PROTOCOL: Cognitive-behavioural treatment for amphetamine-type stimulants (ATS) use disorders

Types of studies

Randomised controlled trials (RCTs) and quasi-RCTs. This includes designs that either randomly assign participants to the cognitive-behavioral treatment or comparator condition, or designs that use a quasi-random allocation mechanism, such as alternating assignment, next available treatment slot or wait-list controls. We will exclude all other trial designs.

Types of participants

Participants with ATS dependence or abuse diagnosed by any set of criteria. This includes both the DSM-IV (Diagnostic and Statistical Manual of Mental Disorders) and ICD 10 criteria as well as any other explicit ATS dependence or abuse diagnostic system. We will excluded studies that rely solely on client self-reporting of an ATS dependence or abuse disorder without formal clinical assessment. We will only include trials recruiting adults (i.e. aged 18 years or above). Participants may have comorbid conditions.

Types of interventions

We will include any CBT interventions in either individual or group therapy formats, in any treatment settings and any treatment modalities (e.g. face-to-face treatment, telephone treatment, computer-based treatment). CBT interventions include behavioural therapy, cognitive therapy, cognitive-behavioural therapy, “third-wave” CBT and any combinations of these therapies. However, we will exclude any studies where CBT is delivered in conjunction with other types of psychotherapy and pharmacotherapy.

Comparison: other types of psychotherapy, pharmacotherapy, 12-step facilitation, no intervention or treatment as usual.

Types of outcome measures

Electronic searches

We will search for relevant trials in the following sources:

Cochrane Drugs and Alcohol Group Specialised Register;

We will search databases using MeSH and free-text terms relating to CBT and ATS use disorder as shown in Appendix 1. We will combine the PubMed search with the Cochrane Highly Sensitive Search Strategy for identifying RCTs in MEDLINE: sensitivity-maximising version (2008 revision; Lefebvre 2011). We will revise this strategy appropriately for each database to take account of differences in controlled vocabulary and syntax rules.

We will search for ongoing clinical trials and unpublished trials via Internet searches on the following sites:

ClinicalTrials.gov (www.clinicaltrials.gov);

Searching other resources

We will contact trial authors for additional trials and data. We will also examine the reference lists of eligible studies and other systematic reviews for trials that may have otherwise been missed.

We will not apply any language or publication restrictions.

Selection of studies

Two review authors will independently screen the abstracts of all studies obtained through the search process. Any disagreement will be resolved by discussion. Subsequently, we will retrieve full-text copies of all potentially relevant studies and two review authors will independently assess the eligibility for inclusion. We wil resolve any disagreement by discussion and when necessary, with the third author.

Data extraction and management

For eligible trials, at least two review authors will extract the data using a pre-designed data extraction form. We will resolve discrepancies through discussion or, if required, we will consult a third review author. We will enter data into RevMan 2011 and check for accuracy.

When information regarding any of the above is unclear, we will attempt to contact trial authors to provide further details.

Assessment of risk of bias in included studies

Two review authors will independently assess risk of bias for each trial using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We will resolve any disagreement by discussion or by involving a third review author. The recommended approach for assessing risk of bias in studies included in a Cochrane Review is a two-part tool, addressing seven specific domains namely sequence generation and allocation concealment (selection bias), blinding of participants and providers (performance bias), blinding of outcome assessor (detection bias), incomplete outcome data (attrition bias), selective outcome reporting (reporting bias) and other sources of bias. The first part of the tool involves describing what was reported to have happened in the trial. The second part of the tool involves assigning a judgement relating to the risk of bias for that entry, in terms of low, high or unclear risk. To make these judgments we will use the criteria indicated by Higgins 2011 but adapt them to the addiction field. See Appendix 2 for details.

Measures of treatment effect

Dichotomous data

For dichotomous data, we will present results as summary risk ratio with 95% confidence interval (CI).

Continuous data

For continuous data we will use the standardized mean difference effect size (Hedges' g). Ideally, these effect sizes will be based on means, standard deviations and sample sizes for each condition. However, this effect size can be computed from a range of reported statistical information such as from a t-test, a P value from a t-test, an F-test, regression coefficients, etc. Using this effect size index enables the combination of effect sizes across trials that examine a common construct but measure that construct differently.

Unit of analysis issues

The unit of analysis will be an individual patient. We will consider cluster-RCTs if identified but we will compute effect sizes at the individual patient level.

Cluster-RCTs

We will include cluster-RCTs in the analyses along with individually-RCTs. We will adjust their sample sizes using the methods described in Higgins 2011using an estimate of the intracluster correlation co-efficient (ICC) derived from the trial (if possible), from a similar trial or from a study of a similar population. If we use ICCs from other sources, we will report this and conduct sensitivity analyses to investigate the effect of variation in the ICC. If we identify both cluster-RCTs and individually-RCTs, we plan to synthesise the relevant information. We will consider it reasonable to combine the results from both unless there is non-negligible heterogeneity between the trial designs and the interaction between the effect of intervention and the choice of randomisation unit is considered to be likely.

We will also acknowledge heterogeneity in the randomisation unit and perform a sensitivity analysis to investigate the effects of the randomisation unit.

Dealing with missing data

For included trials, we will note levels of attrition. We will explore the impact of including trials with high levels of missing data in the overall assessment of treatment effect by using sensitivity analysis.

For all outcomes we will carry out analyses, as far as possible, on an intention-to-treat basis, i.e. we will attempt to include all participants randomised to each group in the analyses, and we will analyse all participants in the group to which they were allocated, regardless of whether or not they received the allocated intervention. The denominator for each outcome in each trial will be the number randomised minus any participants whose outcomes are known to be missing.

Assessment of heterogeneity

We will assess statistical heterogeneity in each meta-analysis using the T², I² and Chi² statistics. We will regard heterogeneity as substantial if the I² is greater than 30% and or if the P value for the Chi² test for heterogeneity is less than 0.10.

Assessment of reporting biases

If there are 10 or more included trials in the meta-analysis, we will investigate reporting biases (such as publication bias) using funnel plots and the trim-and-fill method. We will assess funnel plot asymmetry visually. If asymmetry is suggested by a visual assessment, we will perform explanatory analysis to investigate it.

Data synthesis

We will carry out statistical analysis using RevMan 2011. We will use a random-effects model for combining data as the assumptions of the fixed-effects model are unreasonable for this literature. Trials are likely to differ in numerous ways that may affect the underlying treatment effect being estimated such as the specifics of the CBT being implements, the context of the treatment and the unique characteristics of the population. The random-effects model converges on the fixed-effects model as the data becomes homogeneous, so this approach is reasonable and consistent with recommended practice within the meta-analysis literature. We will present the results as the average treatment effect with its 95% CI, and also calculate the estimates of T² and I².

Quality of the evidence

We will assess the overall quality of the evidence for the primary outcome and for adverse effect will be assessed using the GRADE system (Atkins 2004). The GRADE system uses the following criteria for assigning grade of evidence:

High: further research is very unlikely to change our confidence in the estimate of effect;

The grade of evidence is decreased if there is:

Serious (-1) or very serious (-2) limitation to study quality;

Also, the grade of evidence can be increased if there is:

Stong evidence of association - significant relative risk of > 2 (< 0.5) based on consistent evidence from two or more observational studies, with no plausible confounders (+1);

Subgroup analysis and investigation of heterogeneity

If we identify substantial heterogeneity, we will investigate it using subgroup analyses and sensitivity analyses. We will consider whether an overall summary is meaningful, and if it is, use random-effects analysis to produce it.

We plan to carry out the following subgroup analyses:

Gender;

We will use the following outcomes in subgroup analysis:

Abstinence rate measured by urine samples, or self-report, or both.

We will perform these moderator analyses using random effects meta-analytic regression methods or analog-to-the ANOVA, depending on the nature of the moderator variable. These analyses will be performed in Stata using macros developed by David Wilson and available at http://mason.gmu.edu/∼dwilsonb/ma.html

Sensitivity analysis

We plan to carry out sensitivity analysis to explore the effects of trial quality assessed by allocation concealment and other risk of bias components, by omitting trials at high risk of bias for these components. We will restrict sensitivity analysis to the primary outcome.

1. Sequence generation (checking for possible selection bias)

We will describe for each included trial the method used to generate the allocation sequence in sufficient detail to allow an assessment of whether it should produce comparable groups. We will assess the method as:

low risk of bias (any truly random process, e.g. random number table; computer random number generator);

2. Allocation concealment (checking for possible selection bias)

We will describe for each included trial the method used to conceal the allocation sequence and determine whether intervention allocation could have been foreseen in advance of, or during recruitment, or changed after assignment. We will assess the methods as:

low risk of bias (e.g. telephone or central randomisation; consecutively numbered sealed opaque envelopes);

4. Incomplete outcome data (checking for possible attrition bias through withdrawals, dropouts, protocol deviations)

We will describe for each included trial, and for each outcome or class of outcomes, the completeness of data including attrition and exclusions from the analysis. We will state whether attrition and exclusions were reported, the numbers included in the analysis at each stage (compared with the total number of randomised participants), reasons for attrition or exclusion where reported, and whether missing data were balanced across groups or were related to outcomes. Where sufficient information is reported, or can be supplied by the trial authors, we will re-include missing data in the analyses we undertake. We will assess methods as:

low risk of bias: e.g. less than 20% missing outcome data; missing outcome data balanced across groups;

5. Selective reporting bias

For each included trial we will describe how we investigated the possibility of selective outcome reporting bias and our findings. We will assess the methods as:

low risk of bias: where it is clear that all of the trial's pre-specified outcomes and all expected outcomes of interest to the review have been reported;

6. Other sources of bias

We will describe any important concerns we have about other possible sources of bias for each included trial. We will assess whether each trial was free of other problems that could have put it at risk of bias by stating:

low risk of other bias;