[Paleopsych] Prevention & Treatment: Listening to Prozac butHearing Placebo
Steve Hovland
shovland at mindspring.com
Sat Oct 22 21:07:27 UTC 2005
There was a studyin Britain not so long ago
where Zoloft, placebo, and St. John's Wort
produced similar results. I have heard
that St. Johns is the most common prescription
for depression in Germany.
-----Original Message-----
From: paleopsych-bounces at paleopsych.org
[mailto:paleopsych-bounces at paleopsych.org]On Behalf Of Lynn D. Johnson,
Ph.D.
Sent: Saturday, October 22, 2005 12:57 PM
To: The new improved paleopsych list
Subject: Re: [Paleopsych] Prevention & Treatment: Listening to Prozac
butHearing Placebo
Recent 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. Note than there is little placebo response seen in anti-psychotic
and ADHD drugs, presumably because of the difference in the patient
population.
Lynn
Premise Checker wrote:
> 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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