Participant characteristics

Participants were males and females of any age and ethnicity.

Diagnosis

Participants were required to meet criteria for gambling disorder/pathological gambling or problem gambling using standardised diagnostic or assessment instruments. These included general clinical interviews based on DSM criteria (APA 2013) or structured clinical interviews, such as the Diagnostic Interview for Gambling Schedule (DIGS; Winters 2002) and the Structured Clinical Interview for Pathological Gambling (SCI‐PG: Grant 2004). We also included studies that employed validated self‐report measures, such as the Problem Gambling Severity Index (PGSI; Ferris 2001), the South Oaks Gambling Screen (SOGS; Lesieur 1987), and the National Opinion Research Centre DSM Screen for Gambling Problems (NODS; Gernstein 1999).

Comorbidities

There were no restrictions on the inclusion of studies utilising samples with comorbid psychiatric disorders. However, studies comprising samples of participants diagnosed with Parkinson’s disease were excluded from this review, as gambling problems in this population can be symptomatic of anti‐Parkinsonian medication (Heiden 2017).

Setting

There were no restrictions on the inclusion of studies based on study setting.

Pharmacological interventions

We considered any type of pharmacological intervention specifically targeting the reduction of gambling symptom severity or behaviour. Several major categories of pharmacological interventions were identified a priori and were used to organise the review. Consistent with all of the available meta‐analyses, we decided to combine mood stabilisers and anticonvulsants into one major category, as both classes of drugs are commonly used to treat mood instability. Indeed, a number of anticonvulsants, including sodium valproate and carbamazepine, have long been recognised in national practice guidelines as mood stabilisers for the treatment or prevention of mood episodes in bipolar disorder (Ceron‐Litvoc 2009; Melvin 2008). Newer anticonvulsants, such as lamotrigine, have also been found to be effective in the treatment of bipolar depression (Calabrese 1999; Goodnick 2007). Topiramate has also been shown to be an effective add‐on treatment for bipolar depression as well as treatment‐resistant depression (McIntyre 2002; Mowla 2011); however, a 2016 Cochrane Review was inconclusive due to the inclusion of few high‐quality studies (Pigott 2016). In contrast, olanzapine was analysed separately, which is consistent with the majority of available meta‐analyses (Bartley 2013; Pallesen 2007). The final major categories of pharmacological interventions therefore included the following.

• Antidepressants, including selective serotonin reuptake inhibitors (SSRIs; e.g. fluvoxamine, paroxetine, sertraline, escitalopram, citalopram, fluoxetine), serotonin and noradrenaline reuptake inhibitors (SNRIs; e.g. venlafaxine, desvenlafaxine, duloxetine), reversible inhibitors of monoamine oxidase (RIMAs; e.g. moclobemide), norepinephrine–dopamine reuptake inhibitors (NDRIs; e.g. bupropion), tricyclic antidepressants (TCAs; e.g. clomipramine, nortriptyline, dothiepin, imipramine, amitriptyline, desipramine, doxepin, protriptyline, trimipramine), noradrenaline and specific serotoninergic antidepressants (NASSAs) (also called tetracyclic antidepressants) (e.g. mirtazapine, mianserin), noradrenaline reuptake inhibitors (NARIs; e.g. reboxetine), monoamine oxidase inhibitors (MAOIs; e.g. tranylcypromine, phenelzine, isocarboxazid, selegiline), melatonergic antidepressants (e.g. agomelatine), serotonin modulators (e.g. vortioxetine), and atypical antidepressants or serotonin‐norepinephrine‐dopamine reuptake inhibitors (SNDRIs) (e.g. nefazodone).

• Opioid antagonists, such as naltrexone, nalmefene, and naloxone.

• Mood stabilisers, including mood stabilisers (e.g. lithium) and anticonvulsants (e.g. topiramate, valproate, carbamazepine, lamotrigine).

• Atypical antipsychotics (e.g. olanzapine, quetiapine, risperidone, aripiprazole, ziprasidone, paliperidone, asenapine).

Comparator interventions

We considered the following control or comparison conditions for this review.

• Placebo: any placebo condition, including active and inert placebo conditions.

• Comparator interventions: one of the aims of this review was to consider the comparative superiority of pharmacological interventions. We thus included comparisons between interventions comprising the major drug categories defined in this review.

Primary outcomes

• Reduction in severity of gambling symptoms: assessed using standardised patient‐ or clinician‐rated measurement tools. These included the Gambling Symptom Assessment Scale (G‐SAS; Kim 2009), the Clinical Global Impression – Severity Scale (CGI‐S; Hollander 1998; Kim 2001b), the South Oaks Gambling Screen (SOGS; Lesieur 1987), and the Yale Brown Obsessive Compulsive Scale adapted for Pathological Gambling (PG‐YBOCS; Pallanti 2005). When a study provided data on multiple measures, we only used one measure. We gave preference to multi‐item patient‐rated measures (e.g. G‐SAS), followed by multi‐item clinician‐rated measures (e.g. PG‐YBOCS) and single‐item clinician‐rated measures (e.g. CGI‐S).

Secondary outcomes

• Reduction in gambling expenditure: assessed using instruments such as gambling diary records, Timeline Follow‐Back interviews (Sobell 1992), visual analogue scales (Tiffany 1993), or single‐item self‐report scales (e.g. amount spent or lost gambling in the past week or month).

• Reduction in gambling frequency: assessed using instruments such as gambling diary records, Timeline Follow‐Back interviews (Sobell 1992), visual analogue scales (Tiffany 1993), or single‐item self‐report scales (e.g. number of sessions or days gambled in the past month).

• Reduction in time spent gambling: assessed using instruments such as gambling diary records, Timeline Follow‐Back interviews (Sobell 1992), visual analogue scales (Tiffany 1993), or single‐item self‐report scales (e.g. number of minutes or hours gambled in the past month).

• Reduction in depressive symptoms: assessed using standardised and validated measures (e.g. Hamilton Depression Rating Scale) (Hamilton 1960).

• Reduction in anxiety symptoms: assessed using standardised and validated measures (e.g. Hamilton Anxiety Rating Scale) (Hamilton 1960).

• Reduction in functional impairment: assessed using standardised and validated measures (e.g. Sheehan Disability Scale) (SDS; Sheehan 1983).

• Responder status: assessed using any categorisation of gambling symptom severity or gambling behaviour (e.g. a score of improved on the CGI‐Improvement Scale (CGI‐I), gambling abstinence (based on gambling frequency), or positive endorsement on items of the Criteria for Control of Pathological Gambling Questionnaire (CCPGQ). When a study provided data on multiple measures, we used only one measure, with preference given to categorical data based on CGI‐I, which was the most commonly used measure.

Dealing with duplicate publications

We identified duplicate publications, listed them with the primary publication, and examined them for any relevant information not mentioned in the primary report. Where multiple publications reported on the same outcome (e.g. gambling behaviour), we used the publication that reported the most complete information (such as means, standard deviations (SDs), and n’s) for each meta‐analysis.

Managing skewed data

We expected skewed distributions for the gambling behaviour outcomes (gambling expenditure, gambling frequency, and time spent gambling), which are frequently characterised by long tails reflecting small numbers of extreme high values (e.g. indicating high gambling losses). In primary studies, numerous strategies may be employed to address skewed data, such as a commonly used logarithmic transformation of raw data. Where transformations occur, the descriptive statistics reported in the primary study may be in original or geometric forms (based on log‐transformed data). Meta‐analyses involving skewed data can be challenging, given that: (a) results from untransformed and log‐transformed data cannot be synthesised (Deeks 2008); and (b) skewed distributions with long tails can also inflate estimates of variability and thus homogenise the SMD.

We managed skewed data by extracting available information on skew and strategies to address skew from each primary study. Although log transformed data were reported in some instances for measures of gambling behaviour, the untransformed statistics were most consistently available and were used initially in meta‐analyses. Where log transformed data was the only information available, we converted these to approximate raw statistics using methods outlined by Higgins (Higgins 2008b).

Studies with multiple treatment groups

Where studies reported comparisons involving multiple treatments across different drug categories defined in this review, we evaluated only one intervention within each meta‐analysis. We considered comparisons involving other treatment types (e.g. alternative categories of pharmacotherapy) from the same study in separate analyses. Where different treatment arms reflected the same drug across multiple doses or different drugs within the same category, but data were reported separately, we pooled data from these groups to provide a single overall mean and SD (Higgins 2008c).

Studies using cluster‐randomised designs

Where cluster‐randomised trials were identified (Higgins 2008c), we extracted the methods used to analyse the data. We used the inflated standard error approach to adjust standard errors for non‐independence of observations (Higgins 2008c). We extracted the degree of non‐independence, as reflected in the intra‐class correlation (ICC), to facilitate adjustments. We assumed the ICC to be 0.05 when it was not reported.

Studies using cross‐over designs

Where cross‐over trials were identified, in which participants were randomly allocated to treatment sequence, we only used the between‐group comparison from the first treatment stage in the analyses.

Missing information on study design and results/statistics

Where information about results or statistics (e.g. means, SDs, effective sample size at each time point) was missing or inadequately reported in the primary study, we examined secondary or duplicate publications for the relevant information before making contact with study authors. If these strategies were unsuccessful, we attempted to obtain the missing statistics from other results (e.g. standard errors, t‐values, F‐statistics). We also obtained approximate estimates of certain statistics (e.g. means) from figures presented in publications. As a last resort, we excluded the study from the specific analysis.

Missing observations from primary studies due to attrition

We determined the consideration of 'completers only' versus ITT data by the information reported, for example if required statistics from the ITT analyses were not reported. Where both types of data were available, we gave preference to the ITT sample. The influence of using completers‐only data informed sensitivity analyses (see Sensitivity analysis).

Clinical homogeneity

The decision to conduct a meta‐analysis and pool results across studies was based, primarily, on the clinical homogeneity of the studies. For studies that were clinically heterogeneous or that presented insufficient information for pooling, we presented a narrative summary of the results.

Statistical homogeneity

We quantified statistical heterogeneity in effect size estimates using the I² statistic, which represents the percentage of total variability across studies that is attributable to between‐study differences (Huedo‐Medina 2006). We also reported the Chi² statistic and associated significance test (P value), but as this lacks statistical power (Deeks 2008), we placed greater emphasis on the I². Despite arbitrary thresholds for I², there are overlapping bands suggesting minor (0% to 40%), moderate (30% to 60%), substantial (50% to 90%), and considerable (75% to 100%) heterogeneity (Deeks 2008). We also qualified interpretation by evaluating the pattern of variability, and whether individual studies indicated beneficial, null, or harmful effects. Where there was strong evidence of true heterogeneity, we considered the pooled effect to a limited best estimate which we qualified through discussion of diversity.

Design

All 17 studies were randomised trials, with one study utilising a cross‐over randomised trial design (Hollander 2005). Thirteen studies had a placebo control group, with three studies comparing multiple doses of the same medication with placebo control (Grant 2006a; Grant 2008; Grant 2010). The remaining three studies compared two or more different drug categories (Dannon 2005a; Dannon 2005b; Rosenberg 2013). Treatment duration ranged from 7 to 96 weeks (average of around 18 weeks). All studies reported collection of outcome data that was scheduled for the end of treatment, with only one study conducting a longer‐term post‐treatment follow‐up (Rosenberg 2013). We did not include these 24‐month follow‐up data in the current review.

Sample sizes

A total of 1193 participants were randomly allocated to groups. The average sample size was 68, ranging from 13, in Hollander 2000, to 233, in Grant 2010.

Setting

The majority of  studies were conducted in the United States (k = 12), followed by Israel (k = 3) and Spain (k = 2). Four trials involved multiple sites or centres (Berlin 2013; Grant 2003; Grant 2006a, Grant 2010). Eleven studies clearly reported that trials were conducted in outpatient settings. The remaining studies did not describe the trial setting.

Participants

All studies utilised adult samples, with a mean age of 42.71 years. The majority of studies included males and females (k = 13), with four trials including male‐only samples (Dannon 2005a; Dannon 2005b; Hollander 2000; Rosenberg 2013). Fong 2008 recruited pathological gamblers who reported a primary problem concerning video poker, and Hollander 2005 restricted their trial to pathological gamblers with a comorbid bipolar spectrum disorder. With the exception of Rosenberg 2013, for which exclusion criteria were not provided, all trials excluded participants with common comorbid mental health disorders. Several trials excluded individuals on the presence of any Axis I diagnosis (Berlin 2013; Blanco 2002; Dannon 2005a; Dannon 2005b; Fong 2008; Grant 2003; Grant 2006a; Grant 2010; Kim 2002), whereas other trials specified exclusions based on the presence of psychotic disorders (Black 2007; Grant 2008; Hollander 2000; Hollander 2005; McElroy 2008; Saiz‐Ruiz 2005), bipolar disorders (Black 2007; Grant 2008; Hollander 2000; Hollander 2005 (bipolar I only); McElroy 2008; Saiz‐Ruiz 2005), substance abuse or dependence (Black 2007; Grant 2008; Hollander 2000; Hollander 2005; Kim 2001a; McElroy 2008; Saiz‐Ruiz 2005), alcohol abuse or dependence (Kim 2001a; Saiz‐Ruiz 2005), personality disorders (Berlin 2013; Kim 2001a; McElroy 2008), or eating disorders (Black 2007). Several studies excluded individuals based on elevated baseline depressive (Berlin 2013; Black 2007; Grant 2003; Grant 2006a; Grant 2008; Kim 2002) and anxiety (Grant 2003; Grant 2006a; Grant 2008; Kim 2002) symptomatology.

All studies required that participants met criteria for gambling disorder/pathological gambling or problem gambling using the DSM criteria or validated assessment measures based on the DSM criteria, with some studies employing multiple measures. The most commonly used measure was the South Oaks Gambling Screen (SOGS) (score of 5 or more; k = 8) (Black 2007; Dannon 2005a; Dannon 2005b; Hollander 2000; Hollander 2005; Kim 2001a; Kim 2002; McElroy 2008), followed by a range of structured clinical interviews for pathological gambling (k = 7) (Berlin 2013; Fong 2008; Grant 2003; Grant 2008; Hollander 2005; McElroy 2008; Saiz‐Ruiz 2005), the National Opinion Research Centre DSM Screen for Gambling Problems (NODS) (score of 5 or more; Black 2007), and the Yale Brown Obsessive Compulsive Scale adapted for Pathological Gambling (PG‐YBOCS) (score of 21 or more; Grant 2010). Seven studies indicated that participants were required to meet DSM‐IV or DSM‐III‐R criteria for pathological gambling, but did not report the specific means of ascertaining diagnoses (Blanco 2002; Dannon 2005a; Dannon 2005b; Hollander 2000; Kim 2001a; Kim 2002; Rosenberg 2013). We identified no studies that comprised only a subset of participants who were eligible for inclusion in the review.

Interventions

In all studies except one (Hollander 2005), pharmacological treatment was delivered to target gambling specifically, rather than associated psychiatric comorbidity. Hollander 2005 delivered pharmacological treatment to assess reduction in both pathological gambling and bipolar spectrum disorder symptoms, although the primary outcomes related to gambling outcomes, whilst the secondary outcomes related to depressive symptoms, affective instability, and impulsivity severity.

Nine studies examined the efficacy of antidepressant pharmacotherapy. Seven evaluated SSRIs, specifically fluvoxamine (Blanco 2002; Dannon 2005b; Hollander 2000), paroxetine (Grant 2003; Kim 2002), sertraline (Saiz‐Ruiz 2005), and escitalopram (Rosenberg 2013). The remainder of the studies examined the efficacy of the NDRI bupropion (Black 2007; Dannon 2005a; Rosenberg 2013). Six studies investigated the efficacy of opioid antagonists, including naltrexone, Dannon 2005a; Grant 2008; Kim 2001a; Rosenberg 2013, and nalmefene (Grant 2006a; Grant 2010). Four studies examined the efficacy of drugs classified within the mood stabiliser category of this review. Specifically, one study examined sustained‐release lithium, a mood stabiliser (Hollander 2005), whilst the remaining studies examined topiramate, an anticonvulsant (Berlin 2013; Dannon 2005b; Rosenberg 2013). Finally, two studies examined an atypical antipsychotic, which was olanzapine (Fong 2008; McElroy 2008).

Outcomes

Sixteen of 17 studies reported measures of gambling symptom severity, with some studies using more than one measure. Fourteen studies used clinician‐administered measures, including the PG‐YBOCS, Berlin 2013; Black 2007; Dannon 2005a; Dannon 2005b; Grant 2003; Grant 2006a; Grant 2008; Grant 2010; Hollander 2000; Hollander 2005; McElroy 2008, and the Clinical Global Impression – Severity Scale (CGI‐S) (Fong 2008; Grant 2003; Grant 2008; McElroy 2008; Saiz‐Ruiz 2005). Nine studies used self‐report measures of gambling symptom severity, including the Gambling Symptom Assessment Scale (G‐SAS), Berlin 2013; Black 2007; Grant 2003; Grant 2006a; Grant 2008; Grant 2010; Kim 2001a; Kim 2002, and the SOGS (Saiz‐Ruiz 2005).

Six of 17 studies assessed gambling expenditure, using various self‐report measures and timeframes. One study used the Timeline Follow Back to measure money wagered per week (Black 2007), and one study used a 100‐millimetre visual analogue scale to measure amount gambled (Saiz‐Ruiz 2005). Blanco 2002 assessed money spent on gambling per week; Fong 2008 assessed money lost per day; Hollander 2005 assessed money lost per week; and Kim 2002 assessed reduction in money spent from baseline to post‐treatment.

Five studies assessed gambling frequency, also using various self‐report measures and timeframes. One study used a 100‐millimetre visual analogue scale (Saiz‐Ruiz 2005), and one study used take‐home diaries to assess weekly gambling frequency (McElroy 2008). Fong 2008 specified that gambling frequency was assessed as the number of days gambled per week, whereas Dannon 2005b only reported number of times gambled without specifying a timeframe. Hollander 2005 assessed gambling episodes per week.

Six studies assessed time spent gambling, again using various self‐report measures and timeframes. One study used the Timeline Follow Back to assess time spent gambling per week in minutes (Black 2007), and one study used take‐home dairies to assess hours spent gambling per week (McElroy 2008). The remaining three studies assessed number of hours spent gambling per week in the previous month (Blanco 2002); average time spent per day (Fong 2008); time spent per gambling in minutes (Dannon 2005a); and time spent per gambling episode in minutes (Hollander 2005).

Fourteen studies reported measures of depressive symptoms, with one study using more than one measure (Fong 2008). The most commonly used measure was the Hamilton Depression Rating Scale (k = 13; Black 2007; Dannon 2005a; Dannon 2005b; Fong 2008; Grant 2003; Grant 2006a; Grant 2008; Hollander 2000; Hollander 2005; Kim 2001a; Kim 2002; McElroy 2008; Rosenberg 2013), followed by the Beck Depression Inventory (Fong 2008), and the Montgomery‐Asberg Depression Rating Scale (Berlin 2013).

Eleven studies reported measures of anxiety symptoms, with all studies employing the Hamilton Anxiety Rating Scale (Berlin 2013; Dannon 2005a; Dannon 2005b; Fong 2008; Grant 2003; Grant 2006a; Grant 2008; Hollander 2005; Kim 2001a; Kim 2002; Rosenberg 2013).

Six studies reported measures of functional impairment, with all studies using the Sheehan Disability Scale (SDS) (Berlin 2013; Black 2007; Grant 2003; Grant 2006a; Grant 2008; Grant 2010).

Fifteen studies reported an assessment of responder status, using various definitions. Responder status was most commonly defined as a score of 1 (very much improved) or 2 (much improved) on the clinician‐rated CGI‐Improvement Scale (CGI‐I) (k = 8; Berlin 2013; Black 2007; Grant 2003; Grant 2006a; Hollander 2000; Hollander 2005; Kim 2001a; Kim 2002). Four studies classified participants as responders if they reported abstinence for a prespecified period of time (Blanco 2002; Dannon 2005a; Dannon 2005b; Grant 2008). One study classified participants as responders when prespecified items on the Criteria for Control of Pathological Gambling Questionnaire (CCPGQ) (i.e. ability to control gambling urges and substantial decrease in gambling problems) were positively endorsed (Saiz‐Ruiz 2005); one study required a reduction (≥ 35%) in scores on the PG‐YBOCS (Grant 2010); and one study classified participants as responders if there was a reduction in PG‐YBOCS scores and a score of 1 or 2 on the CGI‐I (McElroy 2008).

Randomisation

We classified four studies as having a low risk of bias as they described an appropriate method of randomisation. Three of these conducted block randomisation in sizes of eight, using computer‐generated randomisation with no clinical information provided (Grant 2006a; Grant 2008; Grant 2010), and one study used a computer‐generated table of random numbers (Grant 2003). We classified the remaining studies as having an unclear risk of bias as they did not provide sufficient information about the randomisation procedure.

Allocation concealment

We classified only three studies as having a low risk of bias as  they described an appropriate method of allocation concealment. Black 2007 had a pharmacy department prepare the drug and look‐alike placebo and develop the randomisation scheme. Randomisation for all sites in Grant 2003 was completed by an operator with no clinical contact who kept the code during the trial. Finally, allocation concealment in McElroy 2008 involved the use of a research pharmacy performing the randomisation, packaging the study medication, and maintaining integrity of the blinded information. We classified the remaining 14 studies as having an unclear risk of bias as they did not indicate processes used to conceal allocation.

Blinding of participants and personnel

We classified six studies as having a low risk of bias as they clearly and explicitly described the blinding of participants and personnel (Black 2007; Grant 2006a; Grant 2010; Hollander 2005; Kim 2001a; McElroy 2008). We assessed two studies as having a high risk of bias as they indicated that only the rater was blinded to treatment allocation  (Dannon 2005a; Dannon 2005b). Although the remaining nine studies were described as double‐blind trials, we classified them as having an unclear risk of bias as they did not explicitly describe the blinding of participants and personnel.

Blinding of outcome assessors

We classified six studies as having a low risk of bias as they clearly and explicitly described the blinding of the outcome assessors (Black 2007; Dannon 2005a; Dannon 2005b; Grant 2006a; Grant 2008; McElroy 2008). The remaining 11 studies were described as double‐blind trials, but were classified as having an unclear risk of bias as they did not explicitly describe that outcome assessors were blinded.

Industry involvement

We classified 12 studies as having a high risk of bias due to the receipt of financial support from a pharmaceutical company for the conduct of the study (Berlin 2013; Blanco 2002; Dannon 2005b; Fong 2008; Grant 2003; Grant 2006a; Grant 2010; Hollander 2000; Hollander 2005; Kim 2002; McElroy 2008; Saiz‐Ruiz 2005). We classified three studies as having a low risk of bias as they reported no industry involvement (Black 2007; Grant 2008; Kim 2001a). Finally, we classified two studies as having an unclear risk of bias as they did not report where financial support for the study came from (Dannon 2005a; Rosenberg 2013).

Primary outcome

Secondary outcomes

Primary outcome

This comparison comprised data from four studies including 562 participants evaluating nalmefene, Grant 2006a; Grant 2010, and naltrexone, Grant 2008; Kim 2001a. See Table 2.

Secondary outcomes

Primary outcome

Secondary outcomes

Primary outcome

Secondary outcomes

Primary outcome

Secondary outcomes

Primary outcome

Primary outcome

Secondary outcomes

Comparison 11: Antidepressants versus placebo

Random sequence generation: There was evidence from only one study assessed as at low risk of bias for random sequence generation, and findings should be interpreted considering the scarcity of trials at low risk of bias for this domain. In a multicentre, randomised, double‐blind, flexible‐dose, placebo‐controlled trial of 71 participants (Grant 2003), antidepressants (SSRIs) were no more effective than placebo when conditions were compared on gambling symptom severity at the end of the 16‐week treatment: SMD −0.10 (95% CI −0.56 to 0.37) (Analysis 11.1). It was not possible to conduct a meta‐analysis or appraise statistical heterogeneity given that multiple studies were not available.

Allocation concealment: There was evidence from only two studies (110 participants) assessed as at low risk of bias for allocation concealment. The evidence indicated that antidepressants were no more effective than placebo when conditions were compared on gambling symptom severity at post‐treatment: SMD −0.07 (95% CI −0.45 to 0.30) (Analysis 11.2). Study results were generally consistent (I² = 0%). 

Blinding of participants/personnel: There was evidence from only study assessed as at low risk of bias for blinding of participants/personnel, and findings should be interpreted considering the scarcity of trials at low risk of bias for this domain. In a randomised, double‐blind, placebo‐controlled, flexible‐dose study of 39 participants (Black 2007), antidepressants (NDRIs) were no more effective than placebo when conditions were compared on gambling symptom severity at the end of the 12‐week treatment: SMD −0.02 (95% CI −0.65 to 0.61) (Analysis 11.3). It was not possible to conduct a meta‐analysis or appraise statistical heterogeneity given that multiple studies were not available.

Blinding of outcome assessors: There was evidence from only one study assessed as at low risk of bias for blinding of outcome assessors, and findings should be interpreted considering the scarcity of trials at low risk of bias for this domain. In a randomised, double‐blind, placebo‐controlled, flexible‐dose study of 39 participants (Black 2007), antidepressants (NDRIs) were no more effective than placebo when conditions were compared on gambling symptom severity at the end of the 12‐week treatment: SMD −0.02 (95% CI −0.65 to 0.61) (Analysis 11.4). It was not possible to conduct a meta‐analysis or appraise statistical heterogeneity given that multiple studies were not available.

Incomplete outcome data: There was evidence from three studies (144 participants) assessed as at low risk of bias for incomplete outcome data. The evidence indicated that antidepressants were more effective than placebo when conditions were compared on gambling symptom severity at post‐treatment: SMD −0.52 (95% CI −1.04 to −0.01) (Analysis 11.5). The point estimate for the SMD suggested a medium effect and potential benefit of antidepressants. There was evidence of moderate statistical heterogeneity across studies (I² = 57%), with point estimates from two studies suggesting beneficial effects of antidepressants (Kim 2002; Saiz‐Ruiz 2005), and a third study suggesting an effect approaching zero (Black 2007).

Selective reporting: No studies comparing antidepressants with placebo on gambling symptom severity were assessed as at low risk of bias for selective reporting, therefore no analyses could be conducted for risk of bias domain.

Other bias – industry funding: There was evidence from only study assessed as at low risk of bias for other bias (industry funding), and findings should be interpreted considering the scarcity of trials at low risk of bias for this domain. In a randomised, double‐blind, placebo‐controlled, flexible‐dose study of 39 participants (Black 2007), antidepressants (NDRIs) were no more effective than placebo when conditions were compared on gambling symptom severity at the end of the 12‐week treatment: SMD −0.02 (95% CI −0.65 to 0.61) (Analysis 11.6). It was not possible to conduct a meta‐analysis or appraise statistical heterogeneity given that multiple studies were not available.

Comparison 12: Opioid antagonists versus placebo

Random sequence generation: There was evidence from only two studies (223 participants) assessed as at low risk of bias for random sequence generation. The evidence indicated that opioid antagonists were more effective than placebo when conditions were compared on gambling symptom severity at post‐treatment: SMD −0.49 (95% CI −0.79 to −0.20) (Analysis 12.1). The point estimate for the SMD suggested a medium effect and potential benefit of opioid antagonists. Study results were generally consistent (I² = 0%). 

Allocation concealment: No studies comparing opioid antagonists with placebo on gambling symptom severity were assessed as at low risk of bias for allocation concealment, therefore no analyses could be conducted for risk of bias domain.

Blinding of participants/personnel: There was evidence from only two studies (182 participants) assessed as at low risk of bias for blinding of participants/personnel. The evidence indicated that opioid antagonists were more effective than placebo when conditions were compared on gambling symptom severity at post‐treatment: SMD −0.43 (95% CI −0.74 to −0.11) (Analysis 12.2). The point estimate for the SMD suggested a medium effect and potential benefit of opioid antagonists. Study results were generally consistent (I² = 0%). 

Blinding of outcome assessors: There was evidence from only two studies (223 participants) assessed as at low risk of bias for blinding of outcome assessors. The evidence indicated that opioid antagonists were more effective than placebo when conditions were compared on gambling symptom severity at post‐treatment: SMD −0.49 (95% CI −0.79 to −0.20) (Analysis 12.3). The point estimate for the SMD suggested a medium effect and potential benefit of opioid antagonists. Study results were generally consistent (I² = 0%). 

Incomplete outcome data: There was only one study assessed as at low risk of bias for incomplete outcome data, and findings should be interpreted considering the scarcity of trials at low risk of bias for this domain. In a double‐blind, placebo‐controlled study of 77 participants (Grant 2008), opioid antagonists (naltrexone) were no more effective than placebo when conditions were compared on gambling symptom severity at the end of the 18‐week treatment: SMD −0.57 (95% CI −1.10 to −0.04) (Analysis 12.4). It was not possible to conduct a meta‐analysis or appraise statistical heterogeneity given that multiple studies were not available.

Selective reporting: No studies comparing opioid antagonists with placebo on gambling symptom severity were assessed as at low risk of bias for selective reporting, therefore no analyses could be conducted for risk of bias domain.

Other bias – industry funding: There was evidence from only two studies (113 participants) assessed as at low risk of bias for other bias (industry funding). The evidence indicated that opioid antagonists were more effective than placebo when conditions were compared on gambling symptom severity at post‐treatment: SMD −0.47 (95% CI −0.89 to −0.06) (Analysis 12.5). The point estimate for the SMD suggested a medium effect and potential benefit of opioid antagonists. Study results were generally consistent (I² = 0%). 

Comparison 13: Mood stabilisers versus placebo

Random sequence generation: No studies comparing mood stabilisers with placebo on gambling symptom severity were assessed as at low risk of bias for random sequence generation, therefore no analyses could be conducted for risk of bias domain.

Allocation concealment: No studies comparing mood stabilisers with placebo on gambling symptom severity were assessed as at low risk of bias for allocation concealment, therefore no analyses could be conducted for risk of bias domain.

Blinding of participants/personnel: There was only one study assessed as at low risk of bias for blinding of participants/personnel, and findings should be interpreted considering the scarcity of trials at low risk of bias for this domain. In a randomised, double‐blind, placebo‐controlled study of 29 participants with gambling disorder and comorbid bipolar spectrum disorders (Hollander 2005), mood stabilisers (sustained‐release lithium) were significantly more effective than placebo when conditions were compared on gambling symptom severity at the end of the 10‐week treatment: SMD −1.63 (95% CI −2.50 to −0.77) (Analysis 13.1). It was not possible to conduct a meta‐analysis or appraise statistical heterogeneity given that multiple studies were not available.

Blinding of outcome assessors:No studies comparing mood stabilisers with placebo on gambling symptom severity were assessed as at low risk of bias for blinding of outcome assessors, therefore no analyses could be conducted for risk of bias domain.

Incomplete outcome data: There was evidence from two studies (71 participants) assessed as at low risk of bias for incomplete outcome data. The evidence indicated that mood stabilisers were more effective than placebo when conditions were compared on gambling symptom severity at post‐treatment: SMD −0.92 (95% CI −2.24 to 0.39) (Analysis 13.2). The point estimate for the SMD exceeded a large effect and potential benefit of mood stabilisers. There was evidence of large amounts of statistical heterogeneity across studies (I² = 84%), although point estimates from all studies suggested beneficial effects of mood stabilisers. 

Selective reporting: There was only one study assessed as at low risk of bias for selective reporting, and findings should be interpreted considering the scarcity of trials at low risk of bias for this domain. In a double‐blind, placebo‐controlled study of 42 participants (Berlin 2013), mood stabilisers (topiramate) were no more effective than placebo when conditions were compared on gambling symptom severity at the end of the 14‐week treatment: SMD −0.29 (95% CI −0.90 to 0.32) (Analysis 13.3). It was not possible to conduct a meta‐analysis or appraise statistical heterogeneity given that multiple studies were not available.

Other bias – industry funding: No studies comparing mood stabilisers with placebo on gambling symptom severity were assessed as at low risk of bias for other bias (industry funding), therefore no analyses could be conducted for risk of bias domain.

Comparison 14: Atypical antipsychotics versus placebo

Random sequence generation: No studies comparing atypical antipsychotics with placebo on gambling symptom severity were assessed as at low risk of bias for random sequence generation, therefore no analyses could be conducted for risk of bias domain.

Allocation concealment: There was only one study assessed as at low risk of bias for allocation concealment, and findings should be interpreted considering the scarcity of trials at low risk of bias for this domain. In an outpatient, randomised, double‐blind, parallel‐group, flexible‐dose study of 42 participants (McElroy 2008), atypical antipsychotics (olanzapine) were more effective than placebo when conditions were compared on gambling symptom severity at the end of the 12‐week treatment: SMD −0.64 (95% CI −1.26 to −0.02) (Analysis 14.1). It was not possible to conduct a meta‐analysis or appraise statistical heterogeneity given that multiple studies were not available.

Blinding of participants/personnel: There was only one study assessed as at low risk of bias for blinding of participants/personnel, and findings should be interpreted considering the scarcity of trials at low risk of bias for this domain. In an outpatient, randomised, double‐blind, parallel‐group, flexible‐dose study of 42 participants (McElroy 2008), atypical antipsychotics (olanzapine) were more effective than placebo when conditions were compared on gambling symptom severity at the end of the 12‐week treatment: SMD −0.64 (95% CI −1.26 to −0.02) (Analysis 14.2). It was not possible to conduct a meta‐analysis or appraise statistical heterogeneity given that multiple studies were not available.

Blinding of outcome assessors:There was only one study assessed as at low risk of bias for blinding of outcome assessors, and findings should be interpreted considering the scarcity of trials at low risk of bias for this domain. In an outpatient, randomised, double‐blind, parallel‐group, flexible‐dose study of 42 participants(McElroy 2008), atypical antipsychotics (olanzapine) were more effective than placebo when conditions were compared on gambling symptom severity at the end of the 12‐week treatment: SMD −0.64 (95% CI −1.26 to −0.02) (Analysis 14.3). It was not possible to conduct a meta‐analysis or appraise statistical heterogeneity given that multiple studies were not available.

Incomplete outcome data: There was only one study assessed as at low risk of bias for incomplete outcome data, and findings should be interpreted considering the scarcity of trials at low risk of bias for this domain. In an outpatient, randomised, double‐blind, parallel‐group, flexible‐dose study of 42 participants (McElroy 2008), atypical antipsychotics (olanzapine) were more effective than placebo when conditions were compared on gambling symptom severity at the end of the 12‐week treatment: SMD −0.64 (95% CI −1.26 to −0.02) (Analysis 14.4). It was not possible to conduct a meta‐analysis or appraise statistical heterogeneity given that multiple studies were not available.

Selective reporting: No studies comparing atypical antipsychotics with placebo on gambling symptom severity were assessed as at low risk of bias for selective reporting, therefore no analyses could be conducted for risk of bias domain.

Other bias – industry funding: No studies comparing atypical antipsychotics with placebo on gambling symptom severity were assessed as at low risk of bias for other bias (industry funding), therefore no analyses could be conducted for risk of bias domain.

Antidepressants

The nine studies that evaluated the efficacy of an antidepressant provided varying levels of detail regarding the type and number of adverse effects experienced (Black 2007; Blanco 2002; Dannon 2005a; Dannon 2005b; Grant 2003; Hollander 2000; Kim 2002; Rosenberg 2013; Saiz‐Ruiz 2005). Five studies reported the most common adverse effects experienced by participants, with headaches (Blanco 2002; Grant 2003; Hollander 2000; Kim 2002; Saiz‐Ruiz 2005), nausea (Blanco 2002; Grant 2003; Hollander 2000; Kim 2002), diarrhoea/gastrointestinal issues (Blanco 2002; Hollander 2000; Saiz‐Ruiz 2005), insomnia (Blanco 2002; Hollander 2000; Saiz‐Ruiz 2005), dizziness/lightheadedness (Blanco 2002; Hollander 2000; Saiz‐Ruiz 2005), and dry mouth (Grant 2003; Hollander 2000) reported consistently in more than one study. Three studies reported adverse effects for dropouts only (Dannon 2005b; Saiz‐Ruiz 2005), or for dropouts and completers separately (Dannon 2005a). Participants that dropped out more commonly reported diarrhoea/gastrointestinal tract disturbances (Dannon 2005a; Dannon 2005b; Saiz‐Ruiz 2005), dizziness (Dannon 2005a; Dannon 2005b), vertigo (Dannon 2005a), headaches (Dannon 2005b), loss of appetite (Dannon 2005a) and hypertension (Saiz‐Ruiz 2005), whereas completers more commonly reported dry mouth, nervousness, and dizziness (Dannon 2005a). One study did not report common side effects but reported that participants in the bupropion group experienced more headaches, nervousness, stomach discomfort, and dry mouth then participants in the placebo group (Black 2007). Finally, one study did not report the specific adverse effects experienced by participants, instead reporting that these events were minor in nature and that the medications were very well tolerated (Rosenberg 2013). 

There was also variability in the level of detail provided relating to participant dropout due to these adverse effects. Five studies indicated that participants dropped out due to adverse effects, with dropout ranging from 4.3% to 31.3% of participants in the antidepressant conditions (Dannon 2005a; Dannon 2005b; Grant 2003; Kim 2002; Saiz‐Ruiz 2005); one study indicated that there were no dropouts due to adverse effects (Rosenberg 2013); and the remaining three studies did not refer to dropout due to adverse effects (Black 2007; Blanco 2002; Hollander 2000). Finally, two studies referred to differences in adverse effects between the antidepressant and placebo groups, with one study indicating that participants receiving paroxetine experienced more adverse effects (Grant 2003), and another study indicating that there were no differences between the sertraline and placebo groups in adverse effects experienced (Saiz‐Ruiz 2005).

Opioid antagonists

The six studies evaluating the efficacy of opioid antagonists provided varying levels of detail regarding the type and number of adverse effects experienced (Dannon 2005a; Grant 2006a; Grant 2008; Grant 2010; Kim 2001a; Rosenberg 2013). Three studies reported the most common adverse effects experienced by participants, with nausea (Grant 2006a; Grant 2008; Kim 2001a), dry mouth (Grant 2006a; Grant 2008; Kim 2001a), constipation (Grant 2006a; Grant 2008; Kim 2001a), insomnia (Grant 2006a; Kim 2001a), dizziness (Grant 2006a; Grant 2008), headache (Grant 2008; Kim 2001a), and diarrhoea (Grant 2008; Kim 2001a) reported consistently in more than one study. Dannon 2005a reported adverse effects for dropouts and completers separately, with dropouts more commonly reporting vomiting, nausea, dizziness, agitation, insomnia, and orthostatic hypotension, and completers more commonly reporting sleep disturbance, gastrointestinal tract irritation, dry mouth, and subjective sense of mental dullness. Finally, two studies did not report the specific adverse effects experienced by participants, instead reporting that these adverse effects were not unusual, Grant 2010, or were minor in nature (Rosenberg 2013).

There was also variability in the level of detail provided relating to participant dropout due to these adverse effects. Two studies indicated that participants dropped out due to adverse effects, with dropout ranging from 10.4% to 31.6% of participants in the opioid antagonist conditions (Dannon 2005a; Grant 2010); one study indicated that there were no dropouts due to adverse effects (Rosenberg 2013); and the remaining three studies did not refer to dropout due to adverse effects (Grant 2006a; Grant 2008; Kim 2001a). Finally, no studies referred to differences in adverse effects between the opioid antagonist and placebo groups.

Mood stabilisers

The four studies evaluating the efficacy of mood stabilisers provided varying levels of detail regarding the type and number of adverse effects experienced (Berlin 2013; Dannon 2005b; Hollander 2005; Rosenberg 2013). Berlin 2013 did not differentiate between the types of adverse effects reported by participants in the topiramate and placebo groups, but reported that 48.5% of reported adverse effects were experienced by participants in the topiramate group. The most common adverse effects were tiredness and headaches, with most adverse effects classified as mild to moderate in severity. One study reported only the adverse effects for participants who dropped out of the study, citing concentration, dizziness, and vertigo (Dannon 2005b). Hollander 2005 reported detailed adverse effects for the lithium and placebo groups separately, with dry mouth and sedation the most common effects in the lithium group. Finally, Rosenberg 2013 did not report the specific adverse effects experienced by participants, stating only that they were minor adverse effects and that topiramate was very well tolerated. 

There was also variability in the level of detail provided relating to participant dropout due to these adverse effects. Specifically, Dannon 2005b reported that 13.3% of participants dropped out due to adverse effects, and Berlin 2013 reported a reduction in the drug dose delivered due to adverse effects. Of the remaining two studies, one study reported no participant dropout due to adverse effects (Rosenberg 2013), and the other study did not report if there was any dropout due to adverse effects (Hollander 2005). Finally, only one study referred to differences in adverse effects between the mood stabiliser and placebo groups. Specifically, Hollander 2005 indicated that there were no differences in adverse effects between the lithium and placebo groups.

Atypical antipsychotics

The two studies evaluating the efficacy of the atypical antipsychotic olanzapine provided contrasting findings (Fong 2008; McElroy 2008). McElroy 2008 reported only the adverse effects for participants who dropped out of the study, citing pneumonia, sedation, and increased hypomania. In contrast, Fong 2008 reported that there were no serious adverse effects. 

McElroy 2008 reported that 14.3% of participants dropped out due to adverse effects, whilst Fong 2008 reported that there were no adverse effects experienced by participants that could have affected dropout. Finally, McElroy 2008 indicated that there were no differences in the number of adverse effects between the olanzapine and placebo groups, but that more participants dropped out from the olanzapine group due to these adverse effects compared to the placebo group.

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