A Systematic Review and Network Meta-Analysis to Assess

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S Y S T E M A T I C R E V I E W

A Systematic Review and Network Meta-Analysis to Assessthe Relative Efficacy of Antipsychotics for the Treatment

of Positive and Negative Symptoms in Early-Onset Schizophrenia

Rebecca C. Harvey1 • Anthony C. James2 • Gemma E. Shields1

Published online: 22 January 2016

Springer International Publishing Switzerland 2016

Abstract

Introduction Early-onset schizophrenia (EOS) is a seri-ous debilitating disorder with considerable morbidity and a

reduced life expectancy; therefore, early diagnosis and

effective treatments are particularly important. Negative

symptoms are more prominent in adolescents and children

(compared with adults), and are key predictors of worse

functional and clinical outcomes in EOS. Therefore, this

study aimed to explore the relative efficacy of antipsy-

chotics used in the treatment of EOS, with a focus on

studies reporting effectiveness using the Positive and

Negative Syndrome scale (PANSS), a scale that includes

an overall symptom measure, in addition to separate sub-

scales for positive and, importantly, negative symptoms.

Methods A systematic literature review was conducted

using the MEDLINE and Cochrane Central Register of Controlled Trials databases to identify trials conducted in

children and adolescents with schizophrenia, and symptom

control was reported using the PANSS. A Bayesian ran-

dom-effects network meta-analysis was performed, syn-

thesising data for a number of outcomes, including mean

change from baseline in PANSS scores, treatment discon-

tinuation and weight gain.

Results Eleven studies were included in the evidence

synthesis, comprising 1714 patients across eight active

interventions (aripiprazole, haloperidol, molindone, olan-

zapine, paliperidone, quetiapine, risperidone and ziprasi-

done) and placebo. All treatments showed a greater

reduction in total PANSS scores vs placebo; however, only

three interventions (molindone, olanzapine and risperi-

done) were associated with a statistically significant

reduction in total PANSS scores at 6 weeks vs placebo.

Haloperidol had the greatest reduction vs placebo; how-

ever, this result was not statistically significant [mean

difference, -15.6, 95 % credible interval (-35.4, 4.1)].

Haloperidol, olanzapine and risperidone showed a statisti-

cally significant reduction in positive PANSS scores vs

placebo; however, whilst all interventions showed a trend

of reduction in negative PANSS scores vs placebo, no

comparisons were statistically significant.

Conclusions Many of the treatments are efficacious in

controlling symptoms, and all showed a trend of superiority

vs placebo for total, positive and negative PANSS scores,

although only olanzapine and risperidone yielded statisti-

cally significant results vs placebo for both total and pos-

itive PANSS scores. Varying results for discontinuation

and weight gain demonstrate a need to balance efficacy

with side-effect profiles.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s40263-015-0308-1 ) contains supplementarymaterial, which is available to authorized users.

& Rebecca C. Harvey

[email protected]

1 BresMed Health Solutions, North Church Business House,

84 Queen Street, Sheffield S1 2DW, UK

2 Highfield Unit Oxford, Warneford Hospital, Oxford, UK

CNS Drugs (2016) 30:27–39

DOI 10.1007/s40263-015-0308-1

http://dx.doi.org/10.1007/s40263-015-0308-1http://crossmark.crossref.org/dialog/?doi=10.1007/s40263-015-0308-1&domain=pdfhttp://crossmark.crossref.org/dialog/?doi=10.1007/s40263-015-0308-1&domain=pdfhttp://dx.doi.org/10.1007/s40263-015-0308-1


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Key Points

The analysis demonstrates positive outcomes in

terms of symptom control, using the Positive and

Negative Syndrome Scale outcome scale, although

there were no statistically significant results between

treatments for managing negative symptoms.

Rates for discontinuation and weight gain highlight a

need to balance treatment efficacy with side-effect

profiles.

Evidence for the comparative efficacy and safety of

antipsychotic use in the paediatric population is

limited; large randomised controlled trials are

needed to verify the results of indirect treatment

comparisons.

1 Introduction

Early-onset schizophrenia (age of onset before 18 years)

(EOS) is often a severe and debilitating psychotic disorder

with considerable impairments in psychological, social,

educational and occupational functioning and a reduced

life expectancy. Schizophrenia is estimated to affect

1.6–1.9 per 100,000 in the child population, with a steep

rise during adolescence [1, 2]. EOS poses a heavy burden

on the patient, family and health services, accounting for

24.5 % of all adolescent psychiatric admissions (with an

overall admission rate of 0.46 per 1000 for this age range inEngland and Wales) [3].

EOSappears to be a more severe disorder than adult-onset

schizophrenia [4]. In particular, it has been linked to a higher

burden of negative symptoms, in addition to reduced cog-

nitive functioning, lower educational attainment and poorer

response to treatment [5]. Furthermore, treatment can be

problematic in children and adolescents, although a recent

longitudinal study from Melbourne, with an early-interven-

tion service, found advantageous outcomes with early

treatment [6]. Psychological interventions have a positive

impact if delivered before the onset of psychosis in indi-

viduals with attenuatedor transient psychotic symptoms, andare recommended as a first line of treatment for psychosis in

this age group [7, 8]. Antipsychotic (AP) medication is the

mainstay of treatment of psychosis; however, there is limited

evidence of efficacy for APs in this age group [8, 9]. Sys-

tematic reviews indicate efficacy, but with small effect sizes.

For second-generation antipsychotics (SGAs), there are

considerable problems with weight gain, metabolic and

cardiac effects, and a risk of diabetes mellitus [10]. Overall,

the risk of side effects associated with AP treatment for

schizophrenia in this age group is greater than that in adults

[10–12]. This has led to concerns about a potential public

health crisis, with the increased prescription of APs resulting

in increased risks of cardiovascular mortality following

treatment [13, 14].

Hence, there is a pressing need to systematically eval-

uate treatments and their side effects. A previous study by

Leucht et al. compared the efficacy and tolerability of APtherapies in the adult schizophrenia population [15]. That

study found that APs differed substantially in side effects,

with only small but robust differences in efficacy. How-

ever, conclusions reached in the adult population may not

necessarily apply to the adolescent and child population,

who are undergoing major physical growth, including

crucial brain and cognitive changes.

The objective of this review was to explore the relative

efficacy of AP therapies for EOS. While looking at

symptom improvement overall is helpful, there is little

correlation between positive and negative symptoms [16].

Therefore, when studies use a symptom measure thatsummarises both aspects, it means that we cannot infer the

impact of intervention on each symptom group. The

validity of distinguishing between positive and negative

symptoms has been explored and confirmed as a useful tool

for monitoring disease severity and predicting outcomes

[17]. In addition, the literature recognises that negative

symptoms are more prominent in adolescents and children

compared with adults, and are key predictors of worse

functional and clinical outcomes in EOS [18–21]. There-

fore, we focused on studies reporting effectiveness using

the Positive and Negative Syndrome Scale (PANSS), a

scale that includes an overall symptom measure in addition

to separate subscales for positive and negative symptoms.

A systematic literature review (SLR) was conducted to

identify trials conducted in children and adolescents with

schizophrenia that report symptom control and effectiveness

using the PANSS. Following this, a network meta-analysis

(NMA) synthesising data extracted from the final list of

relevant studies was conducted to broaden the evidence base

by providing a different method of comparing pharmaco-

logical interventions. The analysis aimed to assist clinicians

in making decisions on therapeutic approaches to EOS (in

particular, which treatments may be efficacious for patients

with predominantly positive or negative symptoms), and the

results highlight the limitations in the current evidence base.

2 Materials and Methods

2.1 Literature Search

An SLR was conducted in January 2015 using two global

electronic databases (MEDLINE and the Cochrane Central

28 R. C. Harvey et al.


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Register of Controlled Trials), to identify evidence of the

effectiveness of interventions for EOS. Searches were

limited to English language studies, with no restriction

placed on date. Results were limited to clinical trials, with

other types of publication (e.g. reviews and letters) exclu-

ded from initial searches.

Searches identified trials conducted in the EOS popu-

lation. Common search terms included disease-specificterms, e.g. ‘‘schizophrenia’’; trial terms, e.g. ‘‘randomisa-

tion’’; and age-group terms, e.g. ‘‘adolescent’’. Both free-

text and expanded medical subject headings were used to

construct search terms. Strategies varied between the

databases as a result of design and search capabilities.

Spelling variants, synonyms and abbreviations were

included to capture all potentially relevant citations. A full

copy of the search strategy is presented in the electronic

supplementary material. Reference lists of key papers were

checked for articles that the initial search had failed to

identify.

2.2 Inclusion Criteria

Following database searches, primary screening was con-

ducted by reviewers with titles and abstracts of all identi-

fied citations against a set of explicit eligibility criteria. For

citations that appeared to meet the inclusion criteria based

on abstract screening, full articles were obtained and

assessed against the full eligibility criteria. Two reviewers

independently conducted primary (title and abstract) and

secondary (full-text) screening before comparing results.

Any disagreements were discussed and settled with the

assistance of a third reviewer.

Eligibility studies (a) focused on patients aged 18

years and below with a diagnosis of schizophrenia,

schizoaffective disorder or schizophreniform disorder;

(b) conducted two or more arm trials in the disease area;

(c) reported mean (and standard deviation or error)

baseline and changes over time in PANSS symptom

scores (total, positive or negative), where reductions in

PANSS scores are indicative of symptom improvements;

and (d) reported data between 6- and 12-week endpoints

for comparisons to be justified. Where there was evi-

dence that the studies reported results from the same

primary study, the paper reporting these data within the

most relevant time scale (i.e. closest to a 6-week end-

point for primary outcome data) was chosen, or if time

points were equal, the most recently published paper was

chosen, to avoid double counting the data.

Because of the paucity of randomised controlled trials

(RCTs) in this population, non-RCT designs were included

to allow for a broader treatment comparison. A sensitivity

analysis excluding non-randomised studies was planned at

the protocol stage.

2.3 Data Extraction and Quality Assessment

Specific data were extracted using a pre-defined and

pilot-tested data extraction form, including quantitative

results. Standard errors/deviations were extracted where

available. Information on study design and patient char-

acteristics were also extracted to assess heterogeneity in

population characteristics and study designs. Studieswere reviewed for quality and bias risk using the Critical

Appraisal Skills Programme critical appraisal RCTs

checklist [22].

The data extraction process was completed indepen-

dently by two reviewers. Extracted data were then cross-

checked, and any disagreements were discussed and

resolved.

2.4 Data Analysis

2.4.1 Synthesis of Evidence

Because of the availability of multiple interventions used to

treat EOS, it is of interest to determine which treatment is

the most efficacious. An NMA enables relative efficacy to

be estimated between any comparators identified in the

SLR, despite the absence of head-to-head trials. Indirect

comparisons may be useful to compare competing inter-

ventions that have not been compared in a head-to-head

RCT. NMA is an extension of pairwise meta-analysis,

relying upon the use of both direct and indirect evidence.

This combination of evidence produces more precise esti-

mates of relative effectiveness than considering only direct

trial data (where such data exist). Not all studies are

required to be placebo controlled for inclusion within an

NMA. Moreover, synthesis of data from studies comparing

active treatments may provide ‘feedback loops’, which

assist with checking consistency between direct and indi-

rect evidence.

A Bayesian NMA, using Markov chain Monte Carlo

methods was performed to synthesise the evidence base

identified from the SLR. A random-effects model was fitted

to the data, which was selected because schizophrenia, by

its very nature, is expected to occur in a very heteroge-

neous population. The statistical between-study hetero-

geneity that is likely to be present within this population

will be accounted for by the use of the random-effects

model.

Analyses were performed using statistical software,

WinBUGS (version 1.4.3) and R (version 3.1.1). The

models fitted, measuring arm-specific change from baseline

effects, were proposed by Dias et al. in the National

Institute for Health and Care Excellence (NICE) Decision

Support Unit (DSU) Technical Support Document (TSD) 2

[23]. Vague prior distributions were assigned to parameters

Relative Efficacy of Antipsychotics for the Treatment of Early-Onset Schizophrenia 29


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of interest and were in line with those specified in the NICE

DSU TSD 2 [23]. Analyses were based on 10,000 samples

after a burn-in of 50,000 was discarded. This burn-in was

selected after assessing the Brooks–Gelman–Rubin statistic

for two chains with varying initial values [23]. Conver-

gence was observed after approximately 50,000 iterations.

A thinning interval of 100 was chosen to reduce the

autocorrelation between samples and to ensure that thechain was mixing well and was representative of the pos-

terior distribution. Overall goodness of fit of the model was

assessed by examining the total residual deviance. The

total residual deviance of a well-fitting model should be

similar to the number of unique data points included within

the analysis [23]. By adopting a Bayesian approach,

treatment ranking probabilities may be estimated. These

show the probability of each treatment attaining each

possible rank within the network (i.e. first, second, third

and so on) [24].

2.4.2 Outcome Measures

The primary efficacy measure was mean change from

baseline to 6 weeks in total PANSS scores (a symptom

severity scale) [25]. Secondary outcomes were mean

change from baseline to 6 weeks in positive and negative

subscale PANSS scores, weight, odds of all-cause treat-

ment discontinuation and odds of discontinuation because

of adverse events. Studies reporting results at any time

between 8 and 12 weeks post-baseline were used as proxy

where 6-week data were not reported; an approach taken by

Leucht et al., who considered it to be clinically plausible

[15].

2.4.3 Assumptions Regarding the Data

Where a study did not report a measure of variation

around the change in PANSS score, the following

assumptions were made to retain the study in the analysis.

If the study reported a variance of the baseline (V b) and

endpoint (V e) PANSS scores, the variance of the change

(V D) in PANSS scores was calculated using the following

equation:

V D

¼ V b

þ V e

2q ffiffiffiffiffiffiffiffiffiffiffi

V b

V ep ;

where the value of q (within-patient correlation) is rarely

reported, and a conservative correlation of 0.5 was used

to reflect the lack of independence between baseline and

endpoint scores. This is detailed further in the NICE

DSU TSD 2 [23]. For studies that did not a report a

variance of either the change from baseline or the end-

point, the average (mean) value from all other studies

was imputed.

Different doses of the same intervention were not dis-

tinguished in the analysis. For trials that consider the same

intervention at different doses, data were pooled, weighting

by the sample size in each treatment arm to estimate an

overall effect for this treatment within a study.

Sensitivity analyses were performed to exclude partic-

ular studies from the NMA based on any anomalies iden-

tified in the trial. This included the removal of the one non-randomised trial that was identified to examine the impact

on results.

2.4.4 Inconsistency of Evidence

An underlying assumption of NMAs is that direct and

indirect sources of evidence are estimating the same

treatment effects. When this assumption is violated,

inconsistency may be present, which is a discrepancy

between direct and indirect evidence. Inconsistency can

only be checked where both direct and indirect data are

present for any pairs of treatments, i.e. closed loops existwithin the network (not arising from three-arm trials).

Potential inconsistency was evaluated using a node-split-

ting approach, which is only possible within a Bayesian

framework [26].

2.4.5 Presentation of Results

Results are presented in the form of forest plots showing

relative efficacy of all interventions vs placebo. Addi-

tionally, all pairwise treatment comparisons are presented

in tabular format within the supplementary material to

show the relative efficacy between all interventions

included in the network. Point estimates [mean differ-

ence (MD) for continuous outcomes, odds ratios (OR)

for binary outcomes] and 95 % credible intervals (CrI)

are presented. Because a reduction in PANSS scores

(total, positive and negative), lower discontinuation rates

and lower weight change are favourable, point estimates

lying to the right of the line of no difference favour the

intervention over placebo. When the 95 % CrI lies

exclusively to the left or right of this line of no differ-

ence, this indicates a statistically significant result vs

placebo. Treatment ranking probabilities are presented in

the form of rankograms, which show the probability of

attaining each possible rank. The vertical axis represents

the probability, and the horizontal axis shows the pos-

sible ranks in ascending order (equal to the number of

treatments in the network). Lines showing a decreasing

trend indicate more efficacious treatments as they indi-

cate a reducing probability of being ranked lower. All

outcomes are displayed on one figure and are identified

by the line type.



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3 Results

3.1 Search Results

In total, 1613 potentially relevant citations were identified

through database searches. Primary title and abstract

screening resulted in 1174 citations being excluded on the

basis that they clearly did not meet the eligibility criteria of the review. Of the resulting 439 citations accessed in full,

11 were publications of studies meeting the inclusion cri-

teria (Fig. 1).

3.2 Study Characteristics and Quality Assessment

Key characteristics of the included studies are presented in

Table 1. Generally, the studies were quite small, although

this varied, from 7 to 190 patients per treatment arm.

Baseline patient characteristics (such as age and proportion

of male individuals) varied across studies, demonstrating

heterogeneity in the study population.

All but one of the studies randomised patients to treat-

ment arms [28–37]. The risk of bias owing to a lack of

blinding affected two studies [27, 28], and may affect a

third in which the blinding method was unclear [34].

However, the majority of studies satisfactorily reported

blinding and methods, reducing the risk of bias [29–33, 35–37]. There were signs of selective reporting as it was

unclear whether two trials reported all outcomes measured

[29, 32]. Potential bias was suggested by differences

between the baseline characteristics of groups in three

studies, which reported some minor differences between

groups [27, 30, 34]. Five of the studies did not have sig-

nificant differences in the drop-out rates between arms [30,

31, 35–37]; five studies demonstrated differences between

the drop-out rates between arms, but only two studies

reported and explained differences in rates between the

I d e n t i f i c a t i o n

S c r e e n i n g

E l i g i b i l i t y

I n c l u d e d

Records identified through database searching (n=1,715)

Medline (n=383)

Cochrane Central Register of Controlled Trials (n=1,332)

Records after duplicates removed (n=1,613)

Records screened (n=1,613) Records excluded (n=1,174)

Full-text articles assessed for

eligibility (n=439)

Full-text articles excluded

(n=428)

Outcome (n=21)

Study design (n=26)

Population (n=336)

Overlapping patient population (n=8)

Duplicate (n=6)

Lack of detail (n=26)

Unable to access (n=5)

Studies included in synthesis

(n=11)

Fig. 1 Flow diagram of search



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Table 1 Study characteristics

References Study type Time point

post-baseline

(week)

Treatment arm

(mg/day)

N Age,

years

(mean)

Male

patients,

%

Baseline

total

PANSS

Gothelf et al. (2003) [27] Controlled trial 8 Haloperidol

(5–15)

7 17.1 71.4 86.1

Olanzapine

(10–20)

17 16.8 68.4 71.6

Risperidone

(0.5–6)

15 17.0 52.9 90.2

Mozes et al. (2006) [28] Randomised controlled trial 12 Olanzapine

(2.5–20)

12 11.5 41.7 92.75

Risperidone

(0.25–4.5)

13 10.7 38.5 93.85

Findling et al. (2008) [29] Randomised, double-blind, placebo-

controlled trial

6 Aripiprazole (10) 99 15.6 45 93.6

Aripiprazole (30) 97 15.4 63.7 94.0

Placebo 98 15.4 61 94.6

Sikich et al. (2008) [30] Randomised, double-blind, controlled

trial

8 Molindone

(10–140)

40 NR 58 99.7

Olanzapine(2.5–20) 35 NR 71 100.3

Risperidone

(0.5–6)

41 NR 66 103.3

Haas et al. (2009) [31] Randomised controlled trial 8 Risperidone

(0.15–0.6)

131 NR NR 96.4

Risperidone

(0.5–6)

124 NR NR 93.3

Haas et al. (2009) [32] Randomised, double-blind, placebo-

controlled trial

6 Risperidone

(1–3)

54 15.7 30 95.4

Risperidone

(4–6)

50 15.7 37 93

Placebo 54 15.5 35 93.2

Kryzhanovskaya et al.(2009) [33] Randomised, double-blind, placebo-controlled trial 6 Olanzapine(2.5–20) 72 16.1 51 95.3

Placebo 35 16.3 24 95.5

Swadi et al. (2010) [34] Randomised controlled trial 6 Quetiapine

(100–800)

11 NR NR 89

Risperidone

(0.5–6)

11 NR NR 87.09

Singh et al. (2011) [35] Randomised, double-blind, placebo-

controlled trial

6 Paliperidone

(1.5)

54 15.1 30 91.6

Paliperidone

(3–6)

48 15.3 31 90.6

Paliperidone

(6–12)

47 15.5 33 91.5

Placebo 51 15.7 23 90.6Findling et al. (2012) [36] Randomised, double-blind, placebo-

controlled trial

6 Quetiapine (400) 73 15.45 43 96.2

Quetiapine (800) 74 15.45 44 97.0

Placebo 73 15.34 42 96.7

Findling et al. (2013) [37] Randomised, double-blind, placebo-

controlled trial

6 Ziprasidone

(40–160)

190 15.24 56.48 88.9

Placebo 88 15.43 68.69 87.4

NR not reported, PANSS Positive and Negative Syndrome Scale



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arms [28, 33], while in the remaining three studies, this was

unclear [27, 32, 34].

3.3 Data Analysis

3.3.1 NMA Evidence Base

Eleven studies were included in the primary analysis

(change from baseline total PANSS scores) as shown by

the network of evidence (Fig. 2). All studies except Swadi

(2010) were included in the analyses of change from

baseline positive PANSS scores, negative PANSS scores

and weight change (denoted * in Fig. 2) [34]. All-cause

discontinuation was less frequently reported; seven studies

were included in this analysis (denoted ** in Fig. 2). For

all analyses except all-cause discontinuation, eight active

treatments and placebo were included within the network of evidence (Fig. 2).

3.3.2 Assessment of Efficacy

Relative treatment effects vs placebo for change from

baseline to 6 weeks in total PANSS scores are shown by the

forest plot (Fig. 3a). There was evidence to suggest that

three interventions (molindone, olanzapine and risperidone)

were associated with a statistically significant reduction in

total PANSS scores at 6 weeks vs placebo. The most

effective intervention was haloperidol, with an estimated

reduction of 15.6 in total PANSS scores vs placebo; how-

ever, the 95 % CrI for haloperidol showed a great amount of

uncertainty around the true relative effect vs placebo. Thisstatistically non-significant result may be attributed to the

positioning of haloperidol in the network as it is not directly

connected with placebo (Fig. 2). In addition, this result may

be owing to the presence of only one study (which was also

non-randomised) evaluating the effects of haloperidol. The

treatment ranking probabilities are shown in Fig. 4.

Haloperidol has the greatest probability of being the best

treatment (prob = 0.49), followed by molindone

(prob = 0.25). All remaining treatments have probabilities

of less than 0.13. While the point estimate for haloperidol

showed a statistically non-significant reduction vs placebo,

the ranking probabilities are being driven by the distributionof the relative treatment effects and take into account the

uncertainty shown by the 95 % CrI.


baseline to 6 weeks in positive and negative subscale

PANSS scores are shown by the forest plots (Fig. 3b, c).

Haloperidol, olanzapine and risperidone showed a statisti-

cally significant reduction in positive PANSS scores vs

placebo as the 95 % CrIs lie exclusively to the right of

zero. While all interventions showed a trend of a greater

Fig. 2 Network of evidence.

Solid lines represent two-arm

studies, dashed lines represent

three-arm studies; node size is

proportional to the number of

patients treated with

intervention, Asterisk study only

included in total Positive and

Negative Syndrome Scale

analysis; Double asterisk study

included in all-cause

discontinuation analysis



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Fig. 3 Forest plots for all outcomes. AE adverse events, CrI credible interval, MD mean difference, OR, odds ratio, PANSS Positive and

Negative Syndrome Scale



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reduction in negative PANSS scores vs placebo, no com-

parison was statistically significant. Haloperidol and

molindone showed the greatest reduction in negative

PANSS scores vs placebo (MD = -3.42); however, the

respective 95 % CrIs spanned zero. Treatment ranking

probabilities (Fig. 4) show that haloperidol had the greatest

probability of being ranked the best treatment in the net-

work for both positive and negative PANSS scores, with

probabilities of 0.85 and 0.40, respectively.


baseline to 6 weeks in weight are shown by the forest plot(Fig. 3d). Olanzapine, quetiapine and risperidone showed a

statistically significant weight gain vs placebo. Haloperi-

dol, molindone and ziprasidone showed a trend of reduced

weight gain vs placebo; however, there was no statistically

significant difference between these treatments and pla-

cebo. The treatment ranking probabilities (Fig. 4) show

that haloperidol and molindone have the greatest proba-

bilities of being the best treatments (prob = 0.50 and

prob = 0.45, respectively). All remaining treatments each

had probabilities of less than 0.03.

Odds ratios of all treatments vs placebo for all-cause

discontinuation are shown in the forest plot (Fig. 3e). Onetreatment (risperidone) showed statistically significant

reduced odds of discontinuing treatment because of any

reason vs placebo. All treatments showed a trend of lower

odds of discontinuing because of any reason vs placebo; for

risperidone, this is a statistically significant result

[OR = 0.48, 95 % CrI (0.25, 0.84)]. The treatment with

the lower odds vs placebo was haloperidol. However, there

is much uncertainty around this estimate [OR = 0.19,

95 % CrI (0.01, 2.42)]. The treatment ranking probabilities

(Fig. 4) show that haloperidol has the greatest probability

of being the best treatment (prob = 0.67), followed by

quetiapine (prob = 0.16). All remaining treatments each

had probabilities of less than 0.08 of being ranked as the

best treatment. Discontinuation because of adverse events

was also investigated, and all treatments showed a trend of

increased odds vs placebo. However, because of low event

rates in the placebo arm of studies and the presence of only

nine studies estimating eight treatment effects, there is

considerable uncertainty in the results (Fig. S1 in the

Electronic Supplementary Material).Haas et al. investigated the effects of risperidone at two

doses [31]. The authors stated that one of these doses

(0.15–0.6 mg/day) was equivalent to a control arm; hence,

in the following analyses, this arm was reclassified as

placebo vs risperidone to allow this study to be included in

the analyses. Removing this study in a sensitivity analysis

performed on total PANSS scores yielded almost identical

results (additional uncertainty was observed in the pairwise

comparisons), which are presented in the Electronic Sup-

plementary Material (Fig. S2A), A second sensitivity

analysis was performed, removing the one identified non-

randomised study (Gothelf) from the analysis [27]. Con-sequently, haloperidol was omitted from the network.

Results for all remaining treatment comparisons were

almost identical to those when including this study and are

presented in the Electronic Supplementary Material

(Fig. S2B). Despite NMAs relying on the presence of

RCTs, this study was included in the analysis to allow

relative efficacy to be estimated between haloperidol and

other comparators, as it was the only evidence identified

for this treatment.

Fig. 4 Treatment ranking probabilities



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For completeness, all pairwise treatment comparisons for

each outcome are presented in the Electronic Supplementary

Material (Figs. S3–S5). These show the relative treatment

effects for all treatments compared with one another.

Goodness of fit of the model measured by the totalresidual deviance is presented in Table 2. The number of

individual data points included in each model is compa-

rable to the total residual deviance obtained from each

analysis. As such, the models appear to be a reasonable fit

to the data for each outcome. Removal of Haas et al. in a

sensitivity analysis did not improve model fit according to

the total residual deviance [31].

There is evidence of mild-to-moderate heterogeneity in

the effects of interventions between studies. This result is

expected because of the heterogeneous population under

investigation in these analyses. Inconsistency was checked

for the primary outcome (total PANSS) using a node-splitting approach. Only three pairwise treatment compar-

isons could be evaluated for inconsistency, relying on the

presence of feedback loops within the network of evidence.

No statistically significant inconsistencies were detected

between the direct and indirect evidence.

4 Discussion

This study, using an NMA, aimed to provide a comparison

of the efficacy profiles of different AP medications for the

treatment of EOS, with data from both direct and indirecttrial comparisons. Changes in PANSS scores can demon-

strate the efficacy of a treatment in maintaining symptom

control; however, rather than looking at total PANSS

scores, we chose to first analyse the positive and negative

symptom scores. These are useful clinical indicators, par-

ticularly in view of the poor treatment response for nega-

tive symptoms to date. While acknowledging the

uncertainty in the results, haloperidol showed a trend of

being the most efficacious intervention for reducing

positive PANSS scores followed by olanzapine and

risperidone (all comparisons vs placebo were statistically

significant). Furthermore, both risperidone and olanzapine

showed positive effects in treating negative symptoms,

which fell just short of statistical significance, and were

considerably greater than the effects of other medications.

Considerable concern has been expressed by clinicians,

academics and organisations such as NICE over the sideeffects of SGAs, in particular weight gain, which can have

a far greater impact on the adolescent population [10, 38].

Weight gain is a risk factor not only for obesity, which by

itself is associated with a reduced quality of life, but also

for metabolic syndrome and diabetes [39, 40]. However,

sufficient data were available from 11 trials to allow

analysis. Three treatments showed a statistically significant

weight gain at 6 weeks vs placebo. Olanzapine showed the

greatest weight gain vs placebo, whereas molindone and

haloperidol showed the least likelihood to cause weight

gain, and indeed showed a trend of reduced weight gain

compared with placebo. These findings are reflected inrecent clinical guidelines, as olanzapine has been recom-

mended only as a second-line treatment option because of

the significant weight gain associated with treatment [41].

Data for all-cause discontinuation were analysed as a

proxy measure for tolerability. Risperidone showed statis-

tically significant lower odds of discontinuing treatment

when compared with placebo. All other treatments showed

a trend of lower odds of discontinuing when compared with

placebo, although results were not statistically significant.

All active treatments were found to be associated with

treatment discontinuation because of adverse events;

olanzapine was associated with the highest odds of dis-

continuation because of adverse events. However, uncer-

tainty around this estimate means that it should be

interpreted with caution.

The finding from this study that haloperidol, a first-

generation antipsychotic (FGA), is the most efficacious AP

raises some interesting questions, particularly in light of the

serious weight and cardio-metabolic side effects associated

with the use of SGAs; these problems have previously been

described as an emerging health crisis [42]. A Cochrane

review found no difference in efficacy between FGAs and

SGAs but notable and important differences in side-effect

profiles [12]. Given the higher rates of extrapyramidal side

effects (EPSE) with FGAs, some of which are serious long-

term complications, such as tardive dyskinesia, it may

nonetheless be appropriate to reconsider the use of FGAs

as part of a treatment plan. For instance when weight gain

is evident or there is an increased risk of side effects such

as diabetes, abnormal lipid levels or impaired glucose

tolerance.

Comparison of our results with the NMA of APs

conducted in adults is informative [15]. The study

Table 2 Goodness-of-fit statistics

Outcome Total

residual

deviance

No. of data points

included in the

model

Total PANSS score 26.1 24

Positive PANSS score 20.6 22

Negative PANSS score 20.8 22Weight gain 20.0 22

All-cause discontinuation 16.0 15

Sensitivity analysis: total PANSS

score (removal of Haas et al.

[31])

19.9 22

PANSS Positive and Negative Syndrome Scale



11/13

conducted in the adult population was much more com-

prehensive and included 212 trials, with data for 43,049

participants, which is considerably more than the 11

studies and 1714 participants available here. This high-

lights, once again, the poor evidence base for APs in the

adolescent population. However, the interesting sugges-

tion coming from both studies is to consider broadening

the range of medications used, depending on the clinicalneeds of the patient, rather than limiting choice to the

dichotomous classification of APs into first- and second-

generation groupings.

There are a number of limitations to the study. The

usefulness of existing trials completed in the paediatric

population is often restricted by their quality, small sample

numbers and heterogeneity among the study populations

[43]. Trials demonstrate efficacy of symptom control;

however, there is a paucity of data on the side effects of

treatment and longer-term health impacts, demonstrating a

need for more research. The potential bias identified in the

studies reduces the reliability of the data used to form thenetwork, and some of the bias (e.g. signs of selective bias

and differences in baseline characteristics) may lead to

incorrect interpretations around treatment effect. Small

sample numbers in the trials may be due to hesitation (in

particular ethical considerations) in conducting clinical

trials in a younger populations [44]. The literature search

was not restricted to RCTs, acknowledging the lack of data

in this population, and thus widened the evidence base,

which places the study at a higher risk of bias. Addition-

ally, NMAs are susceptible to publication bias, with

detrimental effects of publication bias being identified in

previous psychiatric studies [45].

There is considerable uncertainty in the relative treat-

ment effects, most likely because of the limited number of

studies included in the network. Many of the treatment

comparisons are informed by only one trial. It has previ-

ously been noted that there is often a substantial response

in the placebo arm of trials for psychiatric interventions,

which may therefore limit the reliability of NMA results

[45]. Additionally, a non-RCT was included in the analysis

to allow comparisons to be made with haloperidol. Inclu-

sion of the study (Gothelf et al.) enabled relative effects vs

haloperidol to be estimated [27]. While a sensitivity anal-

ysis excluding this study was performed, with results

remaining consistent, NMAs do rely upon the synthesis of

RCTs only. Another limitation with the evidence base is

the assumption that risperidone (0.15–0.6 mg) is equivalent

to placebo. This assumption was made so this trial could be

included within the analyses, thus providing more evidence

on the higher dose of risperidone, given the paucity of data

in this evidence base. In addition, as the typical starting

dose is 0.5 mg, it is stated that this low dose is seen as a

‘‘pseudo-placebo’’ [46].

The study also includes few outcomes owing to lim-

itations in the data (a lack of reporting of all possible

outcomes) and research constraints. An increase in the

number of studies available should report a greater

number of outcomes (e.g. EPSE), which will allow for

more in-depth research. In addition, this review focused

on the PANSS outcome scale for assessing response to

treatment as this allowed us to differentiate between thepositive and negative symptoms in schizophrenia. How-

ever, this meant that some treatments could not be

included because trials studying these treatments did not

use PANSS as the tool to measure symptom control, and

they therefore did not meet the inclusion criteria (e.g. a

trial was identified with clozapine, but had to be

excluded as the outcome was not reported using

PANSS). There are a number of other scales, including

the Brief Psychiatric Rating Scale and the Clinical

Global Impression. To widen the scope of the research,

it would have been possible to widen the criteria and to

make assumptions around the relationship between out-come measures. However, this would have prevented the

separate analysis of the positive and negative subscales,

and would have relied on using the standardised MD,

which also has limitations (e.g. outcome measures would

need to be deemed similar enough to combine and

interpretability of results could be challenging). As it

stands, decision makers need to carefully consider the

treatments available in their locality and whether the

study is reflective of the commonly used treatments.

Finally, this study focuses on pharmacological treat-

ments. There is an emerging evidence base for the effec-

tiveness of psychological interventions, although no studies

were identified during the review that focused on these

alternative treatments.

5 Conclusion

Evidence for the comparative efficacy and safety of AP use

in the paediatric population is limited, and results should be

interpreted cautiously. The evidence does demonstrate

favourable outcomes in terms of symptom control, using

the PANSS outcome scale; however, it does also highlight

a lack of statistically significant effects on negative

symptoms, increased weight gain and a lack of quantifiable

data to assess the risk of other side effects of treatment

more comprehensively.

Clinical strategy will involve a trade-off between the

efficacy of an AP and the likely side effects resulting from

treatment. This has to be seen as a major consideration, not

only because of the serious side effects seen with SGAs

(predominantly weight gain and associated metabolic

effects), and with FGAs (EPSE, including tardive



12/13

dyskinesia), but also because of the high rate of discon-

tinuation rates for APs in general.

Acknowledgments We thank Rachel Brown, a Clinical Lead

Pharmacist, for advising on the dosing of interventions included in the

analyses.

Author contributions G E. Shields and R. C. Harvey designed and

carried out the research, with A. C. James providing clinical valida-tion. G. E. Shields undertook the systematic review, with R. C. Har-

vey acting as the second reviewer. R. C. Harvey undertook the

statistical analysis. G. E. Shields, R. C. Harvey and A. C. James wrote

the first draft of the manuscript. All authors contributed to and have

approved the final manuscript.

Compliance with Ethical Standards

Conflicts of interest R C. Harvey, G. E. Shields and A. C. James

state that they have no conflicts of interest.

Funding No funding was received for this research.

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A Systematic Review and Network Meta-Analysis to Assess

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