[Paleopsych] Prevention & Treatment: Listening to Prozac but Hearing Placebo
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Listening to Prozac but Hearing Placebo: A Meta-Analysis of Antidepressant
Medication
http://www.journals.apa.org/prevention/volume1/pre0010002a.html
Prevention & Treatment, Volume 1, Article 0002a, posted June 26, 1998
[I read something similar in Science, maybe twenty years ago, about the placebo
effect being proportionate to the medical effect, and I think it deal with a
much larger categories of illnesses. Does anyone know anything further about
these anomalies?]
by Irving Kirsch, Ph.D., University of Connecticut, Storrs, CT
and Guy Sapirstein, Ph.D., Westwood Lodge Hospital, Needham, MA
ABSTRACT
Mean effect sizes for changes in depression were calculated for
2,318 patients who had been randomly assigned to either
antidepressant medication or placebo in 19 double-blind clinical
trials. As a proportion of the drug response, the placebo response
was constant across different types of medication (75%), and the
correlation between placebo effect and drug effect was .90. These
data indicate that virtually all of the variation in drug effect
size was due to the placebo characteristics of the studies. The
effect size for active medications that are not regarded to be
antidepressants was as large as that for those classified as
antidepressants, and in both cases, the inactive placebos produced
improvement that was 75% of the effect of the active drug. These
data raise the possibility that the apparent drug effect (25% of
the drug response) is actually an active placebo effect.
Examination of pre-post effect sizes among depressed individuals
assigned to no-treatment or wait-list control groups suggest that
approximately one quarter of the drug response is due to the
administration of an active medication, one half is a placebo
effect, and the remaining quarter is due to other nonspecific
factors.
_________________________________________________________________
EDITORS' NOTE
The article that follows is a controversial one. It reaches a
controversial conclusion--that much of the therapeutic benefit of
antidepressant medications actually derives from placebo
responding. The article reaches this conclusion by utilizing a
controversial statistical approach--meta-analysis. And it employs
meta-analysis controversially--by meta-analyzing studies that are
very heterogeneous in subject selection criteria, treatments
employed, and statistical methods used. Nonetheless, we have chosen
to publish the article. We have done so because a number of the
colleagues who originally reviewed the manuscript believed it had
considerable merit, even while they recognized the clearly
contentious conclusions it reached and the clearly arguable
statistical methods it employed.
We are convinced that one of the principal aims of an electronic
journal ought to be to bring our readers information on a variety
of current topics in prevention and treatment, even though much of
it will be subject to heated differences of opinion about worth and
ultimate significance. This is to be expected, of course, when one
is publishing material at the cutting-edge, in a cutting-edge
medium.
We also believe, however, that soliciting expert commentary to
accompany particularly controversial articles facilitates the
fullest possible airing of the issues most germane to appreciating
both the strengths and the weaknesses of target articles. In the
same vein, we welcome comments on the article from readers as well,
though for obvious reasons, we cannot promise to publish all of
them.
Feel free to submit a comment by emailing admin at apa.org.
Peter Nathan, Associate Editor (Treatment)
Martin E. P. Seligman, Editor
_________________________________________________________________
We thank R. B. Lydiard and Smith-Kline Beecham Pharaceuticals for
supplying additional data. We thank David Kenny for his assistance
with the statistical analyses. We thank Roger P. Greenberg and
Daniel E. Moerman for their helpful comments on earlier versions of
this paper.
Correspondence concerning this article should be addressed to
Irving Kirsch, Department of Psychology, U-20, University of
Connecticut, 406 Babbidge Road Storrs, CT 06269-1020.
E-mail: Irvingk at uconnvm.uconn.edu
_________________________________________________________________
More placebos have been administered to research participants than any
single experimental drug. Thus, one would expect sufficient data to
have accumulated for the acquisition of substantial knowledge of the
parameters of placebo effects. However, although almost everyone
controls for placebo effects, almost no one evaluates them. With this
in mind, we set about the task of using meta-analytic procedures for
evaluating the magnitude of the placebo response to antidepressant
medication.
Meta-analysis provides a means of mathematically combining results
from different studies, even when these studies have used different
measures to assess the dependent variable. Most often, this is done by
using the statistic d, which is a standardized difference score. This
effect size is generally calculated as the mean of the experimental
group minus the mean of the control group, divided by the pooled
standard deviation. Less frequently, the mean difference is divided by
standard deviation of the control group (Smith, Glass, & Miller,
1980).
Ideally, to calculate the effect size of placebos, we would want to
subtract the effects of a no-placebo control group. However, placebos
are used as controls against which the effects of physical
interventions can be gauged. It is rare for an experimental condition
to be included against which the effects of the placebo can be
evaluated. To circumvent this problem, we decided to calculate
within-cell or pre-post effect sizes, which are the posttreatment mean
depression score minus the pretreatment mean depression score, divided
by the pooled standard deviation (cf. Smith et al., 1980). By doing
this for both placebo groups and medication groups, we can estimate
the proportion of the response to antidepressant medication that is
duplicated by placebo administration, a response that would be due to
such factors as expectancy for improvement and the natural course of
the disorder (i.e., spontaneous remission). Later in this article, we
also separate expectancy from natural history and provide estimates of
each of these effects.
Although our approach is unusual, in most cases it should provide
results that are comparable to conventional methods. If there are no
significant pretreatment differences between the treatment and control
groups, then the subtraction of mean standardized pre-post difference
scores should result in a mean effect size that is just about the same
as that produced by subtracting mean standardized posttreatment
scores. Suppose, for example, we have a study with the data displayed
in Table 1. The conventionally calculated effect size would be would
be 1.00. The pre-post effect sizes would be 3.00 for the treatment
group and 2.00 for the control group. The difference between them is
1.00, which is exactly the same effect calculated from posttreatment
scores alone. However, calculating the effect size in this manner also
provides us with the information that the effect of the control
procedure was 2/3 that of the treatment procedure, information that we
do not have when we only consider posttreatment scores. Of course, it
is rare for two groups to have identical mean pretreatment scores, and
to the extent that those scores are different, our two methods of
calculation would provide different results. However, by controlling
for baseline differences, our method should provide the more accurate
estimate of differential outcome.
CAPTION: Table 1
Hypothetical Means and Standard Deviations for a Treatment Group and a
Control Group
Treatment Control
Pretreatment Posttreatment Pretreatment Posttreatment
M 25.00 10.00 25.00 15.00
SD 5.50 4.50 4.50 5.50
The Effects of Medication and Placebo
Study Characteristics
Studies assessing the efficacy of antidepressant medication were
obtained through previous reviews (Davis, Janicak, & Bruninga, 1987;
Free & Oei, 1989; Greenberg & Fisher, 1989; Greenberg, Bornstein,
Greenberg, & Fisher, 1992; Workman & Short, 1993), supplemented by a
computer search of PsycLit and MEDLINE databases from 1974 to 1995
using the search terms drug-therapy or pharmacotherapy or
psychotherapy or placebo and depression or affective disorders.
Psychotherapy was included as a search term for the purpose of
obtaining articles that would allow estimation of changes occurring in
no-treatment and wait-list control groups, a topic to which we return
later in this article. Approximately 1,500 publications were produced
by this literature search. These were examined by the second author,
and those meeting the following criteria were included in the
meta-analysis:
1. The sample was restricted to patients with a primary diagnosis of
depression. Studies were excluded if participants were selected
because of other criteria (eating disorders, substance abuse,
physical disabilities or chronic medical conditions), as were
studies in which the description of the patient population was
vague (e.g., "neurotic").
2. Sufficient data were reported or obtainable to calculate
within-condition effect sizes. This resulted in the exclusion of
studies for which neither pre-post statistical tests nor
pretreatment means were available.
3. Data were reported for a placebo control group.
4. Participants were assigned to experimental conditions randomly.
5. Participants were between the ages of 18 and 75.
Of the approximately 1,500 studies examined, 20 met the inclusion
criteria. Of these, all but one were studies of the acute phase of
therapy, with treatment durations ranging from 1 to 20 weeks (M =
4.82). The one exception (Doogan & Caillard, 1992) was a maintenance
study, with a duration of treatment of 44 weeks. Because of this
difference, Doogan and Caillard's study was excluded from the
meta-analysis. Thus, the analysis was conducted on 19 studies
containing 2,318 participants, of whom 1,460 received medication and
858 received placebo. Medications studied were amitriptyline,
amylobarbitone, fluoxetine, imipramine, paroxetine, isocarboxazid,
trazodone, lithium, liothyronine, adinazolam, amoxapine, phenelzine,
venlafaxine, maprotiline, tranylcypromine, and bupropion.
The Calculation of Effect Sizes
In most cases, effect sizes (d) were calculated for measures of
depression as the mean posttreatment score minus the mean pretreatment
score, divided by the pooled standard deviation (SD). Pretreatment SDs
were used in place of pooled SDs in calculating effect sizes for four
studies in which posttreatment SDs were not reported (Ravaris, Nies,
Robinson, et al., 1976; Rickels & Case, 1982; Rickels, Case,
Weberlowsky, et al., 1981; Robinson, Nies, & Ravaris, 1973). The
methods described by Smith et al. (1980) were used to estimate effect
sizes for two studies in which means and SDs were not reported. One of
these studies (Goldberg, Rickels, & Finnerty, 1981) reported the t
value for the pre-post comparisons. The effect size for this study was
estimated using the formula:
d= t (2/n)^1/2
where t is the reported t value for the pre-post comparison, and n is
the number of subjects in the condition. The other study (Kiev &
Okerson, 1979) reported only that there was a significant difference
between pre- and posttreatment scores. As suggested by Smith et al.
(1980), the following formula for estimating the effect size was used:
d= 1.96 (2/n)^ 1/2 ,
where 1.96 is used as the most conservative estimation of the t value
at the .05 significance level used by Kiev and Okerson. These two two
effect sizes were also corrected for pre-post correlation by
multiplying the estimated effect size by (1 - r)^ 1/2 , r being the
estimate of the test-retest correlation (Hunter & Schmidt, 1990).
Bailey and Coppen (1976) reported test-retest correlations of .65 for
the Beck Depression Inventory (BDI; Beck, Ward, Mendelson, Mock, &
Erbaugh, 1961) and .50 for the Hamilton Rating Scale for Depression
(HRS-D; Hamilton, 1960) . Therefore, in order to arrive at an
estimated effect size, corrected for the pre-post correlation, the
estimated effect sizes of the HRS-D were multiplied by 0.707 and the
effect sizes of the BDI were multiplied by 0.59.
In studies reporting multiple measures of depression, an effect size
was calculated for each measure and these were then averaged. In
studies reporting the effects of two drugs, a single mean effect size
for both was calculated for the primary analysis. In a subsequent
analysis, the effect for each drug was examined separately. In both
analyses, we calculated mean effect sizes weighted for sample size (D;
Hunter & Schmidt, 1990).
Effect Sizes
Sample sizes and effect sizes for patients receiving medication or
placebo are presented in Table 2. Mean effect sizes, weighted for
sample size, were 1.55 SDs for the medication response and 1.16 for
the placebo response. Because effect sizes are obtained by dividing
both treatment means by a constant (i.e., the pooled SD), they can be
treated mathematically like the scores from which they are derived. ^1
In particular, we have shown that, barring pretreatment between-group
differences, subtracting the mean pre-post effect size of the control
groups from the mean pre-post effect size of the experimental groups
is equivalent to calculating an effect size by conventional means.
Subtracting mean placebo response rates from mean drug response rates
reveals a mean medication effect of 0.39 SDs. This indicates that 75%
of the response to the medications examined in these studies was a
placebo response, and at most, 25% might be a true drug effect. This
does not mean that only 25% of patients are likely to respond to the
pharmacological properties of the drug. Rather, it means that for a
typical patient, 75% of the benefit obtained from the active drug
would also have obtained from an inactive placebo.
CAPTION: Table 2
Studies Including Placebo Control Groups
Drug Placebo
Study n d n d
Blashki et al. (1971) 43 1.75 18 1.02
Byerly et al. (1988) 44 2.30 16 1.37
Claghorn et al. (1992) 113 1.91 95 1.49
Davidson & Turnbull (1983) 11 4.77 8 2.28
Elkin et al. (1989) 36 2.35 34 2.01
Goldberg et al. (1981) 179 0.44 93 0.44
Joffe et al. (1993) 34 1.43 16 0.61
Kahn et al. (1991) 66 2.25 80 1.48
Kiev & Okerson (1979) 39 0.44 22 0.42
Lydiard (1989) 30 2.59 15 1.93
Ravaris et al. (1976) 14 1.42 19 0.91
Rickels et al. (1981) 75 1.86 23 1.45
Rickels & Case (1982) 100 1.71 54 1.17
Robinson et al. (1973) 33 1.13 27 0.76
Schweizer et al. (1994) 87 3.13 57 2.13
Stark & Hardison (1985) 370 1.40 169 1.03
van der Velde (1981) 52 0.66 27 0.10
White et al. (1984) 77 1.50 45 1.14
Zung (1983) 57 .88 40 0.95
Inspection of Table 2 reveals considerable variability in drug and
placebo response effect sizes. As a first step toward clarifying the
reason for this variability, we calculated the correlation between
drug response and placebo response, which was found to be
exceptionally high, r = .90, p < .001 (see Figure 1). This indicates
that the placebo response was proportionate to the drug response, with
remaining variability most likely due to measurement error.
[pre0010002afig1a.gif]
Figure 1. The placebo response as a predictor of the drug response.
Our next question was the source of the common variability. One
possibility is that the correlation between placebo and drug response
rates are due to between-study differences in sample characteristics
(e.g., inpatients vs. outpatients, volunteers vs. referrals, etc.).
Our analysis of psychotherapy studies later in this article provides a
test of this hypothesis. If the correlation is due to between-study
differences in sample characteristics, a similar correlation should be
found between the psychotherapy and no-treatment response rates. In
fact, the correlation between the psychotherapy response and the
no-treatment response was nonsignificant and in the opposite
direction. This indicates that common sample characteristics account
for little if any of the relation between treatment and control group
response rates.
Another possibility is that the close correspondence between placebo
and drug response is due to differences in so-called nonspecific
variables (e.g., provision of a supportive relationship, color of the
medication, patients' expectations for change, biases in clinician's
ratings, etc.), which might vary from study to study, but which would
be common to recipients of both treatments in a given study.
Alternately, the correlation might be associated with differences in
the effectiveness of the various medications included in the
meta-analysis. This could happen if more effective medications
inspired greater expectations of improvement among patients or
prescribing physicians (Frank, 1973; Kirsch, 1990). Evans (1974), for
example, reported that placebo morphine was substantially more
effective than placebo aspirin. Finally, both factors might be
operative.
We further investigated this issue by examining the magnitude of drug
and placebo responses as a function of type of medication. We
subdivided medication into four types: (a) tricyclics and
tetracyclics, (b) selective serotonin reuptake inhibitors (SSRI), (c)
other antidepressants, and (d) other medications. This last category
consisted of four medications (amylobarbitone, lithium, liothyronine,
and adinazolam) that are not considered antidepressants.
Weighted (for sample size) mean effect sizes of the drug response as a
function of type of medication are shown in Table 3, along with
corresponding effect sizes of the placebo response and the mean effect
sizes of placebo responses as a proportion of drug responses. These
data reveal relatively little variability in drug response and even
less variability in the ratio of placebo response to drug response, as
a function of drug type. For each type of medication, the effect size
for the active drug response was between 1.43 and 1.69, and the
inactive placebo response was between 74% and 76% of the active drug
response. These data suggest that the between-drug variability in drug
and placebo response was due entirely to differences in the placebo
component of the studies.
CAPTION: Table 3
Effect Sizes as a Function of Drug Type
Statistic Type of drug
Antidepressant Other
drugs
Tri- and
tetracyclic SSRI Other
N 1,353 626 683 203
K 13 4 8 3
D--Drug 1.52 1.68 1.43 1.69
D--Placebo 1.15 1.24 1.08 1.29
Placebo/drug .76 .74 .76 .76
N = number of subjects; K = number of studies; D = mean weighted
effect size; placebo/drug = placebo response as a proportion of active
drug response.
Differences between active drug responses and inactive placebo
responses are typically interpreted as indications of specific
pharmacologic effects for the condition being treated. However, this
conclusion is thrown into question by the data derived from active
medications that are not considered effective for depression. It is
possible that these drugs affect depression indirectly, perhaps by
improving sleep or lowering anxiety. But if this were the case and if
antidepressants have a specific effect on depression, then the effect
of these other medications ought to have been less than the effect of
antidepressants, whereas our data indicate that the response to these
nonantidepressant drugs is at least as great as that to conventional
antidepressants.
A second possibility is that amylobarbitone, lithium, liothyronine,
and adinazolam are in fact antidepressants. This conclusion is
rendered plausible by the lack of understanding of the mechanism of
clinical action of common antidepressants (e.g., tricyclics). If the
classification of a drug as an antidepressant is established by its
efficacy, rather than by knowledge of the mechanism underlying its
effects, then amylobarbitone, lithium, liothyronine, and adinazolam
might be considered specifics for depression.
A third possibility is that these medications function as active
placebos (i.e., active medications without specific activity for the
condition being treated). Greenberg and Fisher (1989) summarized data
indicating that the effect of antidepressant medication is smaller
when it is compared to an active placebo than when it is compared to
an inert placebo (also see Greenberg & Fisher, 1997). By definition,
the only difference between active and inactive placebos is the
presence of pharmacologically induced side effects. Therefore,
differences in responses to active and inert placebos could be due to
the presence of those side effects. Data from other studies indicate
that most participants in studies of antidepressant medication are
able to deduce whether they have been assigned to the drug condition
or the placebo condition (Blashki, Mowbray, & Davies, 1971; Margraf,
Ehlers, Roth, Clark, Sheikh, Agras, & Taylor, 1991; Ney, Collins, &
Spensor, 1986).^ This is likely to be associated with their previous
experience with antidepressant medication and with differences between
drug and placebo in the magnitude of side effects. Experiencing more
side effects, patients in active drug conditions conclude that they
are in the drug group; experiencing fewer side effects, patients in
placebo groups conclude that they are in the placebo condition. This
can be expected to produce an enhanced placebo effect in drug
conditions and a diminished placebo effect in placebo groups. Thus,
the apparent drug effect of antidepressants may in fact be a placebo
effect, magnified by differences in experienced side effects and the
patient's subsequent recognition of the condition to which he or she
has been assigned. Support for this interpretation of data is provided
by a meta-analysis of fluoxetine (Prozac), in which a correlation of
.85 was reported between the therapeutic effect of the drug and the
percentage of patients reporting side effects (Greenberg, Bornstein,
Zborowski, Fisher, & Greenberg, 1994).
Natural History Effects
Just as it is important to distinguish between a drug response and a
drug effect, so too is it worthwhile to distinguish between a placebo
response and a placebo effect (Fisher, Lipman, Uhlenhuth, Rickels, &
Park, 1965). A drug response is the change that occurs after
administration of the drug. The effect of the drug is that portion of
the response that is due to the drug's chemical composition; it is the
difference between the drug response and the response to placebo
administration. A similar distinction can be made between placebo
responses and placebo effects. The placebo response is the change that
occurs following administration of a placebo. However, change might
also occur without administration of a placebo. It may be due to
spontaneous remission, regression toward the mean, life changes, the
passage of time, or other factors. The placebo effect is the
difference between the placebo response and changes that occur without
the administration of a placebo (Kirsch, 1985, 1997).
In the preceding section, we evaluated the placebo response as a
proportion of the response to antidepressant medication. The data
suggest that at least 75% of the drug response is a placebo response,
but it does not tell us the magnitude of the placebo effect. What
proportion of the placebo response is due to expectancies generated by
placebo administration, and what proportion would have occurred even
without placebo administration? That is a much more difficult question
to answer. We have not been able to locate any studies in which pre-
and posttreatment assessments of depression were reported for both a
placebo group and a no-treatment or wait-list control group. For that
reason, we turned to psychotherapy outcome studies, in which the
inclusion of untreated control groups is much more common.
We acknowledge that the use of data from psychotherapy studies as a
comparison with those from drug studies is far less than ideal.
Participants in psychotherapy studies are likely to differ from those
in drug studies on any number of variables. Furthermore, the
assignment of participants to a no-treatment or wait-list control
group might also effect the course of their disorder. For example,
Frank (1973) has argued that the promise of future treatment is
sufficient to trigger a placebo response, and a wait-list control
group has been conceputalized as a placebo control group in at least
one well-known outcome study (Sloane, Staples, Cristol, Yorkston, &
Whipple, 1975). Conversely, one could argue that being assigned to a
no-treatment control group might strengthen feelings of hopelessness
and thereby increase depression. Despite these problems, the
no-treatment and wait-list control data from psychotherapy outcome
studies may be the best data currently available for estimating the
natural course of untreated depression. Furthermore, the presence of
both types of untreated control groups permits evaluation of Frank's
(1973) hypothesis about the curative effects of the promise of
treatment.
Study Characteristics
Studies assessing changes in depression among participants assigned to
wait-list or no-treatment control groups were obtained from the
computer search described earlier, supplemented by an examination of
previous reviews (Dobson, 1989; Free, & Oei, 1989; Robinson, Berman, &
Neimeyer, 1990). The publications that were produced by this
literature search were examined by the second author, and those
meeting the following criteria were included in the meta-analysis:
1. The sample was restricted to patients with a primary diagnosis of
depression. Studies were excluded if participants were selected
because of other criteria (eating disorders, substance abuse,
physical disabilities or chronic medical conditions), as were
studies in which the description of the patient population was
vague (e.g., "neurotic").
2. Sufficient data were reported or obtainable to calculate
within-condition effect sizes.
3. Data were reported for a wait-list or no-treatment control group.
4. Participants were assigned to experimental conditions randomly.
5. Participants were between the ages of 18 and 75.
Nineteen studies were found to meet these inclusion criteria, and in
all cases, sufficient data had been reported to allow direct
calculation of effect sizes as the mean posttreatment score minus the
mean pretreatment score, divided by the pooled SD. Although they are
incidental to the main purposes of this review, we examined effect
sizes for psychotherapy as well as those for no-treatment and
wait-list control groups.
Effect Sizes
Sample sizes and effect sizes for patients assigned to psychotherapy,
wait-list, and no-treatment are presented in Table 4. Mean pre-post
effect sizes, weighted for sample size, were 1.60 for the
psychotherapy response and 0.37 for wait-list and no-treatment control
groups. Participants given the promise of subsequent treatment (i.e.,
those in wait-list groups) did not improve more than those not
promised treatment. Mean effect sizes for these two conditions were
0.36 and 0.39, respectively. The correlation between effect sizes (r =
-.29) was not significant.
CAPTION: Table 4
Studies Including Wait-List or No-Treatment
Control Groups
Study Psychotherapy Control
n d n d
Beach & O'Leary (1992) 15 2.37 15 0.97
Beck & Strong (1982) 20 2.87 10 -0.28
Catanese et al. (1979) 99 1.39 21 0.16
Comas-Diaz (1981) 16 1.87 10 -0.12
Conoley & Garber (1985) 38 1.10 19 0.21
Feldman et al. (1982) 38 2.00 10 0.42
Graff et al. (1986) 24 2.03 11 -0.03
Jarvinen & Gold (1981) 46 0.76 18 0.34
Maynard (1993) 16 1.06 14 0.36
Nezu (1986) 23 2.39 9 0.16
Rehm et al. (1981) 42 1.23 15 0.48
Rude (1986) 8 1.75 16 0.74
Schmidt & Miller (1983) 34 1.25 10 0.11
Shaw (1977) 16 2.17 8 0.41
Shipley & Fazio (1973) 11 2.12 11 1.00
Taylor & Marshall (1977) 21 1.94 7 0.27
Tyson & Range (1981) 22 0.67 11 1.45
Wierzbicki & Bartlett (1987) 18 1.17 20 0.21
Wilson et al. (1983) 16 2.17 9 -0.02
Comparison of Participants in the Two Groups of Studies
Comparisons of effect sizes from different sets of studies is common
in meta-analysis. Nevertheless, we examined the characteristics of the
samples in the two types of studies to assess their comparability.
Eighty-six percent of the participants in the psychotherapy studies
were women, as were 65% of participants in the drug studies. The age
range of participants was 18 to 75 years (M = 30.1) in the
psychotherapy studies and 18 to 70 years (M = 40.6) in the drug
studies. Duration of treatment ranged from 1 to 20 weeks (M = 4.82) in
psychotherapy studies and from 2 to 15 weeks (M = 5.95) in
pharmacotherapy studies. The HRS-D was used in 15 drug studies
involving 2,016 patients and 5 psychotherapy studies with 191
participants. Analysis of variance weighted by sample size did not
reveal any significant differences in pretreatment HRS-D scores
between patients in the drug studies (M = 23.93, SD = 5.20) and
participants in the psychotherapy studies (M = 21.34, SD = 5.03). The
Beck Depression Inventory (BDI) was used in 4 drug studies involving
261 patients and in 17 psychotherapy studies with 677 participants.
Analysis of variance weighted by sample size did not reveal any
significant differences in pretreatment BDI scores between
participants in drug studies (M = 21.58, SD = 8.23) and those in
psychotherapy studies (M = 21.63, SD = 6.97). Thus, participants in
the two types of studies were comparable in initial levels of
depression. These analyses also failed to reveal any pretreatment
differences as a function of group assignment (treatment or control)
or the interaction between type of study and group assignment.
Estimating the Placebo Effect
Just as drug effects can be estimated as the drug response minus the
placebo response, placebo effects can be estimated as the placebo
response minus the no-treatment response. Using the effect sizes
obtained from the two meta-analyses reported above, this would be 0.79
(1.16 - 0.37). Figure 2 displays the estimated drug, placebo, and
no-treatment effect sizes as proportions of the drug response (i.e.,
1.55 SDs). These data indicate that approximately one quarter of the
drug response is due to the administration of an active medication,
one half is a placebo effect, and the remaining quarter is due to
other nonspecific factors.
[pre0010002afig2a.gif]
Figure 2. Drug effect, placebo effect, and natural history effect
as proportions of the response to antidepressant medication.
Discussion
No-treatment effect sizes and effect sizes for the placebo response
were calculated from different sets of studies. Comparison across
different samples is common in meta-analyses. For example, effect
sizes derived from studies of psychodynamic therapy are often compared
to those derived from studies of behavior therapy (e.g., Andrews &
Harvey, 1981; Smith et al., 1980). Nevertheless, comparisons of this
sort should be interpreted cautiously. Participants volunteering for
different treatments might come from a different populations, and when
data for different conditions are drawn from different sets of
studies, participants have not been assigned randomly to these
conditions. Also, assignment to a no-treatment or wait-list control
group is not the same as no intervention at all. Therefore, our
estimates of the placebo effect and natural history component of the
response to antidepressant medication should be considered tentative.
Nevertheless, when direct comparisons are not available, these
comparisons provide the best available estimates of comparative
effectiveness. Furthermore, in at least some cases, these estimates
have been found to yield results that are comparable to those derived
from direct comparisons of groups that have been randomly assigned to
condition (Kirsch, 1990; Shapiro & Shapiro, 1982).
Unlike our estimate of the effect of natural history as a component of
the drug response, our estimate of the placebo response as a
proportion of the drug response was derived from studies in which
participants from the same population were assigned randomly to drug
and placebo conditions. Therefore, the estimate that only 25% of the
drug response is due to the administration of an active medication can
be considered reliable. Confidence in the reliability of this estimate
is enhanced by the exceptionally high correlation between the drug
response and the placebo response. This association is high enough to
suggest that any remaining variance in drug response is error variance
associated with imperfect reliability of measurement. Examining
estimates of active drug and inactive placebo responses as a function
of drug type further enhances confidence in the reliability of these
estimates. Regardless of drug type, the inactive placebo response was
approximately 75% of the active drug response.
We used very stringent criteria in selecting studies for inclusion in
this meta-analysis, and it is possible that data from a broader range
of studies would have produced a different outcome. However, the
effect size we have calculated for the medication effect (D = .39) is
comparable to those reported in other meta-analyses of antidepressant
medication (e.g., Greenberg et al., 1992, 1994; Joffe, Sokolov, &
Streiner, 1996; Quality Assurance Project, 1983; Smith et al., 1980;
Steinbrueck, Maxwell, & Howard, 1983). Comparison with the Joffe et
al. (1996) meta-analysis is particularly instructive, because that
study, like ours, included estimates of pre-post effect sizes for both
drug and placebo. Although only two studies were included in both of
these meta-analyses and somewhat different calculation methods were
used, ^2 their results were remarkably similar to ours. They reported
mean pre-post effect sizes of 1.57 for medication and 1.02 for placebo
and a medication versus placebo effect size of .50.
Our results are in agreement with those of other meta-analyses in
revealing a substantial placebo effect in antidepressant medication
and also a considerable benefit of medication over placebo. They also
indicate that the placebo component of the response to medication is
considerably greater than the pharmacological effect. However, there
are two aspects of the data that have not been examined in other
meta-analyses of antidepressant medication. These are (a) the
exceptionally high correlation between the placebo response and the
drug response and (b) the effect on depression of active drugs that
are not antidepressants. Taken together, these two findings suggest
the possibility that antidepressants might function as active
placebos, in which the side-effects amplify the placebo effect by
convincing patients of that they are receiving a potent drug.
In summary, the data reviewed in this meta-analysis lead to a
confident estimate that the response to inert placebos is
approximately 75% of the response to active antidepressant medication.
Whether the remaining 25% of the drug response is a true pharmacologic
effect or an enhanced placebo effect cannot yet be determined, because
of the relatively small number of studies in which active and inactive
placebos have been compared (Fisher & Greenberg, 1993). Definitive
estimates of placebo component of antidepressant medication will
require four arm studies, in which the effects of active placebos,
inactive placebos, active medication, and natural history (e.g.,
wait-list controls) are examined. In addition, studies using the
balanced placebo design would be of help, as these have been shown to
diminish the ability of subjects to discover the condition to which
they have been assigned (Kirsch & Rosadino, 1993).
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_________________________________________________________________
^1 A reviewer suggested that because effect sizes are essentially
z-scores in a hypothetically normal distribution, one might use
percentile equivalents when examining the proportion of the drug
response duplicated by the placebo response. As an example of why this
should not be done, consider a treatment that improves intelligence by
1.55 SDs (which is approximately at the 6^th percentile) and another
that improves it by 1.16 SDs (which is approximately at the 12^th
percentile). Our method indicates that the second is 75% as effective
as the first. The reviewer's method suggests that it is only 50% as
effective. Now let's convert this to actual IQ changes and see what
happens. If the IQ estimates were done on conventional scales (SD =
15), this would be equivalent to a change of 23.25 points by the first
treatment and 17.4 points by the second. Note that the percentage
relation is identical whether using z-scores or raw scores, because
the z-score method simply divides both numbers by a constant.
^2 Instead of dividing mean differences by the pooled SDs, Joffe et
al. (1996) used baseline SDs, when these were available, in
calculating effect sizes. When baseline SDs were not available, which
they reported to be the case for most of the studies they included,
they used estimates taken from other studies. Also, they used a
procedure derived from Hedges and Olkin (1995) to weight for
differences in sample size, whereas we used the more straightforward
method recommended by Hunter and Schmidt (1990).
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