participants, we will calculate the observed mean minus the lowest possible value of the scale and divide this by the standard deviation (SD) (Higgins 2021).

For example, in a scale that has possible lowest values higher than 0 (such as the PANSS, which can have values from 30 to 210), we will subtract the minimum score (in this case 30) from the observed mean, and then divide by the SD. In a scale that has 0 as minimum possible score, we will divide the observed mean by the SD.

For this calculation, we will check the original publication of the scales referenced in the studies, in order to understand if they can have a lowest possible score different from 0, and the adjustment described above is needed or not.

If the ratio obtained is lower than one, it strongly suggests that the data are skewed. If it is higher than one but less than two, there is suggestion that the data are skewed; if the ratio is larger than two, it is less likely that they are skewed (Altman 1996).

Where there is suggestion of skewness (ratio less than two), we will exclude the relevant studies in a sensitivity analysis to check if they have an impact on the results (see Sensitivity analysis for further details).

Skewed results will nevertheless be reported in 'other data' tables.

We will enter all relevant data from studies of more than 200 participants in the analysis irrespective of the above rules, because skewed data pose less of a problem in large studies. We will also enter all relevant change data, as 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.

2.5 Common measurement

To facilitate comparison between trials we aim, where relevant, 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, we will make efforts 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 (BPRS) (Overall 1962), or the PANSS which correspond to 'much improved' according to the Clinical Global Impressions scale of raters (CGI, Guy 1976), could be considered as a clinically significant response (Leucht 2005a; Leucht 2005b), in particular for acutely ill patients. However, we assumed that most participants included in the studies would be chronically ill. For these, even small improvements such as an at least 20% or 30% reduction of the BPRS or PANSS, which correspond to 'minimally improved' on the CGI (Leucht 2005a; Leucht 2005b), may be meaningful. Therefore, these cut‐offs were chosen as the primary ones. If data based on these thresholds are not available, we will use the primary cut‐off presented by the original authors, because the exact cut‐off is not so important in a meta‐analysis using risk ratios (RR) or odds ratios as effect sizes (Furukawa 2011).

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 MCT. Where keeping to this makes it impossible to avoid outcome titles with clumsy double‐negatives (e.g. 'not un‐improved') we will report data where the left of the line indicates an unfavourable outcome and note this in the relevant graphs.

Assessment of risk of bias in included studies

At least two review authors (GG, VL, IE, AlC, MA, FT, AS, ArC, GGh, AP) will independently assess risk of bias by using RoB 2 tool using the criteria described in the Cochrane Handbook for Systematic Reviews of Interventions to assess trial quality (Higgins 2021; Sterne 2019, Chapter 8).

This set of criteria is based on the judgement of the following domains:

• bias arising from the randomisation process;

• bias due to deviations from intended interventions;

• bias due to missing outcome data;

• bias in measurement of the outcome; and

• bias in selection of the reported result.

For each domain we will rate the available 'signalling questions' in order to reach a judgement (high, some concerns and low) following the tool algorithms implemented in the RoB 2 Excel tool (riskofbiasinfo.org).

RoB 2 generally allows studies to be assessed from two angles:

• the effect of assignment to the interventions at baseline, regardless of whether the interventions were received as intended (i.e. the intention‐to‐treat (ITT) effect);

• the adherence to the interventions (i.e. the per‐protocol effect) (Section 8.2.2, Cochrane Handbook for Systematic Reviews of Interventions;Higgins 2021). For the purpose of this review, we will aim to assess the ITT effect.

An evaluation with the RoB 2 tool will be performed for the following outcomes.

• Clinically important change in general mental state – short‐term

• Leaving the study early for any reason – overall tolerability

• Mean endpoint or change score on general mental state scale – short‐term

• Clinically important change in positive symptoms – short‐term

• Mean endpoint or change score on positive symptoms scale – short‐term

• Mean endpoint or change score on neurocognitive testing scale – short‐term

• Clinically important change in quality of life – short‐term

For cluster trials, we will use the additional domain specific for cluster‐RCTs from the archived version of the tool (Domain 1b – 'Bias arising from the timing of identification and recruitment of participants') and use the signalling questions from the archived version. For cross‐over trials, since we only use data from the first phase (see Measures of treatment effect), we will use the standard version of the RoB 2.

If the raters disagree, we will make the final rating by consensus. In case consensus cannot be achieved, the senior review author (AP) will make a final determination. Where there are inadequate details of randomisation and other characteristics of trials, we will attempt to contact authors of the studies in order to obtain further information. We will report non‐concurrence in quality assessment, but if disputes arise regarding the category to which a trial is to be allocated, we will resolve this by discussion.

We will note the level of risk of bias in the text of the review, the relevant forest plots, risk of bias graph, risk of bias summary, and the summary of findings table.

Assessment of bias in conducting the systematic review

We will conduct the review according to this published protocol, and report any deviations from it in the 'Differences between protocol and review' section.

Measures of treatment effect

1. Binary data

For binary outcomes, we will calculate a standard estimation of the RR and its 95% confidence interval (CI), as it has been shown that RR is more intuitive than odds ratios (Boissel 1999); and that odds ratios tend to be interpreted as RR by clinicians (Deeks 2000). Although the number needed to treat for an additional beneficial outcome (NNTB) and the number needed to treat for an additional harmful outcome (NNTH), with their CIs, are intuitively attractive to clinicians, they are problematic to calculate and interpret in meta‐analyses (Hutton 2009). For binary data presented in the summary of findings table we will, where possible, calculate illustrative comparative risks. For cross‐over trials, we will only use data from the first phase.

2. Continuous data

For continuous outcomes, we will estimate MD between groups, in particular when studies use natural units (such as days, kilograms, etc.). We prefer not to calculate effect size measures (SMD). However, if studies use scales of very considerable similarity, we will presume there is a small difference in measurement, and we will calculate SMD. It should be noted that SMD can be transformed to MD using the formula MD = SMD × SD of the scale of interest (Higgins 2021). For cross‐over trials, we will only use data from the first phase.

Unit of analysis issues

1. Cluster‐randomised trials

Studies increasingly employ 'cluster randomisation' (such as randomisation by clinician or practice), but analysis and pooling of clustered data poses problems. Authors often fail to account for intraclass correlation in clustered studies, leading to a unit‐of‐analysis error whereby P values are spuriously low, CIs unduly narrow and statistical significance overestimated (Divine 1992). This causes type I errors (Bland 1997; 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 adjust for the clustering effect.

Where clustering is not accounted for in primary studies, we will present data in a table, with a (*) symbol to indicate the presence of a probable unit of analysis error. We will seek to contact first authors of studies to obtain intraclass correlation coefficients (ICC) for their clustered data and to adjust for this by using accepted methods (Gulliford 1999).

We have sought statistical advice and have been advised that the binary data from cluster trials 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: thus design effect = 1 + (m − 1) × ICC (Donner 2002). If the ICC is not reported we will assume it to be 0.1 (Ukoumunne 1999).

If cluster studies have been appropriately analysed and taken ICCs and relevant data documented in the report into account, 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, participants can differ significantly from their initial state at entry to the second phase, despite a washout phase. For the same reason cross‐over trials are not appropriate if the condition of interest is unstable (Elbourne 2002). As both carry‐over and unstable conditions are very likely in severe mental illness, we will only use data from the first phase of cross‐over studies.

3. Studies with multiple treatment groups

Where a study involves more than two treatment arms, if relevant, we will present the additional treatment arms in comparisons. If data are binary, we will simply add these and combine within the two‐by‐two table. If data are continuous, we will combine data following the formula in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021), as implemented in RevMan calculator. Where additional treatment arms are not relevant, we will not reproduce these data, but will list them in the 'Characteristics of included studies' table.

Dealing with missing data

1. Overall loss of credibility

Although at some degree of loss of follow‐up, data lose credibility (Xia 2009), we will not exclude studies based on this.

However, if more than 50% of data are unaccounted for (lost to follow‐up) we will exclude these studies from a Sensitivity analysis. If more than 50% of those 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 by downgrading the certainty (and not excluding the study in the Sensitivity analysis). Finally, we will also downgrade certainty within the summary of findings table should the loss be 25% to 50% in total.

2. Binary

We will present data on a 'once‐randomised‐always‐analyse' basis (an ITT). We will undertake a Sensitivity analysis excluding studies using completer analyses.

3. Continuous

3.1 Standard deviations

If SDs are not reported, we will try to obtain the missing values from the study authors. If these are not available, where there are missing measures of variance for continuous data, but an exact standard error (SE) and CIs available for group means, and either P value or t value available for differences in mean, we can calculate SDs according to the rules described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021). When only the SE is reported, SDs are calculated using the formula SD = SE × √(n). The Cochrane Handbook for Systematic Reviews of Interventions presents detailed formulae for estimating SDs from P, t or F values; CIs; ranges or other statistics (Higgins 2021). If these formulae do not apply, we will calculate the SDs according to a validated imputation method which is based on the SDs 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. Nevertheless, we will examine the validity of the imputations in a sensitivity analysis that excludes imputed values.

3.2 Assumptions about participants who left the trials early or were lost to follow‐up

Various methods are available to account for participants who left the trials early or were lost to follow‐up. Some trials just present the results of study completers; others use the method of last observation carried forward (LOCF); while more recently, methods such as multiple imputation or mixed‐effects models for repeated measurements (MMRM) have become more of a standard. While the latter methods seem to be somewhat better than LOCF (Leon 2006), we consider that the high percentage of participants leaving the studies early and differences between groups in their reasons for doing so is often the core problem in randomised schizophrenia trials. Therefore, we will not exclude studies based on the statistical approach used. However, by preference we will use the more sophisticated approaches, that is, we will prefer to use MMRM or multiple‐imputation to LOCF, and we will only present completer analyses if some type of ITT data are not available at all. Moreover, we will address this issue in the item 'Incomplete outcome data' of the RoB 2 tool.

Assessment of heterogeneity

1. Clinical heterogeneity

We will consider all included studies initially, without seeing comparison data, to judge clinical heterogeneity. We will simply inspect all studies for participants who are clearly outliers or situations that we had not predicted would arise and, where found, discuss such situations or participant groups.

2. Methodological heterogeneity

We will consider all included studies initially, without seeing comparison data, to judge methodological heterogeneity. We will simply inspect all studies for clearly outlying methods which we had not predicted would arise and discuss any such methodological outliers.

3. Statistical heterogeneity

3.1 Visual inspection

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

3.2 Employing the I² statistic

We will investigate heterogeneity between studies by considering the I² statistic alongside the Chi² 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 the magnitude and direction of effects as well as the strength of evidence for heterogeneity (e.g. P value from Chi² test, or a CI for the I² statistic). We will interpret an I² estimate greater than or equal to 50% and accompanied by a Chi² statistic with P < 0.05 as evidence of substantial heterogeneity (Chapter 10, Cochrane Handbook for Systematic Reviews of Interventions; Deeks 2020). When there are substantial levels of heterogeneity found in the primary outcomes, we will explore reasons for heterogeneity (Subgroup analysis and investigation of heterogeneity).

Assessment of reporting biases

Reporting biases arise when the dissemination of research findings is influenced by the nature and direction of results (Egger 1997). These are described in Chapter 13 of the Cochrane Handbook for Systematic reviews of Interventions (Higgins 2021).

1. Protocol versus full study

We will try to locate protocols of included RCTs. If the protocol is available, we will compare outcomes in the protocol and in the published report. If the protocol is not available, we will compare outcomes listed in the methods section of the trial report with actually reported results. If details from ClinicalTrials.gov and WHO registry (ICTRP) are available, they will be included in the search results and these can be used to compare the differences between planned methods and published results.

2. Funnel plot

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 10 or fewer studies, or where all studies are of similar size. 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 for preference for 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. This often seems to be true to us and the random‐effects model takes into account differences between studies, even if there is no significant heterogeneity. However, there is a disadvantage to the random‐effects model as it puts added weight onto small studies, which often are the most biased ones. Depending on the direction of effect, these studies can either inflate or deflate the effect size. We will use a random‐effects model for analyses. In a sensitivity analysis of the primary outcomes we will apply the fixed‐effect model.

Subgroup analysis and investigation of heterogeneity

1. Subgroup analyses

Subgroup analyses will only be conducted on the primary outcomes. We are aware that subgroup analyses are observational by nature and, therefore, consider the results to be exploratory and not explanatory. Since the primary outcomes are dichotomous, we will not conduct a meta‐regression analyses in R.

1.1 Setting of study

We will perform subgroup analyses based on setting where the MCT has been studied (inpatient or outpatient, or both). This can provide some valuable clinical information on the setting where MCT or MCT+ can perform the best.

1.2 Effect of group versus individual training

We will perform subgroup analyses based on modality in which the MCT has been studied (group, MCT or individual, MCT+). This can provide some valuable clinical information on whether individual treatment may perform better or worse than group treatment.

1.3 Population: chronic versus first‐episode schizophrenia

We will perform subgroup analyses based on the population of the study (chronic or first‐episode schizophrenia). This can provide some valuable clinical information on the efficacy of MCT or MCT+ in a population with more or less chronic illness.

2. Investigation of heterogeneity

We will report if inconsistency is high. First, we will investigate whether data have been entered correctly. If this is the case, we will consider the following strategies:

• Pool the data despite the heterogeneity. An example where this strategy may be appropriate is the effects of all studies are in the same direction. In other words, the heterogeneity reflects the degree of an effect rather than its direction which is less problematic. Another example is when heterogeneity can be explained by appropriate subgroup analyses.

• Exclude outlying studies. This strategy may apply, if reinspection of such studies reveal methodological or clinical differences that were previously overlooked.

• Not pool the studies.

We will describe and discuss all decisions in this regard.

Sensitivity analysis

Where possible we will perform sensitivity analyses for the primary outcomes to explore the influence of the following factors on effect size. If there are substantial differences in the direction or precision of effect estimates in any of the sensitivity analyses listed below, we will discuss them in the 'Discussion' section.

1. Blinding of outcome assessor

We will exclude studies that did not employ a blind outcome assessor.

2. Assumptions for missing data

We will exclude studies using completer analyses only (see Dealing with missing data).

3. Loss to follow‐up

We will exclude studies where the overall loss of data was greater than 50%.

4. Risk of bias

We will analyse the effects of excluding trials that are at overall high risk of bias (see Assessment of risk of bias in included studies) for the meta‐analysis of the primary outcomes.

5. Imputed values

We will undertake a sensitivity analysis excluding trials where we use imputed values for ICC in calculating the design effect in cluster‐RCTs or where we imputed SDs.

6. Fixed‐ and random‐effects models

We will synthesise data using a random‐effects model; however, we will also synthesise data for the primary outcomes using a fixed‐effect model to evaluate whether this alters the significance of the results.

7. Skewed data

We will perform a sensitivity analysis excluding studies for which there is suggestion of skewness (mean/SD ratio less than two; see Data extraction and management). If this changes the results in comparison with the main analysis (from significantly favouring the intervention to significantly favouring the control, or vice‐versa), we will exclude these studies also from the main analysis, and present their data in 'Other data' tables.

8. Chinese studies

Studies from mainland China tends to have a different methodology, and at times the methods are often not described in detail (Woodhead 2016). To account for these potential differences we will exclude these studies from a sensitivity analysis.

Summary of findings and assessment of the certainty of the evidence

We will use the GRADE approach to interpret findings (Schünemann 2020); and will use GRADEpro GDT to import data from RevMan Web to create a summary of findings table (GRADEpro GDT; RevMan Web 2022). These tables provide outcome‐specific information concerning the overall certainty 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 rate as important to patient care and decision‐making. We will use the overall RoB 2 judgements to feed into the GRADE assessment. We aim to select the following main outcomes for inclusion in the 'Summary of findings' table.

• Clinically important change in general mental state – short‐term

• Leaving the study early for any reason – overall tolerability

• Mean endpoint or change score on general mental state scale – short‐term

• Clinically important change in positive symptoms – short‐term

• Mean endpoint or change score on positive symptoms scale – short‐term

• Mean endpoint or change score on neurocognitive testing scale – short‐term

• Clinically important change in quality of life – short‐term

If data are not available for these prespecified outcomes but are available for ones that are similar, we will present the closest outcome to the prespecified one in the table but take this into account when grading the findings.

We will justify all decisions to downgrade the certainty of the evidence using footnotes and we will make comments to aid reader's understanding of the review where necessary.

Contributions of authors

GG: conceiving, designing and co‐ordinating the review; writing the protocol 'Background' and 'Methods'; guarantor of the review.

VL: reviewing the protocol and providing input on it.

IE: reviewing the protocol and providing input on it.

MA: reviewing the protocol and providing input on it.

FT: reviewing the protocol and providing input on it.

AS: reviewing the protocol and providing input on it.

GGh: reviewing the protocol and providing input on it.

AIC: reviewing the protocol and providing input on it.

AP: reviewing and helping write the 'Background' section.

Sources of support

Internal sources

• National Institute for Health and Care Research (NIHR), UK

  provided funding for Cochrane Schizophrenia Group

External sources

• New Source of support, Other

  No external source of support

Declarations of interest

GG: is a Cochrane Editor. He was not involved in the editorial process of the manuscript. He is a Diplomate of the Academy of Cognitive Therapy.

VL: none.

IE: none.

MA: none.

FT: none.

AS: none.

GGh: none.

AIC: none.

AP: he is trained in CBTp and MCT and provides treatment using these modalities in his clinical practice.

New