IACAPAP Textbook of Child and Adolescent Mental Health

Chapter

A.6

INTRODUCTIONFEASIBLE EVIDENCE-BASEDPRACTICE IN CHILD AND
ADOLESCENT MENTAL HEALTHJohn Hamilton

John Hamilton MD, MSc

Teaching Associate in Childand Adolescent Psychiatry,
Cambridge Health Alliance,
Harvard Medical School,
Cambridge, Massachusetts,
USA
Katsushika Hokusai

Conflict of interest: nonereported.

This publication is intended for professionals training or practicing in mental health and not for the general public. The opinions
expressed are those of the authors and do not necessarily represent the views of the editors or IACAPAP. This publication seeks todescribe the best treatments and practices based on the scientific evidence available at the time of writing as evaluated by the authorsand may change as a result of new research. Readers need to apply this knowledge to patients in accordance with the guidelines andlaws of their country of practice. Some medications may not be available in some countries and readers should consult the specific drug
information since not all dosages and unwanted effects are mentioned. Organizations, publications and websites are cited or linked toillustrate issues or as a source of further information. This does not mean that authors, the editors or IACAPAP endorse their content
or recommendations, which should be critically assessed by the reader. Websites may also change or cease to exist.
©IACAPAP 2021. This is an open-access publication under the Creative Commons Attribution Non-commercial License. Use,
distribution and reproduction in any medium are allowed without prior permission provided the original work is properly cited andthe use is non-commercial.
Suggested citation: Hamilton J. Feasible evidence-based practice in child and adolescent mental health. In Rey JM, Martin A (eds),
JM Rey’s IACAPAP e-Textbook of Child and Adolescent Mental Health. Geneva: International Association for Child and Adolescent
Psychiatry and Allied Professions 2021.

Evidence-based practice A.6

1

IACAPAP Textbook of Child and Adolescent Mental HealthT

his chapter shows how to improve the alignment between what you do inyour practice and good evidence. Consider Alex. Fourteen-years-old, he isattending high school and tried to hang himself at home with his belt. His
brother heard the labored breathing and ran into his room. He was hospitalized fora few days, but a week later was discharged and came to see you. What are feasible
treatments which align with reliable, low-bias evidence?
In choosing treatment, this chapter will emphasize the usefulness of goingdirectly to aggregated evidence from multiple randomized controlled trials. There areseveral ways to make sense quickly of aggregated data, including meta-analyses
and systematic reviews, with their highly useful visual summaries of results. For
Alex’s condition, a search online for such aggregated evidence about treatment
for adolescents who attempt suicide brings up Ougrin’s and colleagues’ workfrom 2015. They offer a visual summary—often a great place to start—of theirconclusions after examining 19 of the most relevant studies. Their visual summary
below (Figure A.6.1) condenses results comparing intervention groups to control
groups for self-harm, defined as both non-suicidal self-injury, such as cutting
without intent to die, as well as suicide attempts. The overall therapeutic result isquite modest with a number needed to treat (NNT) of 14: fourteen youth need to
be treated in order to prevent a single episode of self-harm (either a suicide attempt
or a non-suicidal self-injury).

••

Do you havequestions?
Comments?

Click here to go to the
Textbook’s Facebook
page to share yourviews about thechapter with otherreaders, question theauthors or editor andmake comments.

We have two sorts of evidence now. First, we know considerable detailabout Alex from a clinical interview regards his family, his development, current
symptoms and their context. Second, we have Ougrin’s summary of 19 intervention
studies dealing with adolescents who harm themselves. And finally, most of us

Figure A.6.1 A ‘forest plot’ is the conventional term for this plot, showing the difference in risk for selfharm or suicide in treated adolescents versus in control adolescents summarizing results from 19 different
randomized controlled trials. The ‘forest’ comes from the image of the horizontal bars, representing theconfidence intervals for the risk difference between treated and control subjects in each study. (Wouldn’t‘rowers crossing the finish line’ be a better description than ‘forest’?). The ‘black diamond,’ with its tighter
confidence intervals, summarizes the results overall, its width representing confidence intervals.**Ougrin et al, 2015; with permission.

Evidence-based practice A.6

2

IACAPAP Textbook of Child and Adolescent Mental Healthclinicians can say what we might do at this point—we have ideas about how wemight proceed. We may have seen children like Alex and successfully treated them.
There are many individual characteristics of Alex as a person that appear relevant: heis sensitive, intelligent, used to play tennis often with his brother and mother, feels
alienated from his father, and looks scared. He has several important friendships andis uncertain about his burgeoning sexuality. These observations are useful evidence
for Alex’s treatment that might well be included in a psychodynamic formulation.
To be effective, the challenge is to make use of both sorts of evidence, the meta-analyticaland the highly personal, the statistical aggregates and the subtle observations of his
face and behavior. The reward is being able to improve the alignment between
your work and the ever-increasing knowledge-base as experiences and studies
accumulate around the world while staying attuned to Alex himself.
Feasible evidence-based practice finds aggregated evidence by going straightto ‘the top of the evidence pyramid’ where meta-analyses and systematic reviews
reside, and then looking for a visual summary of the results. The outcome is a
startling view over the ‘evidence-scape’—all or nearly all relevant randomized
controlled trials (RCTs) included as well as a visual summary of the primary
outcome for each RCT. Using aggregated data is feasible because it is quick. A
single figure such as the forest plot in figure A.6.1 contains a remarkable amountof information.
This chapter tries to be useful to clinicians arriving with a spectrumof expertise and experience in evidence-based thinking, from the beginner tointermediate and advanced. After this introduction, the chapter has a section—
basics—to teach fundamental ideas from statistics and epidemiology and may well
be skipped by students and practitioners who have a firm grasp of basic statistics
and of terms like standard error of the mean, odds ratio, effect size, confidence
intervals, number-needed-to-treat, systemic bias versus random error, dichotomous
versus continuous variables, and other basic concepts. The goal is to offer cliniciansfrom multiple backgrounds an opportunity to become familiar with fundamental
statistical ideas and terms essential to understanding evidence-based practice. The‘basics’ section can also be skipped by beginners in statistics and used simply as areference as needed to understand later sections.

After a ‘mental hike’ up the(imaginary) evidence-based
practice pyramid, practitioners
enjoy the view at the ‘top of
the evidence pyramid’ looking
out over the data summarizedvisually in meta-analyses andsystematic reviews.

Evidence-based practice A.6

3

IACAPAP Textbook of Child and Adolescent Mental HealthDistillation and Matching
In distillation and matching, all manuals
describing treatments proven to work well fora given syndrome are ‘distilled’ into their mostcommon components, also called ‘elements.’
Therapists then use these common elements(common, because they are shared by multiple
manuals). An analogy is distilling whiskey:
distilling multiple bulky manuals to produce
something more potent and succinct—a short listof common elements. These common elements
are then ‘matched’ to patients with similar
symptoms.

The next section is how to understand and use methods of aggregating datasuch as:••••Meta-analyses
Systematic reviews
Network meta-analyses, and‘Boiling down’ psychosocial interventions from multiple successful
manuals into those elements which make them successful (called‘distillation and matching of core components’).

For busy practitioners, these data summaries from multiple randomized
controlled trials into a single document, are priceless, allowing us to see in a single
visual display exactly what we want to know: what works and what doesn’t.
Let’s turn now to fundamental concepts from statistics and epidemiology.
These concepts are essential in understanding results from randomized controlled
trials and aggregated results from multiple RCTs, like meta-analyses and systematic
reviews. How to help Alex will be our guide.

BASICSThis section presents fundamental concepts from epidemiology and statistics
that may be new to some readers and an unnecessary review for others. Consider
simply visually scanning it for now. Any parts look interesting…? It can also be
skipped for now and used for reference later. Do not get bogged down into reading
it straight through unless it captures your interest.

What is ‘Good Evidence’?
When a 12-year-old boy and his family walk in for the first time, a goodtherapist is in part like a good detective—looking for evidence. Does the boy lookanxiously to his mother for reassurance? Does he look annoyed and would rather
not be there? How do the parents respond when asked about their family histories?
As the consultation progresses, does the family have an avoidant attachment stylewith reluctance to engage and a distant quality? Does the boy engage with thetherapist when seen alone? Subtle observations are good evidence which is essential topractice. Staying alert to such subtle interpersonal cues is essential.

Evidence-based practice A.6

4

IACAPAP Textbook of Child and Adolescent Mental HealthThis chapter uses the term ‘good evidence’ in a different way, usually referring
to results of treatment trials for specific disorders, especially trials that include
randomization of participants into a treatment group and a control group—as
well as ‘blinding’ of participants, raters, and the treatment team, with the goal ofminimizing bias and random error. The ‘good evidence’ referred to in this chapter
supplements the good evidence clinicians gather during the clinical interview.

Bias and Random Error
Bias interferes with validity—the degree to which a study measures what
it intends to measure—by creating systematic (non-random) deviations from theunderlying truth (Guyatt & Rennie, 2002). Bias is different from random error.
Random error is diminished by increasing sample size, whereas bias can remain ina sample of any size. Consider the targets (a) through (d) in Figure A.6.2 where
the bull’s eye in the center marks the true effect. In study (a) there was little or nosystematic deviation from the true effect (little or no bias), as well as little scatter(random error). In (b), there was also little bias but much random error, and in (c)much bias but little random error. In (d) there was much bias and random error(Kabai, 2021).

Finding Evidence with Low Bias and Low Random Error
The most useful evidence to guide clinical decisions is evidence with low bias
and low random error. There are many ways bias can be introduced into an RCT:
missing outcome data, bias in selecting what is reported (e.g., ignoring difficultside-effects), and bias in measurement of the outcome (e.g., raters of the outcome
suspect that a child is on a medication rather than placebo and, therefore, change
their ratings to achieve a desired result).
Figure A.6.4 shows a graphic summary of potential sources of bias reported
in RCTs making up a meta-analysis of studies using CBT to treat anxiety inchildren done by the Cochrane group (James et al, 2015). Cochrane reviews arenotoriously tough about bias, much to the chagrin of many researchers, yet boththe verbal summary and the plot summarizing the statistics of this meta-analysis
Figure A.6.2
Examples of bias and of random
error in data samples (Kabai, 2021).

Evidence-based practice A.6

Figure A.6.3
Schematic representation of
random error and bias (Kabai, 2021).

5

IACAPAP Textbook of Child and Adolescent Mental Health

Figure A.6.4 A Cochrane review’s visual summary of sources of bias in the RCTs aggregated into asystematic review of CBT interventions for childhood anxiety**James et al, 2015.

conclude that CBT shows a significant effect. This is not unusual. Although
there are potentially significant biases in some studies (e.g., regards how well theblinding was done) the result is significant, useful, and clearly stated. While thereview concludes ‘the evidence is poor,’ the reviewers are referring to the remaining
risk of bias. Yet this Cochrane summary of evidence about the effectiveness of
CBT in treating anxiety disorders in children is among the best evidence anywhereshowing that CBT is an effective treatment for children with anxiety disorders. Do
not be deterred from using treatments showing a significant effect in a Cochranereview even if the review concludes with statements like ‘the evidence is poor.’‘Poor’ is simply commenting on potential uncertainties uncovered by the extremely
thorough Cochrane review processes. ‘Poor’ in this context means that extensive
further studies done with attention to those sources of bias may achieve a different
result. ‘Poor’ can occur in a Cochrane description of what is the best available
evidence. Cochrane reviews are picky but do not be deterred.

ESSENTIAL STATISTICS FOR EVIDENCE-BASEDPRACTICE
A few basic statistical concepts are all that is needed to understand many
core ideas used in evidence-based approaches. First, recall the basic normal or
Gaussian curve which describes the distribution of many aspects of nature, andwhich can be derived mathematically using probability theory. The most common
value is the same as the mean, represented below in Figure A.6.5 by the Greek
letter mu (µ).
Standard deviation (SD) is a measure of variability: it reflects how muchvariation or scatter there is from the mean value (average). A small SD relative tothe mean indicates a distribution where data points are closely clumped together(a and c in Figure A.6.2). A large SD relative to the mean indicates a distribution
where data points are spread out over a large range (b and d in Figure A.6.2). SD
is sometimes described as the typical amount that cases differ from the mean. In a‘normal’ distribution of values, two out of three cases are expected to fall withinplus or minus one SD of the mean.

Evidence-based practice A.6

6

IACAPAP Textbook of Child and Adolescent Mental Health

Figure A.6.5 Plot of a normal (Gaussian)
distribution having a mean value represented by the
Greek letter mu (µ) and a standard deviation, delta(σ), represented by each colored band. Note thatslightly over one-third (34.1%) of the population ofthis distribution lies within one standard deviation
above the mean; slightly over one-third (34.1%) alsolies within one standard deviation below the mean.
Only about 2% of the population lies within 2 or more
standard deviations above the mean, and only about
2% lies within 2 or more standard deviations below
the mean (Wikipedia).

Effect size (ES) in a research study measures how big the effect of a treatment
is relative to the natural scatter in the populations studied. Effect size is also called
the standardized mean difference (SMD) between the two groups in a study if
the primary outcome variable is continuous rather than categorical. Technically,
this is measured as the ratio of the difference due to treatment to the pooled
standard deviation. Pooled refers to combining figures from both the control andexperimental group to create a pooled standard deviation.
Effect size= [Mean of experimental group] - [Mean of control group][Pooled standard deviation]
Absolute risk reduction (ARR) is a measure used to compare two different
alternatives: how much does one treatment reduce the risk of a specified ‘bad’
outcome (like a depressed youth staying depressed) compared to either analternative treatment or placebo? For example, in the Treatment for Adolescentswith Depression Study (TADS; March et al, 2004), at 12 weeks, 65.2% of
adolescents receiving placebo were not rated as improved or very much improved
compared with only 39.4% of adolescents receiving fluoxetine. Therefore, the ARRof not improving for youth with major depressive disorder who took fluoxetine
compared to placebo was 65.2 - 39.4 = 25.8%. In other words, fluoxetine reduced
the risk of not improving considerably: 25.8%, or about 1 out of 4.

Figure A.6.6 Data from this randomized controlled trial shows that, relative to the control group, theexperimental group after treatment has improved: it has moved to the right 0.73 standard deviation
units. Therefore the effect size is 0.73.

Evidence-based practice A.6

7

IACAPAP Textbook of Child and Adolescent Mental HealthAbsolute risk reduction is helpful for two reasons. First, as discussed below,
the ARR can be used to compute the number needed to treat (NNT). The formula issimply NNT= 1/ARR with the result rounded to the next whole number. Second,
the ARR does not exaggerate the treatment effect as does the relative risk reduction(RRR) which is simply computed as the risk in the treatment group divided by therisk in the control group. In TADS, the relative risk reduction when comparing
outcome for youth receiving fluoxetine to youth receiving placebo is the riskof depressive symptoms being rated as not significantly improved in the group
receiving fluoxetine divided by the risk for youth not being significantly improvedin the placebo group, or 39.4 / 65.2 = 0.60, generally expressed as 60%. Notethat this figure is quite different from the absolute risk reduction as calculated in
the paragraph above (25.8%). In general, ARR is a more useful statistic than the
RRR.
Confidence interval (CI). Rather than beginning with a definition, a
specific example might be more useful to gain a feel for this important concept.
Suppose the minister of health tells you she is concerned about depressed teenagers
and she asks you to determine the average (i.e., mean) level of symptoms ofdepression in 13-year-old children in the large city where you live. You accept
the challenge and begin the project by arranging for 13-year-olds from all overthe city to complete an inventory of depression symptoms. The mean score is39.8 on the 49 completed questionnaires. You calculate the standard deviationand it is 9.0. The minister wants you to estimate, based on your data from the 49
returned questionnaires, how close is your mean to the true value—also called the
population value. That is, if it were possible to assess all 13-year-old children in
the city with this depression scale, this would presumably be very close to the true
value. You can see, therefore, that although the true or population value is a usefulconcept, it is often not practical to test all the children. Yet, simply by chance you
may have ended up with a higher proportion of unhappy children or of unusually
happy children in your small sample than is present in the city in general, even if
you managed to avoid being biased in your sampling (like choosing only students
from the same school, or only students in a certain class).
You tell the minister the mean score in your sample is 39.8, as a roughestimate. She, however, wants to know more, and asks, ‘How rough an estimate isthat? What is the highest true value? And the lowest?’ You answer, ‘Yes, I can give
you a range or interval but it depends on how often you want me to be right. Do
you want me to be right like 80% of the time that I give you these estimates? Or
95%? Or even 99%? The more you insist that the answer I give you does containthe true answer for the population, the wider the interval will be.’ The minister

Figure A.6.7 An important factassociated with the "1.96" inthe formula for calculating 95%
confidence intervals: 95% of thearea of a normal distribution iswithin 1.96 standard deviation unitsof the mean.

Evidence-based practice A.6

8

IACAPAP Textbook of Child and Adolescent Mental Healthsays, ‘OK. I want your answers on these estimates to be correct 95% of the time.’
The minister has just asked for the 95% confidence interval of your calculatedmean.
A statistician can now help. She tells us that a good estimate for calculating
confidence intervals in normal distributions is as follows, where N is the number ofsubjects generating the data (49 in this case) and SQRT means square root:
Confidence interval, upper bound = Calculated mean + 1.96 x SD/ SQRT N
Confidence interval, lower bound= Calculated mean - 1.96 x SD/ SQRT N
Therefore, the confidence intervals are:CIupper= 39.8+1.96x9.0/SQRT 49 = 39.8+1.96x9.0/7 = 39.8+2.52 = 42.32
CIlower = 39.8-1.96x9.0/SQRT 49 = 39.8-1.96x9.0/7 = 39.8-2.52 = 38.28
Now you have an answer for the minister.
In summary, CIs are expressed with three numbers, the first being themean—our best estimate of the true value. The other two numbers state the lower
bound and the upper bound of the interval. The more certain we want to be, thewider the confidence interval will be. Confidence intervals are important because
statistical analysis of studies is based on the central idea that, despite making observations
on a limited sample of subjects, the inferred truth to be drawn from the study will apply
to the population of all such subjects. The main purpose of a confidence interval
is to indicate the precision or imprecision of the study sample as an estimate of
the true population value. Confidence intervals are therefore useful and evennecessary whenever an inference is made from the results of one study to the wider
world. In the first example, there were only 49 children completing the depression
questionnaire, but the minister hopes to generalize the findings to all 13-year-olds
in the city. Also, note that even if you and the minister chose your sample wisely—e.g., from very different schools in different parts of the city to avoid bias—theproblem of random variation in the level of depression symptoms remains. Merely
by chance, you may have come across children with notably higher or notably
lower depression levels than exist in the entire population of children of that age
in the city, even though there was no significant systematic bias in your sampling
method.
The number needed to treat, often abbreviated NNT, can be used tosummarize in a single number how effective a specific treatment is compared toplacebo. NNT is defined as the number of people we must treat in order to prevent

How high should be the hurdle
researchers require for a youth
to be considered ‘improved’
when they calculate NNT?
Higher hurdles will raise NNTs
because fewer youth will getover them.

Evidence-based practice A.6

9IACAPAP Textbook of Child and Adolescent Mental Healthone additional bad outcome. A bad outcome (e.g., lack of improvement) is definedby specific criteria. Therefore, low NNTs (such as 3 or 4) indicate an effective
treatment, because it is common for the treatment to convert a patient to a
successful outcome who would not have occurred without the treatment. Typicalsuccessful outcomes are improved mood or improved functioning, often definedwith a cutoff point on a scale.
Note, though, that NNT depends very much on the hurdle that needs to becleared to qualify as ‘better’ or ‘cured.’ How high or low this hurdle is will impact on
the NNT hugely. When reading a paper and seeing an NNT, always note, therefore,
what hurdle the authors have chosen. High NNTs (like 25, 30 or even higher)
suggest either a treatment that is not very effective or an outcome that is difficult
to achieve. For example, if we require that a successful outcome in a depressedadolescent is not to show a single depressive symptom, then it will be difficult toachieve this outcome and the corresponding NNT will be higher than if we hadchosen a more modest outcome (e.g., a 50% reduction of depressive symptoms in
a depression rating scale).
Ourgrin and colleagues (2015) conducted a meta-analysis of 19 RCTs toestimate the effectiveness of interventions for youth who self-harm. They reported
that across studies 28.3 % of participants receiving the studied intervention had
an episode of self-harm within 10 months (the mean of study duration) comparedwith 33.2 % of those receiving treatment. What is the NNT?
In this case NNT = 1/ (0.33.2 – 0.28.3) = 1/0.0499 = 20.04. The NNT is
21 because it is customary to round up NNT to the next whole number. 21 youth
will need to be treated in order that one of them will not self-harm during the next
10 months, who would have harmed himself without treatment. However, only a
few of the studies were generating almost all the effect: 3 studies had NNTs as low
as 2, 4, and 5.
Number needed to harm is abbreviated NNH and summarizes how
frequently a specific side-effect (or side effects) occurs with a specific treatment.
NNH is defined as the number of people we must treat for a single person to beharmed by the treatment who would not have been harmed if he had received
only a placebo. For example, with medications, an undesirable effect (e.g., suicidal
thoughts) occurs that would not have occurred if the patient had received a placebo.
Therefore, high NNHs indicate a safe treatment: many individuals must receive
the treatment before a single individual is harmed by the treatment. Decisions torecommend a specific treatment will require balancing the benefits (NNT) withthe risks (NNH).
Categorial vs continuous variables. The distinction between categorial andcontinuous variables is important because the statistical methods used to aggregate
outcome from a categorical variable summarize results using odds ratios (see how
to calculate odds ratio below), such as whether a child with an anxiety disorder
is in remission or not. The statistical methods used to aggregate outcome froma continuous variable summarize results as standard mean differences (SMDs).
This is why meta-analyses sometimes show results as odds ratios and sometimes
as SMDs, depending on whether the primary outcome variable—the outcome ofprimary interest in the research—is categorical (right handedness) or continuous(height).

Evidence-based practice A.6

A categorical variable
is like being left-handedor right-handed: theperson is assigned to oneof a limited number ofcategories based on sometrait.

A continuous variable islike height or weight. The
person can be anywhere
along a continuum ofan endless number ofpossible values.

10IACAPAP Textbook of Child and Adolescent Mental Health

Odds Ratio. To understand the term odds ratio, recall the concept of odds:
the odds of an event is the ratio of the frequency (or likelihood) of its occurrence tothe frequency (or likelihood) of its non-occurrence (Andrade, 2015). For example,
the odds of randomly drawing an ace out of a pack of cards is 4 (since there are 4
aces in the deck) to 48 (since there are 48 other cards which you might have drawn
instead). To simplify via dividing by 4, the odds of drawing an ace are therefore 1
in 12.
The odds ratio compares the odds of an event after exposure to someoccurrence with the odds of that event in a control group not exposed to theoccurrence. Often the exposure for the purposes of this chapter is the treatment
being studied, whereas the control group is not exposed to the treatment. Consider
a fictitious example: 25 adolescents with conduct problems receive a wilderness
experience and a similar group of 20 adolescents with conduct problems receive
outpatient counseling. One year later 3 adolescents in the wilderness group havebeen arrested versus 7 in the group receiving outpatient counseling. The odds
ratio for not being arrested (the desired outcome) for comparing results in thewilderness experience to outpatient treatment is 22/3 divided by 13/7. This comesout to 7.33/1.86 = 3.94. In plain English, the teens exposed to the wilderness
program were about four times less likely to be arrested during the year following
the wilderness program.

USING YOUR UNDERSTANDING OF THESE BASIC
CONCEPTS TO READ A PAPER ON A SINGLE RCTEFFICIENTLY
Ask yourself: what is the population studied and what is the primary outcome
as displayed in tables and visual graphics. This is a highly efficient way of reading newstudies. For example, consider the results from a study examining the response totreatment of children and adolescents with an anxiety disorder. Called the Child/
Adolescent Anxiety Multimodal Study (CAMS; Walkup et al, 2008), it recruitedchildren and adolescents from multiple sites with common anxiety disorders—
generalized anxiety, social phobia, separation anxiety disorder—and randomized
these youth into receiving one of four treatment strategies: CBT alone, sertraline
alone, combined CBT and sertraline, and placebo. The primary outcome was thechild’s score on the Pediatric Anxiety Rating Scale (PARS).
Examine the graph in Figure A.6.8 showing PARS scores for the 4 groups,
each receiving a different treatment. The red line descends consistently and rapidly
with participants in the group treated with sertraline and CBT enjoying ongoingrelief from anxiety symptoms (scores greater than 13 are consistent with an anxiety
disorder). Combining sertraline and CBT, based on these data, is the most effective
treatment. The error bars on the PARS figures show how much scatter there is in
those numbers. The top of an error bar is most easily understood as the mean plus
the standard error of the mean (calculated as the standard deviation divided by thesquare root of the number of participants), and the bottom of the error bar is themean minus the standard error of the mean. More complex methods of calculating
standard error represented by the error bars also exist. Note that the confidence
interval bars for combination therapy (sertraline + CBT) do not overlap with anyof the three other groups.

Evidence-based practice A.6

11

IACAPAP Textbook of Child and Adolescent Mental Health

Figure A.6.8 Results from the Child/Adolescent Anxiety Multimodal Study, a trial randomizing
anxious youth into one of 4 different treatments**Walkup et al, 2008; with permission.

In this case, the clinical magnitude of the impact of treatment on outcome
was evaluated by calculating effect sizes for each of the three intervention groups
compared to the placebo group: how different were each of the three intervention
groups on the PARS compared to the placebo group? The effect size at 12 weeks was
0.86 for combination therapy, 0.45 for sertraline alone and 0.31 for CBT alone.
The authors also calculated the number needed to treat. Recall that to calculate an
NNT, we must choose a threshold or ‘hurdle’ for each subject to ‘clear’. Higher
hurdles are more difficult to clear and therefore will generate higher NNTs; lower
hurdles will generate lower NNTs. The authors chose as a hurdle that the youth
must be rated as either improved or very much improved on the PARS by the endof treatment, at 12 weeks. Using this criterion, the authors calculated the NNT
for sertraline alone as 3.2; for cognitive behavioral therapy alone as 2.8; for the
combination of both treatments the NNT was 1.7. This low NNT is importantfor clinicians. The authors also used statistics to convey to readers how certain they
were that their results were not a lucky fluke caused by random events that together
resulted in improvements in those youth receiving sertraline and CBT. To assess
this possibility, the authors calculated the confidence intervals for each effect size

Evidence-based practice A.6

12

IACAPAP Textbook of Child and Adolescent Mental Healthand for each number needed to treat. The confidence intervals for a 95% certainty
around the calculated effect size of 0.86 were 0.56 to 1.15 for youth receiving
the combination of sertraline and CBT compared to youth receiving placebo, alarge effect. According to convention, effect sizes of 0.8 and higher indicate a large
effect, 0.3 and below a small effect.
In summary, to read a paper on an RCT efficiently, look first at the
population studied, then go right to the primary outcome results presented either
graphically or in a table. Often the answer to the central research question posed by
the study will be right there visually in the graph or as numbers in a table.

MAKING SENSE OF DATA SUMMARIES
Aggregated evidence summarizes data from multiple RCTs into a single data
set and thereby provides a ‘birds-eye view’ of evidence to reach conclusions about
what works for a specific condition or group of conditions. Importantly, the results
may become more precise because the number of subjects increases. There are atleast four different ways to aggregate data meaningfully: meta-analysis, systematic
review, network meta-analysis, and distillation and matching. Let’s consider each.

Meta-Analysis
A meta-analysis combines the results of multiple randomized trials, eachusing a specific intervention, to produce overall—or ‘average’—statistics on theeffect of those interventions regards benefits and harms, but there are manycaveats. For example, if the populations studied are too different, the outcomesmeasured not sufficiently similar, or there is excessive bias in some studies, then theoverall statistics may not be meaningful. A meta-analysis may look at how a classof treatments works for a class of disorders—how cognitive-behavioral treatment
works for anxiety disorders in children, for example, or how antidepressants work
for anxiety disorders. Crucially, ‘forest plots’ are graphic summaries of the resultsfrom a meta-analysis and are the best way to rapidly grasp results. For example,
Figure A.6.9 shows a forest plot providing a splendid view over the effects ofindividual medications for ADHD compared to placebo (Cortese et al, 2018)
summarizing:•

Effects on ADHD symptoms as rated by clinicians (broken out
separately for youth and adults),•

Effects on ADHD symptoms rated by teachers, and•The chances of the family stopping medication compared to placebo.

This figure was created using a specific form of meta-analysis called network
meta-analysis. The amount of useful information available at a glance is stunning.
We will be discussing how to read a forest plot soon when dealing with systematic
reviews.

Systematic Review
A systematic review is a meta-analysis completed using a well-defined
methodology for the search, inclusion, and synthesis of studies. The statistical

Evidence-based practice A.6

13

IACAPAP Textbook of Child and Adolescent Mental Health

Figure A.6.9 Comparative efficacy and tolerability of medications for ADHD**Cohrane Database of Systematic Reviews (Cortese et al, 2018; with permission)summary remains a meta-analysis but the reader has a quality guarantee about
how it was created: the authors actually explain how the sausage was made. An
analogy of the difference between a meta-analysis versus a systematic review is thatthe former is like a sausage-maker working with more freedom to make it his way,whereas the latter is making sausages in a glass kitchen with a detailed and publishedrecipe. These are efforts to minimize potential bias. A systematic review done by
the Cochrane Library, for example, follows a detailed process outlined in theirtraining manual for reviewers and published on the Internet. Systematic reviews
are increasingly popular in child psychiatry. Figure A.6.10 shows the increase insystematic reviews published in a leading child psychiatry journal by decade fromthe 1970s through the 2010s. Our field increasingly relies on systematic reviews toaggregate information into useful knowledge.

Evidence-based practice A.6

14

IACAPAP Textbook of Child and Adolescent Mental Health

Figure A.6.10 Number of hits to the term ‘systematic review’ in the Journalof the American Academy of Child & Adolescent Psychiatry according to
decade, from 1970 to 2020**Hamilton J, unpublished data.

Figure A.6.11 A systematic review of cognitive-behavioral interventions for children who have beensexually abused**Macdonald et al, 2012; with permission.

Evidence-based practice A.6

15

IACAPAP Textbook of Child and Adolescent Mental Health

The number of systematic reviews published by the Cochrane Collaboration
concerning the mental health of children has also increased markedly in the last
decade. From 2000 through 2009 there were 26 systematic reviews which show
up searching in the Cochrane Database of Systematic Reviews using the searchterms child* AND (ADHD OR Anxiety OR Depression OR PTSD OR Oppositional).
This is a broad search and, in visually scanning the abstracts, not all ‘hits’ appear
potentially useful, but many do. Remarkably, from 2010 through 2019 the samesearch terms retrieve 235 systematic reviews, an increase of 900 percent! There iscurrently much useful aggregated data available to us clinicians caring for children.
Figure A.6.11 shows a recent systematic review of the results of using CBT for
sexually abused children with its results presented as a forest plot (Macdonald et al,2012). Let’s discuss how to use it.
The most efficient way to examine this forest plot is to go straight to the‘black diamonds,’ which summarize the overall effect. The first black diamond
under the heading ‘short term’ (immediately after treatment) has a width,
representing 95% confidence intervals, which does not overlap the ‘0’ vertical line
representing no effect, and the authors provide the standardized mean difference
for the summary of these 6 studies as -0.44. This number (0.44), is called thestandardized mean difference, or effect size, or Cohen’s d—these are all the samething. The treatment group improved on average in the various measures used tomeasure PTSD symptoms 0.44 of a standard deviation, compared to the control
group. This 0.44 effect size is generally considered as moderate. Similar conclusions
can be quickly drawn from this plot both for intermediate-term results and longterm results, demonstrating how efficiently a forest plot displays information.
Therefore, when looking at a systematic review, consider going straight to thefigures and have a look. These graphic displays of information are easily digestible
and cut to the core results of the review immediately. With journals publishingincreasing numbers of systematic reviews, it becomes easier to find such succinct
summaries. There is one caveat: because systematic reviews are labor intensive, it
is often necessary to do the work of updating their results by searching for RCTs
from more recent years on your own.

Network Meta-Analysis
Another method of aggregating data is called network meta-analysis (NMA),
widely used to summarize data about response to treatment. Network metaanalyses, unlike meta-analyses or systematic reviews, compare the effectiveness ofmultiple treatments using methods which allow rank-ordering of those treatmentsfrom most effective to least effective. For clinicians choosing a treatment, this is asignificant improvement. Unlike a meta-analysis, which evaluates the effectivenessof a single treatment measured against a control, a network meta-analysis canevaluate the effectiveness of multiple treatments measured against each other in asingle analysis. A network meta-analysis does this by combining what is called‘direct evidence’—evidence obtained from head-to-head studies comparing one
active treatment to another active treatment—with ‘indirect evidence’—evidence
inferred via computation about the effectiveness of a treatment (A) relative toanother (B) because both, for example, have been compared to a third treatment(C), where ‘C’ may be either a control or an active intervention. The multiple

Evidence-based practice A.6

For a discussion of
network meta-analysis
as used by Cochranego here. If you are new
to Cochrane, you willneed to register here.

16IACAPAP Textbook of Child and Adolescent Mental Healthstudies of varied treatments for a similar condition, tested either against eachother as an active treatment or a placebo or waiting list, comprise a network of
information about effectiveness (Rouse et al, 2017).
Consider how results of a network meta-analysis of antidepressants studiedin adolescents with major depressive disorder can help answer the question: whichantidepressant is most likely to be helpful for Alex, the depressed adolescent who
tried to hang himself mentioned earlier. Going to PubMed and typing network
meta-analysis adolescen* major depression (where * is a ‘wild card’), the first article
listed contains Figure A.6.12 (Cipriani et al, 2016) whether to use pharmacologicalinterventions in this population and which drug should be preferred are still mattersof controversy. Consequently, we aimed to compare and rank antidepressantsand placebo for major depressive disorder in young people. We did a network
meta-analysis to identify both direct and indirect evidence from relevant trials.
We searched PubMed, the Cochrane Library, Web of Science, Embase, CINAHL,
Figure A.6.12 A graphic representation of effectiveness of medications used to treat major depressive

disorder in adolescents, together making up a ‘network’ to be used in a network metaanalysis**Cipriani et al, 2016; with permission.

Evidence-based practice A.6

17

IACAPAP Textbook of Child and Adolescent Mental Health

PsycINFO, LiLACS, regulatory agencies’ websites, and international registers
for published and unpublished, double-blind randomised controlled trials up to
May 31, 2015, for the acute treatment of major depressive disorder in children
and adolescents. We included trials of amitriptyline, citalopram, clomipramine,
desipramine, duloxetine, escitalopram, fluoxetine, imipramine, mirtazapine,
nefazodone, nortriptyline, paroxetine, sertraline, and venlafaxine. Trials recruiting
participants with treatment-resistant depression, treatment duration of less than
4 weeks, or an overall sample size of less than ten patients were excluded. We
extracted the relevant information from the published reports with a predefined
data extraction sheet, and assessed the risk of bias with the Cochrane risk of bias
tool. The primary outcomes were efficacy (change in depressive symptoms).
The thickness of each connecting line in Figure A.6.12 reflects the number ofstudies; the size of each node reflects the total number of participants assigned to thatcondition. This network meta-analysis compares multiple treatments (in this casedifferent antidepressants) by using both direct comparisons of interventions within
randomized controlled trials (i.e., head-to-head studies between antidepressants)
and indirect comparisons across trials based on a common comparator. The term
‘network’ refers to the whole system of multiple studies comparing results for
multiple treatments. A network meta-analysis then uses statistical processes to
examine both direct and indirect comparisons between each pair in the network(Nikolapopoulou et al, 2018). For example, in this network of RCTs studying
antidepressants in adolescents, there is no direct study comparing sertraline tofluoxetine, yet the network meta-analysis will use statistical processes to generate
a comparison between the two based on the results when each was compared toplacebo. The analysis then can rank order each treatment based on effectiveness aswell as tolerability (e.g., based on the proportion of subjects stopping the treatment)using a statistical approach called Surface Under the Cumulative Ranking curve(SUCRA)—highly useful information for clinical practice.
This network meta-analysis generates results useful in considering how
to treat Alex because it rank-orders the antidepressants for efficacy as well as
for tolerability and effect on suicidal ideas/behavior relative both to each otherand to placebo. Network meta-analyses can rank-order treatments in a way thatconventional pairwise meta-analysis cannot, as the technique borrows strengthfrom indirect evidence to gain certainty about all treatment comparisons andallows for estimation of comparative effects that have never been investigated headto-head in randomized clinical trials (Mills et al, 2013). See Figure A.6.13 for amore detailed explanation of network meta-analysis.

SUCRA
The statistical approach used to rank-order a treatment for either
effectiveness or tolerability, called Surface Under the Cumulative Ranking curve,
often abbreviated as SUCRA, uses a graphic representation similar to the receiver
operating characteristic (ROC) analysis used in diagnosis (click here to view atutorial on ROC analysis). The statistical program considers possible ways to rankorder these 14 treatments and asks how closely each possible ranking matches thedata from the available studies. The ‘area under the curve’ is the metric used tomeasure how well any given ranking matches the available data.

Evidence-based practice A.6

18

IACAPAP Textbook of Child and Adolescent Mental Health

Figure A.6.13 Network
meta-analysis of eficacy andtolerability*
This table shows the resultsof a network meta-analysis thatcompares 14 different drugs andplacebo when treating depression
in youth, regards both treatment
response (in blue) and tolerability(pink). Only one drug—fluoxetine—reaches statistical significance for
reducing symptoms, with a standard
mean difference of -0.53 andconfidence intervals of -0.99 to -0.03.
Tolerability is measured by the odds
ratio of discontinuing treatment. An
odds ratio below ‘1’ means the row
defining treatment was tolerated
better than the column defining
treatment. Efficacy results (in lightblue) are based on mean overallchange in depressive symptoms
and have been standardized so that
results from different studies using
different scales can be comparedwith one another.
Drugs are reported in order ofeficacy according to the Surface
Under the Cumulative Ranking
curve, or SUCRA (see below).
Comparisons are read from left
to right. The efficacy (light blueboxes) and tolerability (pink boxes)
estimates are located at theintersection of the column-deﬁning
treatment and the row-deﬁning
treatment, expressed as thestandardized mean difference (withconfidence intervals in parentheses).
For eficacy (mean overall change
in symptoms), a standardized
mean difference below ‘0’ favors
the column-deﬁning treatment.
For tolerability (discontinuation
due to adverse events), an odds
ratio below ‘1’ favors the rowdeﬁning treatment. Signiﬁcantresults are in bold and underlined.
AMI=amitriptyline; CIT=citalopram;
DES=desipramine; DUL=duloxetine;
ESC=escitalopram; FLU=ﬂuoxetine;
IMP=imipramine; MIR=mirtazapine;
NEF=nefazodone; OR=odds ratio;
PAR=paroxetine; PBO=placebo;
SER=sertraline; SMD=standardized
mean diference; SUCRA=surface
under the cumulative ranking curve;
VEN=venlafaxine. Continues next
page.
*Cipriani et al, 2016; with permission.
Note that three drugs have been omitted.

Evidence-based practice A.6

19

IACAPAP Textbook of Child and Adolescent Mental Health

The dark blue blocks
in Figure A.6.13 containthe treatments being
compared. The diagonal‘descending staircase’ indark blue is very usefulfor clinicians (read left
to right). It rank-orders
the treatments basedon SUCRA data for
efficacy, from most to
least efficacious. The
light blue blocks containstatistically generated estimates of the standardized mean differences in the change in depression
ratings comparing the effectiveness of the medication in the column with that on the row. For example,
in this fragment from Figure A.6.13, at the intersection of the column for fluoxetine (FLU) and the row
for sertraline (SER) the standardized mean diﬀerence is -0.28 (that is, below 0—favoring fluoxetine).
The confidence intervals for this standardized mean diﬀerence are -1.38 to -0.82 and contain zero,
which would represent no statistically significant difference. That is, according to this network metaanalysis, fluoxetine is more effective than sertraline but the difference is not statistically significant.
The standardized mean difference was used because depression rating scale data are continuous
rather than categorical. The pink blocks represent data the authors call ‘tolerability’ because thefigures are based on the proportion of youth who continue to take the drug rather than discontinue it.
Since continuing vs discontinuing is a categorical variable, the results are presented as an odds ratio.
In this analysis you can see also that fluoxetine (FLU) is less likely to be discontinued than duloxetine(DUL) (OR = 0.31) and the difference is statistically significant (95% confidence intervals do notcontain 1).

Figure A.6.14 shows the SUCRA curves forfluoxetine and escitalopram. The x-axis contains all Figure A.6.14 Surface Under the Cumulative
the competing medications and placebo. A rising Ranking (SUCRA) data for comparing effectivenessof 14 different antidepressants. Results forcurve shows that the medication being assessed is escitalopram (ESC) and fluoxetine (FLU)*
winning its comparisons and therefore generates
more ‘area under the curve.’ Note how quickly thefluoxetine SUCRA curve rises compared to otherdrugs, whereas escitalopram seems to plod along.
This view is too simplistic, of course, since the actual
curves are generated via complex statistical methods.
The visual information is nevertheless helpful ingrasping a sense of how medications compare toeach other for effectiveness. A SUCRA value of 50%(or 0.50) represents a treatment that has as many *Cipriani et al, 2016; with permission.
wins as losses in comparisons, and a value 100% (or1.0) is a perfect score for a superior treatment winning all its comparisons.
A SUCRA score for each drug can be interpreted as the proportion oftreatments worse than the one of interest. Figure A.6.15 shows SUCRA scores forsafety from suicidal behavior or ideation in children for a range of antidepressants.
Thanks to network analysis, we now can rank-order antidepressants in youth
for efficacy, for risk of increasing suicidal ideas or behavior, and for tolerability—each one helpful in choosing a medication for Alex. These data suggest that

Evidence-based practice A.6

20

IACAPAP Textbook of Child and Adolescent Mental Healthfluoxetine ranks first for efficacy, first for tolerability, and towards
the middle of the pack for safety from suicidal ideas or behavior. In
addition, duloxetine deserves consideration since it ranks high for
efficacy and does well regards safety. Alex was started on fluoxetinewith duloxetine as a potential back-up if fluoxetine failed.

Distillation and Matching

Figure A.6.15 Safety from suicidal
behavior or ideation in children fora range of antidepressants. Larger
Surface Under the Cumulative
Ranking curve (SUCRA) scoresreflect increased safety*
DRUG

SUCRA Score (%)

A fourth method of aggregating data—appropriate only for
Imipramine
68.9
studies of psychotherapy (not medicines)—in order to summarize
Placebo65.6
what works for a specific disorder is to ‘distill’ treatment protocols
Duloxetine
65.3(manuals) which have done well in RCTs for a specific disorder.
Escitalopram
60.4‘Distilling’ here refers to breaking up often large psychotherapy
Clomipramine
59.7
manuals into succinct specific elements like ‘relaxation techniques’
Fluoxetine
53.3
or ‘restructuring cognitions’ or ‘encouraging pleasurable activities.’
Paroxetine
49.0
In other words, rather than choosing a specific manual based on one
Citalopram
42.7
or more studies, why not lift the hood of all the manuals proven to
Sertraline
28.0
get a good result and look at what’s going on in them: what are theVenlafaxine
2.8
aspects that make those manuals work? That is exactly what Chorpita
and Daleiden did: they chose to ‘aggregate evidence-based treatment*Data from Cipriani et al, 2016
protocols empirically according to their constituent treatment
procedures’ (Chorpita et al, 2005). That is, they looked for common
elements or procedures that showed up in multiple manuals that were effective intreatment trials. They reasoned that such elements occurring in multiple effective
manuals were likely to be a part of what created positive change. If you werestudying a fleet of race cars to build a great race car, you might examine multiple
cars which were winning races and then look for features in their engines which
they shared (common elements).
Chorpita and Daleiden began by saying that a psychotherapy manual was‘winning’ if, in at least one study, ‘…an active, nonpharmacological treatment
beat one or more other study groups (e.g., psychosocial treatment, medication,
combined psychosocial and medication, placebo, waiting-list, no treatment, orother control group) in a randomized trial on the primary outcome measure in
the target symptom domain (e.g., the primary depression measure in a study ofdepression)’ (Chorpita et al, 2005). For example, if most manuals proven to help
with depression have a plan for how the therapist will intervene by challenging
pessimistic, gloomy thoughts as unrealistic, this is called a common element(cognitive, because the goal is to gently challenge unrealistic, pessimistic cognitions).
The results of their distilling (i.e., summarizing) the elements from ‘winner’manuals for treating depressed youth is summarized in Figure A.6.16. Each bar
represents the frequency, as a percentage, of all ‘winning’ manuals for depressed
youth in which those specific elements appear. For example, 74% of the ‘winning’
manuals for depressed youth use cognitive therapy as one element of their protocol.
These results ‘match’ as potentially effective treatments for individual youth withdepression but can be further tailored based on the individual’s gender, age, andrace to improve the match (Chorpita & Daleiden, 2009). Chorpita and Weisz,(2009) went through a similar process to define the treatment elements mostfrequently appearing in ‘winner’ manuals for youth with anxiety, with oppositional
defiant behavior, and for those with trauma. For example, here are five elements

Evidence-based practice A.6

21

IACAPAP Textbook of Child and Adolescent Mental Health

Top Ten Most Effective Treatment
Elements for Depressed YouthFigure A.6.16 Top 10 treatment elements ranked by how often (%) each appears in the manuals of‘winner’ treatments for depressed youth*

Self-Reward / Self-Praise
Relaxation
Social Skills Training
Goal Setting
Self-Monitoring
Problem Solving
Activity Scheduling
Maintenance/ Relapse Prevention
Psychoeducational-Child
Cognitive
0%

20%

40%

60%

80%

*Based on data from Chorpita & Daleiden, 2009.

common in successful treatment manuals for anxious youth: exposure, relaxation,
cognitive approaches, modeling, and psychoeducation about the nature of anxiety
problems.
A strength of the distillation and matching approach is its flexibility inthe treatment of youth with comorbid symptoms. A protocol for how to deliver
treatment elements for anxiety, depression, trauma, and conduct problems,
including youth with comorbid symptoms, allows for individualizing treatment
within the context of elements proven to appear frequently in ‘winner’ manuals(Chorpita & Weisz, 2009). To understand how this works in an individual case,recall the image mentioned earlier of an old-fashioned liquor still, boiling downa big mash of grain to produce a small bottle of liquor. Similarly, here, multiple
large bulky psychotherapy manuals, chosen because they are proven to be effective
for a particular problem, are boiled down to their essential ingredients—what thetherapist is directed to do. Figure A.6.17, for example, shows that 80% of manuals
effective for anxiety disorders use exposure. Exposure is therefore a common
element of most psychotherapy manuals for anxiety

Distillation and Matching Treatment Elements for Depression as a
Feasible Evidence-Informed Treatment for Alex
It’s a long way from the cool elegance of a Cochrane systematic review tothe reality of a child’s life, from results in a meta-analysis to what is feasible in our
offices. The messiness of real life and the context of our workplace often shapewhat is feasible. Recall Alex, the high school student who tried to hang himself
and who remains depressed. He hated his one-week stay in the hospital and does

Evidence-based practice A.6

22

IACAPAP Textbook of Child and Adolescent Mental Health

Figure A.6.17
Frequency (%) of common elements effective in treating
anxious youth distilled from ‘winner’ manuals*

Evidence-informed
rather than evidencebased allows us to be
more realistic since atherapy is useless if it is
not feasible.

*Chorpita & Daleiden, 2009.

not want to miss any more school. He lives with his mother while his older brotheris away in college. He is estranged from his father, an electrical engineer. His
Beck Depression Inventory score is 30, consistent with severe depression. A metaanalysis suggests mentalization as a potentially useful psychotherapy to reduce hisrisk of self-harm. A network meta-analysis offers evidence that fluoxetine is thebest antidepressant for him. What evidence-informed psychotherapy is feasible inour setting to combine with fluoxetine to improve his mood? Note we are hedging
here away from ‘evidence-based’ to ‘evidence-informed’ and adding the modifier‘feasible’. Evidence-informed rather than evidence-based allows us to be more
realistic since a therapy is useless if it is not feasible.
The therapist treating Alex used the distillation and matching approachdescribed above. For Alex’s severe depression, distillation and matching suggests
using components or elements of treatment which appear often in ‘winner’ manuals
shown in at least one RCT to be effective in treating depression. The ten mostprevalent elements in successful manuals treating depressed youth are presented
in Figure A.6.16, namely, cognitive, psychoeducation, maintenance and relapse
prevention, activity scheduling, problem-solving, self-monitoring, goal-setting,social skills training, relaxation exercises, self-reward and self-praise.
Many of these treatment elements for depressed youth can be introduced into
early interviews with Alex, split between Alex alone and Alex with his mother.
Psychoeducation in this context refers to teaching them both key aspects of what
is known about severe depression: that it often affects physiological functions
like appetite, sleep, the ability to feel pleasure and joy, concentration, energy
level, motivation, and decision-making among others; that it is normal to lose
interest during depressive episodes, or be unable to concentrate on studies, or
have the energy to complete projects. Encouraging Alex to gently challenge more
pessimistic and less hopeful thoughts is a cognitive intervention and encouraging
him to be a good observer of his thoughts and moods is self-monitoring. Activity
scheduling include encouraging him to find time to hang out with friends totalk, a hedonic activity for him. Alex had a complete recovery in his functioning

Evidence-based practice A.6

23

IACAPAP Textbook of Child and Adolescent Mental Healthover the course of a year-long psychotherapy based on the common elements fordepression and treatment with fluoxetine.

The Cochrane Database of Systematic Reviews (CDSR) and
PubMedCentral (PMC) are free, easily available allies in finding
evidence

Click on the picture to viewa video tutorial on how tosearch using PubMed (2:27)

Feasibility begins with finding convincing evidence quickly using databasesin the public domain. Two Cochrane databases—the Cochrane Databaseof Systematic Reviews (CDSR) and the Cochrane Central Registerof Controlled Trials (CENTRAL) are useful, so is PubMedCentral and
PsycINFO.
All these databases provide abstracts for free, but PubMedCentral has
the advantage of containing not only abstracts but also full-text accessto literally millions of journal articles. CDSR offers abstracts and ‘plain
language’ summaries of the conclusions of each review at no charge
worldwide. In low-income countries, Cochrane offers full text versions ofboth CENTRAL and CDSR at no charge. The exhaustive thoroughnessof Cochrane’s systematic reviews gives them high credibility. PsycINFO
contains free abstracts from a wide swath of journals in psychology.

Evidence-based practice A.6

24

IACAPAP Textbook of Child and Adolescent Mental Health

REFERENCES
Andrade C (2015). Understanding relative risk, odds ratio,
and related terms: as simple as it can get. The Journalof Clinical Psychiatry 76: e857–61. https://doi.
org/10.4088/JCP.15f10150
Chorpita BF, Daleiden E, Weisz J (2005). Identifying andselecting the common elements of evidence-based
interventions: a distillation and matching model.
Mental Health Services Research 7: 5–20
Chorpita BF, Daleiden EL (2009). Mapping evidence-based
treatments for children and adolescents: application of
the distillation and matching model to 615 treatmentsfrom 322 randomized trials. Journal of Consulting
and Clinical Psychology 77:566–579. https://doi.
org/10.1037/a0014565
Chorpita BF, Weisz JR (2009). MATCH-ADTC: Modular
Approach to Therapy for Children with Anxiety,Depression, Trauma, or Conduct Problems. Satellite
Beach, FL: PracticeWise, LLC
Cipriani A, Zhou X, Del Giovane C et al (2016). Comparative
efficacy and tolerability of antidepressants for major
depressive disorder in children and adolescents: a
network meta-analysis. The Lancet 388 (10047): 881–
890. https://doi.org/10.1016/S0140-6736(16)303853
Cortese S, Adamo N, Del Giovane C et al (2018). Comparative
efficacy and tolerability of medications for attentiondeficit hyperactivity disorder in children, adolescents,
and adults: a systematic review and network metaanalysis. The Lancet Psychiatry 5:727–738. https://doi.
org/10.1016/S2215-0366(18)30269-4
Guyatt GH, Rennie D (2002). Users’ Guides to the Medical
Literature: A Manual for Evidence-Based Clinical
Practice Chicago: AMA Press
James AC, James G, Cowdrey FA et al (2015). Cognitive
behavioural therapy for anxiety disorders in children
and adolescents. Cochrane Database of Systematic
Reviews. https://doi.org/10.1002/14651858.
CD004690.pub4

Evidence-based practice A.6

Kabai P (2021). Bias and Random Error (blog). http://apollo.lsc.
vsc.edu/classes/remote/lecture_notes/measurements/
bias_random_errors.html
Macdonald G, Higgins JPT et al (2012). Cognitive‐behaviouralinterventions for children who have been sexually
abused. The Cochrane Database of Systematic Reviews.
https://doi.org/10.1002/14651858.CD001930.pub3
March J, Silva S, Petrycki S et al (2004). Fluoxetine, cognitivebehavioral therapy, and their combination foradolescents with depression: Treatment for Adolescents
With Depression Study (TADS) randomized
controlled trial. JAMA 292:807-820. doi: 10.1001/
jama.292.7.807. PMID: 15315995
Mills EJ, Thorlund K, Ioannidis JPA (2013). Demystifying
trial networks and network meta-analysis. BMJ 346(may14 2): f2914–f2914. https://doi.org/10.1136/
bmj.f2914
Nikolakopoulou A, Mavridis D, Furukawa TA et al (2018).
Living network meta-analysis compared with pairwise
meta-analysis in comparative effectiveness research:empirical study. BMJ, February, k585. https://doi.
org/10.1136/bmj.k585
Ougrin D, Tranah T, Stahl D et al (2015). Therapeuticinterventions for suicide attempts and self-harm inadolescents: systematic review and meta-analysis.
Journal of the American Academy of Child & Adolescent
Psychiatry 54: 97-107.e2. https://doi.org/10.1016/j.
jaac.2014.10.009
Rouse B, Chaimani A, Li T (2017). Network meta-analysis: anintroduction for clinicians. Internal and Emergency
Medicine 12: 103–11. https://doi.org/10.1007/
s11739-016-1583-7
Walkup JT, Albano AM, Piacentini J et al (2008). Cognitive
behavioral therapy, sertraline, or a combinationin childhood anxiety. New England Journal of
Medicine 359:2753–2766. https://doi.org/10.1056/
NEJMoa0804633

25