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Cochrane Database of Systematic Reviews Protocol - Intervention

Intermittent drug techniques for schizophrenia

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Abstract

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

To review the effects of different intermittent drug techniques compared to maintenance therapy in schizophrenia.

Background

Schizophrenia is a disabling mental disorder, with a lifetime prevalence of about 1% (Jablensky 1992). Although schizophrenia can occur as a single episode of illness, up to 41% of those who develop schizophrenia suffer a chronic and often disabling illness with remission and relapses (Prudo 1987). Neuroleptic medication is the mainstay of treatment for this illness (Dencker 1980) and is generally regarded as highly effective, especially in controlling symptoms such as abnormal perceptions (hallucinations), disordered thinking and fixed false beliefs (delusions) (Kane 1998). In addition, maintenance therapy with neuroleptics is associated with a reduced risk of relapse (Schooler 1993). However, neuroleptic medications have been associated with a range of adverse effects, such as abnormal involuntary movements (tardive dyskinesia), dysphoria, and apathy. Such adverse effects impact negatively on the patients quality of life and are a frequently cited reason for non‐compliance with antipsychotic medication.

Adherence to medication is a significant issue in the clinical management of schizophrenia. Early treatment discontinuation in patients with schizophrenia or schizophrenia‐like disorders is strikingly common, with estimates of its prevalence in antipsychotic drug trials ranging from 25%‐75% (Nose 2003). Discontinuation of a prescribed antipsychotic drug is associated with symptom exacerbation, relapse, and increased hospitalisation (Perkins 2002). A review that analyzed 66 studies found a mean cumulative relapse rate of 53% in patients completely withdrawn from neuroleptic therapy compared to 16% for those maintained on a regimen of neuroleptic therapy over a mean follow‐up period of 9.7 months (Gilbert 1995). Evidence also points to the fact that experiencing a relapse of schizophrenia lowers a person's level of social functioning and quality of life (Curson 1985).

Various strategies have been examined in order to reduce a person's cumulative exposure to neuroleptics in the hope that this will reduce adverse effects of long‐term treatment, but maintain the antipsychotic effects. These strategies include dose reduction, neuroleptic cessation and intermittent drug techniques, such as drug holidays. Prodrome‐based intervention is also an intermittent drug technique. For the latter intervention the drug therapy is given only when the patient shows early signs of relapse (prodromal symptoms). In addition, because people with chronic schizophrenia very frequently discontinue and re‐instigate their own antipsychotic medications, and few continue to take their medications for a long time (Lieberman 2005), self‐imposed "drug holidays" are common.

Objectives

To review the effects of different intermittent drug techniques compared to maintenance therapy in schizophrenia.

Methods

Criteria for considering studies for this review

Types of studies

We included all randomised controlled trials. Where a trial was described as 'double‐blind', but it was only implied that the study was randomised, we included these trials in a sensitivity analysis. If there was no substantive difference within primary outcomes (see types of outcome measures) when these 'implied randomisation' studies were added, we included these in the final analysis. If there was a substantive difference, we only used clearly randomised trials and described the results of the sensitivity analysis in the text. We excluded quasi‐randomised studies, such as those allocating by using alternate days of the week.

Types of participants

We included people with schizophrenia and other types of schizophrenia‐like psychoses (schizophreniform and schizoaffective disorders). There is no clear evidence that the schizophrenia‐like psychoses are caused by fundamentally different disease processes or require different treatment approaches (Carpenter 1994).

Types of interventions

1. 'Prodrome‐based intervention' defined as treatment given on the early signs of relapse.
2. 'Crisis intervention' defined as treatment given only in case of full relapse and discontinued again after re‐stabilization.
3. 'Gradually increased drug‐free period' defined as increasing the cessation period of the treatment constantly.
4. 'Drug Holidays' defined as stopping the medication for fixed periods, and then reintroducing it (repeating this more than once).
5. Maintenance therapy, as defined by the treating physician.

Types of outcome measures

Outcomes were grouped into the short term (up to 12 weeks), medium term (13 to 26 weeks) and long term (over 26 weeks).

Primary outcomes

We chose relapse (as defined in the individual studies) and hospitalisation as the primary outcome measures.

Secondary outcomes

1. Death ‐ suicide and natural causes

2. Global state
2.1 No clinically important change in global state (as defined by individual studies)
2.2 Average endpoint global state score
2.3 Average change in global state scores

3. Service outcomes
3.1 Time to hospitalisation

4. Mental state (with particular reference to the positive and negative symptoms of schizophrenia)
4.1 No clinically important change in general mental state
4.2 Average endpoint general mental state score
4.3 Average change in general mental state scores
4.4 No clinically important change in specific symptoms (positive symptoms of schizophrenia, negative symptoms of schizophrenia, depression, mania)
4.5 Average endpoint specific symptom score
4.6 Average change in specific symptom scores

5. General functioning.
5.1 No clinically important change in general functioning
5.2 Average endpoint general functioning score
5.3 Average change in general functioning scores
5.4 No clinically important change in specific aspects of functioning, such as social or life skills
5.5 Average endpoint specific aspects of functioning, such as social or life skills
5.6 Average change in specific aspects of functioning, such as social or life skills

6. Behaviour
6.1 No clinically important change in general behaviour
6.2 Average endpoint general behaviour score
6.3 Average change in general behaviour scores
6.4 No clinically important change in specific aspects of behaviour
6.5 Average endpoint specific aspects of behaviour
6.6 Average change in specific aspects of behaviour

7. Adverse effects ‐ general and specific
7.1 Clinically important general adverse effects
7.2 Average endpoint general adverse effect score
7.3 Average change in general adverse effect scores
7.4 Clinically important specific adverse effects
7.5 Average endpoint specific adverse effects
7.6 Average change in specific adverse effects

8. Engagement with services

9. Satisfaction with treatment
9.1 Leaving the studies early
9.2 Recipient of care not satisfied with treatment
9.3 Recipient of care average satisfaction score
9.4 Recipient of care average change in satisfaction scores
9.5 Carer not satisfied with treatment
9.6 Carer average satisfaction score
9.7 Carer average change in satisfaction scores

10. Quality of life
10.1 No clinically important change in quality of life
10.2 Average endpoint quality of life score
10.3 Average change in quality of life scores
10.4 No clinically important change in specific aspects of quality of life
10.5 Average endpoint specific aspects of quality of life
10.6 Average change in specific aspects of quality of life

11. Economic outcomes
11.1 Direct costs
11.2 Indirect costs

12. Cognitive functioning
12.1 No clinically important change in cognitive functioning
12.2 Average endpoint cognitive functioning score
12.3 Average change in cognitive functioning scores
12.4 No clinically important change in specific aspects of cognitive functioning
12.5 Average endpoint specific aspects of cognitive functioning
12.6 Average change in specific aspects of cognitive functioning

13. Leaving the study early

Search methods for identification of studies

Electronic searches

We searched The Cochrane Schizophrenia Group Trials Register (March 2006) using the phrase:
[((intermit* or drug?holiday* or drug?free* or internal?med*) in title, abstract and index fields in REFERENCE) OR ((intermittent medication or drug‐free period) in interventions field in STUDY]

This register is compiled by systematic searches of major databases, hand searches and conference proceedings (see Group Module).

Searching other resources

1. Reference searching
We inspected reference lists of all identified studies for more trials.

2. Personal contact
We contacted the first author of each included study for information regarding unpublished trials.

Data collection and analysis

[For definitions of terms used in this, and other sections, please refer to the Glossary]

1. Selection of trials
Material downloaded from electronic sources included details of author, institution or journal of publication.

We (MM and AA) inspected each report in order to ensure reliable selection. We resolved any disagreement by discussion, and where there was still doubt, we acquired the full article for further inspection. Once the full articles were obtained, we (MM and AA) independently decided whether the studies met the review criteria. If disagreement could not be resolved by discussion, we sought further information and added these trials to the list of those awaiting assessment.

2. Assessment of methodological quality
We assessed the methodological quality of each of included trials in this review using the criteria described in the Cochrane Handbook (Higgins 2005) and the Jadad Scale (Jadad 1996). The former is based on the evidence of a strong relationship between allocation concealment and direction of effect (Schulz 1995). The categories are defined below:

A. Low risk of bias (adequate allocation concealment)
B. Moderate risk of bias (some doubt about the results)
C. High risk of bias (inadequate allocation concealment). For the purpose of the analysis in this review, trials were included if they met the Cochrane Handbook criteria A or B.

The Jadad Scale measures a wider range of factors that impact on the quality of a trial. The scale includes three items:
1. Was the study described as randomised?
2. Was the study described as double‐blind?
3. Was there a description of withdrawals and drop outs?

Each item receives one point if the answer is positive. In addition, a point can be deducted if either the randomisation or the blinding/masking procedures described are inadequate. For this review we used a cut‐off of two points on the Jadad scale to check the assessment made by the Handbook criteria. However, we did not use the Jadad Scale to exclude trials.

3. Data collection
We (MM and AA) independently extracted data from selected trials, while KB and MAM separately re‐extracted information from the same trials. When disputes arose we attempted to resolve these by discussion. When this was not possible and further information was necessary to resolve the dilemma, we did not enter data and we added the trial to the list of those awaiting assessment.

4. Data synthesis
4.1 Data types
We assessed outcomes using continuous (for example changes on a behaviour scale), categorical (for example, one of three categories on a behaviour scale, such as 'little change', 'moderate change' or 'much change') or dichotomous (for example, either 'no important changes' or 'important changes' in a person's behaviour) measures. Currently RevMan does not support categorical data so we were unable to analyse this.

4.2 Incomplete data
We did not include trial outcomes if more than 50% of people were not reported in the final analysis.

4.3 Dichotomous ‐ yes/no ‐ data
We carried out an intention to treat analysis. On the condition that more than 50% of people completed the study, we counted everyone allocated to the intervention, whether they completed the follow up or not. We assumed that those who dropped out had the negative outcome, with the exception of death. Where possible, we made 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'. If the authors of a study had used a predefined cut off point for determining clinical effectiveness we used this where appropriate. Otherwise we generally assumed that if there had been a 50% reduction in a scale‐derived score, this could be considered as a clinically significant response. Similarly, we considered a rating of 'at least much improved' according to the Clinical Global Impression Scale (Guy 1976) as a clinically significant response.

We calculated the relative risk (RR) and its 95% confidence interval (CI) based on the fixed effects model. We calculated the relative risk of statistically significantly heterogeneous outcomes was using a random effects model. When the overall results were significant we calculated the number needed to treat (NNT) and the number needed to harm (NNH) as the inverse of the risk difference.

4.4 Continuous data
4.4.1 Normally distributed 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 applied the following standards to all data before inclusion: (a) standard deviations and means were reported in the paper or were obtainable from the authors; (b) when a scale started from the finite number zero, the standard deviation, when multiplied by two, was less than the mean (as otherwise the mean is unlikely to be an appropriate measure of the centre of the distribution, (Altman 1996); (c) if a scale started from a positive value (such as PANSS which can have values from 30 to 210) the calculation described above was modified to take the scale starting point into account. In these cases skew is present if 2SD>(S‐Smin), where S is the mean score and Smin is the minimum score. Endpoint scores on scales often have a finite start and end point and these rules can be applied to them. When continuous data are presented on a scale which includes a possibility of negative values (such as change on a scale), it is difficult to tell whether data are non‐normally distributed (skewed) or not. We entered skewed data from studies of less than 200 participants in additional tables rather than into an analysis. Skewed data poses less of a problem when looking at means if the sample size is large and we entered these into a synthesis.

For change data (endpoint minus baseline), the situation is even more problematic. In the absence of individual patient data it is impossible to know if data are skewed, though this is likely. After consulting the ALLSTAT electronic statistics mailing list, we presented change data in MetaView in order to summarise available information. In doing this, we assumed either that data were not skewed or that the analyses could cope with the unknown degree of skew. Without individual patient data it is impossible to test this assumption. Where both change and endpoint data were available for the same outcome category, we only presented endpoint data. We acknowledge that by doing this we excluded much of the published change data, but argue that endpoint data is more clinically relevant and that if we presented change data along with endpoint data, it would be given undeserved equal prominence. We have contacted authors of studies reporting only change data for endpoint figures. We reported non‐normally distributed data in the 'other data types' tables.

4.4.2 Rating scales: A wide range of instruments are available to measure mental health outcomes. These instruments vary in quality and many are not valid, or even ad hoc. For outcome instruments some minimum standards have to be set. It has been shown that the use of rating scales which have not been described in a peer‐reviewed journal (Marshall 2000) are associated with bias, therefore we excluded the results of such scales. Furthermore, we stipulated that the instrument should either be a self report or be completed by an independent rater or relative (not the therapist), and that the instrument could be considered a global assessment of an area of functioning. However, as it was expected that therapists would frequently also be the rater, we included such data but commented on the data as 'prone to bias'.

Whenever possible we took the opportunity to make direct comparisons between trials that used the same measurement instrument to quantify specific outcomes. Where continuous data were presented from different scales rating the same effect, we presented both sets of data and inspected the general direction of effect.

4.4.3 Summary statistic
For continuous outcomes we estimated a weighted mean difference (WMD) between groups, again based on the fixed effects model. Afterwards, we carried out a sensitivity analysis to find heterogeneous data. We reassessed heterogeneous data using a random effects model.

4.5 Cluster trials
Studies increasingly employ 'cluster randomisation' (such as randomisation by clinician or practice) but analysis and pooling of clustered data poses problems. Firstly, authors often fail to account for intra class correlation in clustered studies, leading to a 'unit of analysis' error (Divine 1992) whereby p values are spuriously low, confidence intervals unduly narrow and statistical significance overestimated. This causes type I errors (Bland 1997, Gulliford 1999).

Where clustering was not accounted for in primary studies, we presented the data in a table, with 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 intra‐class correlation co‐efficients of 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 also present these data as if from a non‐cluster randomised study, but adjusted 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 intraclass correlation co‐efficient (ICC) [Design effect = 1+(m‐1)*ICC] (Donner 2002). If the ICC was not reported it was assumed to be 0.1 (Ukoumunne 1999).

If cluster studies had been appropriately analysed taking into account intra‐class correlation coefficients and relevant data documented in the report, synthesis with other studies would have been possible using the generic inverse variance technique.

5. Investigation for heterogeneity
Firstly, we considered all the included studies within any comparison to judge clinical heterogeneity. Then we visually inspected graphs to investigate the possibility of statistical heterogeneity. This was supplemented using, primarily, the I‐squared statistic. This provides an estimate of the percentage of variability due to heterogeneity rather than chance alone. Where the I‐squared estimate was greater than or equal to 75%, we interpreted this as indicating the presence of high levels of heterogeneity (Higgins 2003). If inconsistency was high, we did not summate data, but presented this separately and investigated the reasons for heterogeneity.

6. Addressing publication bias
We entered data from all identified and selected trials into a funnel graph (trial effect versus trial size) in an attempt to investigate the likelihood of overt publication bias.

7. Subgroup analyses
We carried out a subgroup analysis to compare results between different interventions, as defined in 'Types of interventions'.

8. General
Where possible, we entered data in such a way that the area to the left of the line of no effect indicated a favourable outcome for intermittent treatment.