1.1 Deutetrabenazine

Any dose suggested as being therapeutic (6 mg/day to 12 mg/day), any means of delivery.

1.2 Valbenazine

Any dose suggested as being therapeutic (40 mg/day to 80 mg/day), any means of delivery.

versus

1. Tardive dyskinesia (TD) symptoms

Clinically important change in the TD symptoms of individuals, defined as more than 50% improvement on any TD scale ‐ over any time period

2. Adverse effects

Clinically important extrapyramidal adverse effects ‐ over any time period

1. Tardive dyskinesia (TD) symptoms

1.1 Any change in the symptoms of TD, defined as any change on any TD scale
1.2 Deterioration in the symptoms of TD, defined as any deleterious change on any TD scale
1.3 Average endpoint/change score on any TD scale

2. Mental state

2.1 Relapse (according to any definition)
2.2 Any change in mental state (such as delusions and hallucinations), defined as any change on any scale
2.3 Average endpoint score or change score on mental state scale
2.5 Use of clozapine

3. Leaving the study early (acceptability of treatment)

3.1 Acceptability of the intervention to participants as measured by numbers of people leaving the study early

4. Adverse effects

4.1 Any adverse effect (other than deterioration symptoms of TD or relapse).
4.2 Use of any antiparkinson drugs
4.3 Average endpoint/change score on extrapyramidal adverse effects scale
4.4 Acute dystonia
4.5 Other adverse effects ‐ divided into general and specific

5. Hospital and service utilisation outcomes

5.1 Hospital admission
5.2 Average change in days in hospital
5.3 Improvement in hospital status (e.g. change from formal to informal admission status, use of seclusion, level of observation)
5.4 Improvement in community status (intensity of service utilisation, use of treatment orders)

6. Economic outcomes

6.1 Average change in total cost of medical and mental health care
6.2 Total indirect and direct costs
6.3 Employment

7. Social confidence, social inclusion, social networks, or personalised quality‐of‐life measures

7.1. Clinically important changes in social confidence, social inclusion, social networks, or personalised quality‐of‐life measures in either recipients of care or caregivers
7.2 Average endpoint/change score in social confidence, social inclusion, social networks, or personalised quality‐of‐life measures
7.3 Stigma ‐ however assessed.

8. Behaviour

8.1 Clinically important agitation
8.2 Use of adjunctive medication for sedation
8.3 Aggression to self or others

9. Cognitive state

9.1 Clinically important change
9.2 Any change, general and specific

'Summary of findings' table

We will use the GRADE approach to interpret findings (Schünemann 2011) and use GRADEpro‐GDT to export data from this review to create 'Summary of findings' tables. These tables provide outcome‐specific information concerning the overall quality of evidence from the included studies in the comparison, the magnitude of effects of interventions examined, and the sum of available data on all outcomes rated as important to patient care and decision making. We aim to select the following main outcomes for inclusion in the 'Summary of findings' table.

• Tardive dyskinesia: clinically important change TD symptoms, defined as more than 50% improvement on any TD scale ‐ over any time period

• Tardive dyskinesia: deterioration in symptoms of TD, defined as any deleterious change on any TD scale

• Adverse effects: clinically important extrapyramidal adverse effects ‐ over any time period.

• Adverse effects: any adverse effect (other than deterioration in symptoms of TD or relapse).

• Mental state: any change in mental state (such as delusions and hallucinations), defined as any change on any scale.

• Leaving the study early (acceptability of treatment): acceptability of the intervention to participants as measured by numbers of people leaving the study early.

• Social confidence, social inclusion, social networks, or personalised quality of‐life measures: clinically important changes in social confidence, social inclusion, social networks, or personalised quality‐of‐life measures in either recipients of care or caregivers.

2.1 Forms

We will extract data into paper forms.

2.2 Scale‐derived data

We will include continuous data from rating scales only if:

a) the psychometric properties of the measuring instrument have been described in a peer‐reviewed journal (Marshall 2000);
b) the measuring instrument has not been written or modified by one of the trialists for that particular trial; and
c) the instrument should be a global assessment of an area of functioning and not sub‐scores which are not, in themselves, validated or shown to be reliable. However there are exceptions, we will include sub‐scores from mental state scales measuring positive and negative symptoms of schizophrenia.

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; in 'Description of studies' we will note if this is the case or not.

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, as we prefer to use mean differences (MDs) rather than standardised mean differences (SMDs) throughout (Deeks 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 will apply the following standards to relevant continuous data before inclusion.

For endpoint data from studies including fewer than 200 participants:

a) when a scale starts from the finite number zero, we will subtract the lowest possible value from the mean, and divide this by the standard deviation. If this value is lower than one, it strongly suggests that the data are skewed and we will exclude these data. If this ratio is higher than one but less than two, there is suggestion that the data are skewed: we will enter these data and test whether their inclusion or exclusion would change the results substantially. If such data change results we will enter as 'other data'. Finally, if the ratio is larger than two we will include these data, because it is less likely that they are skewed (Altman 1996; Higgins 2011).

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

Please note: 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 or not data are skewed.

2.5 Common measure

Where relevant, to facilitate comparison between trials, we plan 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 plan to convert continuous outcome measures to dichotomous data. This can be done by identifying cutoff 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 (Kay 1986), this can be considered as a clinically significant response (Leucht 2005a; Leucht 2005b). If data based on these thresholds are not available, we will use the primary cutoff presented by the study authors.

2.7 Direction of graphs

Where possible, we plan to enter data in such a way that the area to the left of the line of no effect indicates a favourable outcome for VMAT2. Where keeping to this makes it impossible to avoid outcome titles with clumsy double‐negatives (e.g. 'not un‐improved') we will present data where the left of the line indicates an unfavourable outcome and note this in the relevant graphs.

3.1 Attrition

We will report and use data where attrition for a continuous outcome is between 0% and 50%, and data only from people who had completed the study to that point are reported.

3.2 Standard deviations

If standard deviations (SDs) are not reported, we will try to obtain the missing values from the 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 2011). When only the SE is reported, SDs are calculated by 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 2011). 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.

3.3 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 feel 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. We will therefore not exclude studies based on the statistical approach used. However, by preference we will use the more sophisticated approaches, i.e. we will prefer to use MMRM or multiple‐imputation to LOCF, and we will only present completer analyses if some kind of ITT data are not available at all. Moreover, we will address this issue in the item 'Incomplete outcome data' of the 'Risk of bias' tool.

3.1 Visual inspection

We will visually inspect graphs 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 I² 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 confidence interval for I²). We will interpret an I² estimate greater than or equal to 50% and accompanied by a statistically significant Chi² statistic as evidence of substantial heterogeneity (Chapter 9. Cochrane Handbook for Systematic Reviews of Interventions) (Deeks 2011). When substantial levels of heterogeneity are found in the primary outcome, we will explore reasons for heterogeneity (Subgroup analysis and investigation of heterogeneity).

1.1 Primary outcomes

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 antipsychotic reduction/cessation or specific antipsychotics in people with TD in general. In addition, however, we will try to report data on subgroups of participants in the same clinical state and stage, and with similar problems.