[Paleopsych] Prevention & Treatment: Listening to ProzacbutHearing Placebo
Steve Hovland
shovland at mindspring.com
Sun Oct 23 15:19:08 UTC 2005
By the way, does anyone have a source for
the original science indicating a relationship
between seratonin and depression?
-----Original Message-----
From: paleopsych-bounces at paleopsych.org
[mailto:paleopsych-bounces at paleopsych.org]On Behalf Of Todd I. Stark
Sent: Sunday, October 23, 2005 7:19 AM
To: The new improved paleopsych list
Subject: Re: [Paleopsych] Prevention & Treatment: Listening to
ProzacbutHearing Placebo
Kirsch and Sapirstein (1998) was actually the lead article for a whole
series of articles in Prevention and Treatment. If you read the rest of the
series, you'll see that there is good evidence that the placebo effect and
the drug effect are largely independent. That is, the effect of SSRIs and
the effect of expectancy both contribute to the clinical outcome, but
neither depends entirely upon the other. The common interpretation that
evidence of expectancy effects is also evidence against the efficacy of
SSRIs is inaccurate, or at least very misleading. In my opinion, a more
consistent interpretation would be that expectancy effects and drug effects
are both important factors in clinical outcome for depression, and that the
investigative goal should probably focus on the conditions facilitating
each, and the tradeoffs for each.
kind regards,
Todd
Lynn D. Johnson, Ph.D. wrote on 10/22/2005, 9:35 PM:
The interesting thing about this is that the placebo effect for
antidepressants has been consistently increasing since the 1980s when Prozac
came out, and Kramer wrote Listening to Prozac.* This suggests that loss of
hope is a key ingredient in depression; how that interfaces with the Omega-3
evidence, I wish someone would tell me.
Lynn
*The increasing placebo effect means it is steadily harder to show a
statistically significant treatment effect for antidepressants. Discouraging
for the drug houses.
Steve Hovland wrote:
There was a studyin Britain not so long agowhere Zoloft, placebo, and St.
John's Wortproduced similar results. I have heardthat St. Johns is the most
common prescriptionfor depression in Germany.-----Original Message-----From:
paleopsych-bounces at paleopsych.org[mailto:paleopsych-bounces at paleopsych.org]O
n Behalf Of Lynn D. Johnson,Ph.D.Sent: Saturday, October 22, 2005 12:57
PMTo: The new improved paleopsych listSubject: Re: [Paleopsych] Prevention &
Treatment: Listening to ProzacbutHearing PlaceboRecent reviews of effect
size of antidepresants is around 0.2, indicating 2/10s of a standard
deviation difference between placebo and active drug. Active placebos (with
side effects) have a bigger effect size. Not!
e than there is little placebo response seen in anti-psychotic and ADHD
drugs, presumably because of the difference in the patient
population.LynnPremise Checker wrote: Listening to Prozac but Hearing
Placebo: A Meta-Analysis of Antidepressant
Medicationhttp://www.journals.apa.org/prevention/volume1/pre0010002a.htmlPre
vention & 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,
CTand 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
standar!
dized 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: Ta!
ble 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 PlaceboStudy 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 disord!
ers. 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 re!
sulted 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, amy!
lobarbitone, 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) repor!
ted 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 c!
orrelations 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 size!
s 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. (19!
91) 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 respon!
se 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 expec!
tations 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 antidep!
ressants. 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 Gre!
enberg & 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 fewe!
r 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 w!
orthwhile 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 dis!
orders, 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 grou!
ps. 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 & Stro!
ng (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 & Marsh!
all (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 pharmac!
otherapy 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 d!
ifferences 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 ef!
fect (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
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