Millions of people are affected by depression each year. Approximately 6.6% of adults, or about 13 million of the adult US population, are estimated to have had episodes that meet current criteria for MDD within a year's time (Kessler et al., 2007). Based on a single interview, the lifetime prevalence of MDD is estimated to be 16.2%, or 33 million of the adult population (Kessler et al., 2007). However, lifetime estimates from single interviews are conservative because interviewees vary greatly in their current age and they have difficulty recalling prior episodes of depressive symptoms (Wells and Horwood, 2004; Kessler et al., 2005; Kruijshaar et al., 2005; Moffitt et al., 2009). Longitudinal studies of community samples in which participants are repeatedly interviewed yield higher estimates of the lifetime prevalence of MDD, often exceeding 40% (Wells and Horwood, 2004; Kruijshaar et al., 2005; Moffitt et al., 2009).

Most episodes that meet diagnostic criteria for MDD are associated with stressors (e.g., bereavement, marital difficulties, interpersonal conflict, financial difficulties, health issues). For instance, in a longitudinal study of a large community sample of twins of both sexes, 88.1% of diagnosed episodes of MDD were associated with a stressor of some sort (Keller et al., 2007). Only 11.9% of episodes appeared to be endogenous (depression in the absence of an environmental trigger). However, even many of the apparently endogenous episodes could have reflected a reluctance to disclose stressors of a sensitive nature (Leff et al., 1970).

There are two broadly divergent approaches to the relationship between stressors and depression. In some way, the relationship must be mediated by the brain, but the neurological causes of depression are generally acknowledged to be unknown (Berton and Nestler, 2006). Many researchers, certain that depressive symptoms are maladaptive, search for evidence of stress-induced malfunction in the brain (for recent reviews, see Krishnan and Nestler, 2008; Savitz and Drevets, 2009). These researchers attempt to identify the neurological mechanisms responsible for the chronic nature of depression and its association with a variety of medical conditions (McEwen, 1998, 2007), the structural changes in the brain associated with depression and their effects on cognition (Sapolsky, 1996, 2000, 2001; Duman, 2004; Duman and Monteggia, 2006; Savitz and Drevets, 2009), and the neurochemical pathways of depression, including the mechanisms by which antidepressant medications (ADMs) reduce symptoms (for a review, see Krishnan and Nestler, 2008). An integrating theme in this research is that the neurochemistry of depression is disordered, although the precise neurochemicals that are dysregulated are the subject of much debate and research. Monoamine neurotransmitters, glucocorticoids, neurotrophins, and cytokines are the major chemicals thought to be involved in depression (Krishnan and Nestler, 2008). Regardless of the role of other chemicals, there seems to be consensus that the monoamine neurotransmitters – particularly serotonin (5-hydroxytryptamine or 5-HT), norepinephrine (NE), and, to a lesser extent, dopamine (DA) – are directly or indirectly involved in the biochemical pathways to depression (Krishnan and Nestler, 2008). All ADMs, for instance, act on monoamines through a variety of mechanisms (see below). Moreover, research on rodents in which monoaminergic receptors have been knocked out, or monoamine transmission has been disabled, has provided strong experimental evidence that monoamines play a role in depressive symptoms and in the antidepressant response, particularly 5-HT and NE (Heisler et al., 1998; Mayorga et al., 2001; Cryan et al., 2004; Dziedzicka-Wasylewska et al., 2006).

Other researchers argue that depressive symptoms might be better described by adaptive mechanisms that respond to stressors and threats (Price et al., 1994; Nesse, 2000, 2004; Watson and Andrews, 2002; Allen and Badcock, 2003, 2006; Hagen, 2003; Gilbert, 2006; Horwitz and Wakefield, 2007; Andrews and Thomson Jr., 2009; Nesse and Ellsworth, 2009; Price, 2009). These hypotheses do not propose that all episodes of depression are adaptive since all evolved adaptations in the body are susceptible to malfunction and disorder at some rate. Rather, the issue is whether the symptoms currently used to diagnose depressive disorder accurately distinguish the disordered state from normal, adaptive processes (Spitzer and Wakefield, 1999; Horwitz and Wakefield, 2007; Wakefield et al., 2007, 2010). Some adaptationist hypotheses restrict their application to symptoms and episodes that do not meet diagnostic thresholds, but such restrictions are intended to exclude true instances of disorder. Consequently, these hypotheses are not falsified in any rigorous sense by evidence that current diagnostic criteria are inaccurate. In any event, evolutionarily oriented researchers tend to focus on the ecological mapping of the symptoms of depression (and the cognitive and behavioral outputs) to the stressors that trigger episodes (Gilbert and Allan, 1998; Hagen, 1999, 2002; Badcock and Allen, 2003; Gilbert et al., 2004; Keller and Nesse, 2004, 2005, 2006; Andrews et al., 2007; Wrosch and Miller, 2009), largely treating the brain as a black box whose internal structure and operation are unknown.

Both approaches agree that the body has evolved adaptations for responding to stress. They both believe that, in general, environmental stressors trigger mechanisms in the body that regulate multiple body systems so that cognition and behavior can be adaptively modulated to meet the environmental challenge. They also agree that these evolved stress response mechanisms can malfunction. They primarily differ in what constitutes evidence of depressive disorder and the causes of depressive disorder.

Most articulations of the monoamine disorder hypothesis propose that forebrain levels of monoamine neurotransmitters are depleted in depressive disorder – particularly serotonin (5-HT), norepinephrine (NE) and, to a lesser extent, dopamine (DA). However, some have suggested that monoaminergic transmission may be enhanced in depression (Sapolsky, 2004). The precise direction of association between depression and forebrain monoamine levels is not crucial for the present paper. It is sufficient to say that the neurochemical disorder hypothesis proposes that monoamines, particularly 5-HT and NA, are perturbed in some way.

But monoaminergic perturbation, per se, is not evidence of disorder. The most prominent definition of disorder is based in evolutionary theory (Spitzer, 2007), and it argues that at heart of every disorder is an evolved adaptation that is malfunctioning (Wakefield, 1992). Under this definition, the claim that depression is a disorder of too much or too little of a neurotransmitter is ultimately a claim that the evolved mechanisms that control neurotransmitter levels are malfunctioning.

Monoamine neurotransmitter levels are normally under homeostatic control (Best et al., 2010). Homeostasis involves the regulation of an important substance or physiological parameter within a narrow range around an equilibrium. Minimally, homeostatic mechanisms have a sensor for determining how far the parameter deviates from the equilibrium and feedback mechanisms for bringing the parameter back to equilibrium (Woods, 2009). For instance, the homeostatic control of core body temperature involves neuronal sensors in the preoptic anterior hypothalamus that connect with various efferent pathways to exert feedback and keep temperature at equilibrium (Romanovsky, 2007). Additionally, many homeostatic mechanisms can raise or lower the equilibrium in response to environmental contingencies. Thus, the body often responds to an infection by raising the core body temperature equilibrium– otherwise known as fever (Romanovsky et al., 2005). Feedback mechanisms then maintain core body temperature around this elevated equilibrium. Because some infections last for weeks or months, fever can last for extended periods of time.

If depression is, in whole or in part, a disorder in the homeostatic mechanisms that regulate monoamine transmission, then it could involve malfunction in: (1) the sensors that monitor monoamine levels (2) the feedback mechanisms that maintain monoamine levels at the equilibrium; or (3) the mechanisms that alter the equilibrium point.

Although most adaptationist research treats the brain as a black box, a common (if unstated) implication of all adaptationist hypotheses for depression is that the relevant neurochemistry is altered to change cognition and behavior in ways that adapt the organism to the environmental contingencies that triggered the episode. An adaptationist perspective on the chronic nature of depression suggests that, like fever, monoamines may be under the control of a homeostatic mechanism that maintains forebrain levels at an altered equilibrium for an extended period of time, presumably because the nature of the stressor requires it (see, e.g., Andrews and Thomson Jr., 2009).

The disruption of a mechanism by pharmacological intervention can reveal whether it is under homeostatic control (Young and Goudie, 1995). Acute exposure to opiates inhibits the firing of noradrenergic neurons in the locus coeruleus by suppressing cyclic adenosine 3′,5′-monophosphate (cAMP) levels (Nestler and Aghajanian, 1997). However, with long-term (chronic) exposure, firing rates return to normal because a negative feedback mechanism upregulates the cAMP pathway so that it counteracts the drug's effect and returns the system to the equilibrium (Nestler and Aghajanian, 1997). The neurological changes brought about by homeostatic mechanisms that counteract the disrupting effects of chronic drug exposure are referred to as oppositional tolerance (Young and Goudie, 1995; Fava and Offidani, 2011). These counteractive forces can cause the system to overshoot the equilibrium when the drug is abruptly discontinued (Young and Goudie, 1995; Fava and Offidani, 2011). Thus, when an opioid receptor antagonist is used to abruptly block an opiate, the upregulated cAMP pathway causes firing rates to exceed the normal rate (Nestler and Aghajanian, 1997).

Some researchers have called for greater research on the neurological responses of homeostatic mechanisms to chronic psychotropic drug use, including ADMs (Hyman and Nestler, 1996). Others have specifically hypothesized that oppositional tolerance to ADMs may develop in depression (Fava and Offidani, 2011). Whether the monoamines thought to cause major depression are under homeostatic control has important implications for debates about whether most episodes of MDD are instances of a properly functioning adaptation or true instances of disorder. Homeostatic mechanisms are classic examples of adaptations. Survival and reproduction require the internal environment of the organism to be regulated (Hochochka and Somero, 2002; Woods, 2009). The machinery needed to exert negative feedback to maintain physiological conditions within a narrow band around an equilibrium are often complex and difficult to explain by any process other than natural selection. Consequently, evidence that monoamine neurotransmitters were under homeostatic control in patients diagnosed with MDD would suggest that those mechanisms were not malfunctioning.

In this paper, we focus on a straightforward implication of the hypothesis that depression neurochemistry is under homeostatic control: ADM-induced perturbations to monoamine neurotransmitter levels should trigger oppositional tolerance. Previous research provides some support for oppositional tolerance to ADMs in patients diagnosed with MDD. It is not uncommon for depressive symptoms to reemerge after remission while patients are on maintenance ADM therapy (Byrne and Rothschild, 1998), which is consistent with tolerance (Fava and Offidani, 2011). Other support comes from a meta-analysis of studies examining the risk of relapse among patients with remitted symptoms after ADM treatment had been discontinued (Viguera et al., 1998). The authors found that the risk of relapse was positively associated with the duration of ADM treatment. While this relationship was not statistically significant, it became marginally significant in a re-analysis that included an additional study (Baldessarini et al., 2002). Such evidence suggests oppositional tolerance because longer treatment periods provide homeostatic mechanisms with more time to make neurological changes that oppose pharmacological perturbation (Fava and Offidani, 2011). However, several subsequent meta-analyses have failed to find any evidence that treatment duration affected the risk of relapse after discontinuation (Geddes et al., 2003; Kaymaz et al., 2008; Glue et al., 2010). At present, evidence of oppositional tolerance in depression is equivocal.

Antidepressant medications vary widely in the degree to which they perturb monoamine levels in the brain (see below). Consequently, perturbational differences in ADMs could affect the degree of oppositional tolerance. This is analogous to the negative feedback forces that springs exert when they are displaced from their equilibrium position. As one pulls a spring from its equilibrium position, the spring exerts an oppositional force that attempts to bring the spring back to equilibrium; the more further one displaces the spring from its equilibrium position, the greater the oppositional force that the spring produces. Similarly, ADMs with greater perturbational effects should trigger stronger oppositional forces that attempt to bring monoamine levels back to equilibrium. The buildup of oppositional tolerance under ADM treatment could then cause the system to overshoot its equilibrium upon discontinuation, and the degree of overshoot should be proportional to the perturbational effect of the ADM.

We hypothesize that monoamine levels are under homeostatic control in patients diagnosed with MDD, and that ADM treatment will cause oppositional tolerance. There are two implications for the present paper. First, the buildup of oppositional tolerance during ADM treatment will attempt to bring monoamine levels back to equilibrium, which will cause them to overshoot the equilibrium after ADM treatment is discontinued. Second, the degree to which monoamine levels overshoot the equilibrium will be positively related to the perturbational effect of the ADM. We do not directly test these implications because we lack relevant perturbational data in humans. Rather, we test two predictions that follow from them.

Since depressive symptoms are under monoaminergic control, ADM treatment tends to decrease depressive symptoms. However, oppositional tolerance during ADM treatment should build pressure for increasing symptoms (Fava and Offidani, 2011). After ADM treatment is discontinued, the overshoot of monoamines will cause an increase in symptoms that could exceed diagnostic thresholds for a relapse or a recurrence of MDD (hereafter, just relapse). Since oppositional tolerance will be absent in patients who resolve their episodes without ADM treatment, we predict that the risk of relapse among patients who discontinue ADM treatment will be higher than the risk of relapse among patients who remit without ADM treatment. Second, and more importantly, we predict that the risk of relapse will be positively related to the perturbational effect of the ADM.

We conduct a meta-analysis to test these predictions. Other meta-analyses of ADM discontinuation studies have been conducted (Viguera et al., 1998; Geddes et al., 2003; Kaymaz et al., 2008; Glue et al., 2010), but they have not been specifically designed to address these questions. Moreover, our meta-analysis offers an important methodological improvement. ADM discontinuation studies vary widely in the criteria that are used to define a relapse. Some studies use a threshold score on a single assessment instrument, while others may use a complex combination of different instruments assessed over multiple time periods. These definitional criteria can have a dramatic affect on the relapse rate (Montgomery and Dunbar, 1993; Keller et al., 1998). Studies that use more stringent relapse criteria make it more difficult for an increase in symptoms to qualify as a relapse, resulting in lower relapse rates (Montgomery and Dunbar, 1993). Prior meta-analyses have not attempted to control for the variability in the stringency of relapse criteria in discontinuation studies, possibly because it is difficult to objectively compare relapses defined by different instruments. For each study, we construct a variable (called stringency) that can be objectively calculated as the number of assessment hurdles that have to be passed before an increase in symptoms will be defined as a relapse. In general, studies with more assessment hurdles should make it more difficult for an increase in symptoms to qualify as a relapse. If so, our stringency variable should be a negative predictor of the relapse rate.

All ADMs have multiple pharmacological effects by binding to various receptors, transporters, and enzymes. This could make it difficult to classify them in terms of their perturbational impact. However, one relatively clear measure of perturbational impact is by whether cortical levels of the monoamine neurotransmitters– 5-HT, NE, and DA – are actually affected. When cortical levels of a neurotransmitter are influenced by an ADM, the drug exerts perturbational influence through all the receptors, transporters, and enzymes to which the neurotransmitter binds.

The perturbational effects of ADMs on monoamine forebrain levels have not been directly measured in humans because it requires invasive techniques. However, microdialysis studies of rodents have examined the perturbational effects of ADMs in various brain regions. Since there is regional variability in the effects of ADMs, we look at microdialysis studies assessing the in vivo effects of ADMs on rodent medial prefrontal cortex (mPFC) monoamine levels. We choose the mPFC because it plays an important role in behavioral depression in rodents (Amat et al., 2005, 2006, 2010; Baratta et al., 2009). In human depression, the ventrolateral prefrontal cortex (VLPFC) is thought to be important (Drevets, 1998; Andrews and Thomson Jr., 2009), which may seem incongruent with the rodent literature. However, the rodent brain is organized differently than the human brain, and the rodent mPFC is a likely structural and functional homolog to the human VLPFC (Uylings and Vaneden, 1990; Kesner, 2000; Uylings et al., 2003). The perturbational effects of ADMs on rodent mPFC monoamine levels may correspond to their perturbational effects in human brain regions implicated in depression, such as the VLPFC. Unless otherwise indicated, we rely on microdialysis studies in which a single dose in the 10–15mg/kg range was systemically administered (either by intraperitoneal injection or oral administration). This dose range is commonly used and approximates clinically relevant doses (Koch et al., 2003). From these studies, we extracted the average percent increase of each monoamine (5-HT, NE, and DA) above baseline in the mPFC (see Table ​Table11).

Microdialysis studies of mPFC monoamine levels can be found for most of the major standalone ADMs used in discontinuation studies. However, we lack mPFC microdialysis data for the two monoamine oxidase inhibitors (MAOIs) – phenelzine and selegiline – that have been used in discontinuation studies. Phenelzine is a dual MAO-A/B inhibitor, and so is selegiline at the doses used in the discontinuation study (Stahl, 2008). As noted above, the inhibition of MAO-A primarily increases 5-HT and NE. Although MAO-B does not metabolize 5-HT or NE, it does metabolize β-phenylethylamine, which is related to the release of catecholamines, including 5-HT and NE (Kitaichi et al., 2010). The inhibition of MAO-B can therefore indirectly increase 5-HT and NE as well as directly increase DA (Kitaichi et al., 2010).

Of the ADMs, dual MAO-A/B inhibitors exert the most powerful effects on monoamine levels. Due to differences in regional effects of ADMs, many ADMs do not increase monoamine levels when averaged across the whole rodent brain, yet dual MAO-A/B inhibitors are powerful enough to do so (Campbell and Marshall, 1974; Fuller et al., 1974; Renard et al., 2004; Bano et al., 2010). In one study, a single dose of phenelzine increased whole brain levels of 5-HT by 240%, NE by 150%, and DA by 170% (Parent et al., 2000).

In mPFC, dual MAO-A/B inhibitors tend to have greater effect than the whole brain average. The inhibition of MAO-A by itself increases mPFC levels of 5-HT and NE by 200–300% (Finberg et al., 1993; Bel and Artigas, 1995), but the simultaneous inhibition of MAO-A and MAO-B causes increases of at least 400% (Bel and Artigas, 1995; Takahashi et al., 2000; Kitaichi et al., 2010). We therefore conservatively assume that phenelzine and selegiline (at dual inhibition doses) increase mPFC monoamine levels by 400% above baseline (Table ​(Table1).1). However, since we do not have precise mPFC measurements for phenelzine and selegiline, for our analyses we capped the maximum perturbational impact of ADMs at 400%. We label the perturbational effect of a drug for each monoamine as follows: p5HT (for serotonin), pNE (for norepinephrine), and pDA (for dopamine).

The perturbational effects of other ADMs generally correspond to the class to which they belong, but there are exceptions and, more importantly, differences in degree. The selective serotonin reuptake inhibitors (SSRIs) selectively bind to the serotonin transporter, and so they tend to increase cortical levels of serotonin. The effects of citalopram, fluoxetine, fluvoxamine, paroxetine, and sertraline on mPFC monoamine levels were assessed in a single study (Bymaster et al., 2002). Fluoxetine is unique among the SSRIs in that it also increases NE and, to a lesser extent, DA in the mPFC (Bymaster et al., 2002; Stahl, 2008). The mechanism is not by reuptake blockade, but by antagonism of 5-HT_2C receptors (Stahl, 2008). While we lack microdialysis data for escitalopram in the 10–15mg/kg range, escitalopram is the active S-enantiomer of citalopram (Stahl, 2008), so we use the same perturbational values as for citalopram.

The serotonin norepinephrine reuptake inhibitors (SNRIs) selectively bind to both the 5-HT and NE transporters, although, as a class, they tend to have greater affinity for the 5-HT transporter than the NE transporter. ADMs that inhibit NE reuptake also tend to increase prefrontal levels of DA to some degree because there are few DA transporter molecules in the prefrontal cortex, so DA has a tendency to diffuse widely from the synapse (Stahl, 2008). DA eventually gets taken back into neurons by the NE transporter, which has a higher affinity for DA than NE (Stahl, 2008). We relied on several studies (Kihara and Ikeda, 1995; Koch et al., 2003; Marien et al., 2007; Weikop et al., 2007) to get data on the mPFC monoamine effects of the SNRIs used in discontinuation studies – desvenlafaxine, duloxetine, milnacipran, and venlafaxine. Venlafaxine does not have much effect on NE at 15mg/kg, but it has a larger effect at 40mg/kg (Koch et al., 2003). The discontinuation studies of venlafaxine use doses that are much higher than the recommended dose, so we use the data for 40mg/kg. We lacked microdialysis data for desvenlafaxine, but it is the active metabolite of venlafaxine (Stahl, 2008). In the discontinuation study of desvenlafaxine, it was also administered at doses that are much higher than the recommended dose, so we use the same perturbational values as for venlafaxine.

Two types of studies are relevant to these predictions. In ADM extension studies, patients diagnosed with MDD are initially randomly assigned to ADM or placebo during the treatment phase, and then remitters in both groups are followed in an extension phase in which they continue to receive the same treatment. Since our first prediction relates to the risk of relapse after remission without ADM treatment, we only consider the Placebo–Placebo arms of extension studies. We put placebo in its own class (PBO) and assign to placebo treatment a perturbational score of 100 (no effect on baseline levels) for each of the monoamines.

In ADM discontinuation studies, all patients diagnosed with MDD go through an initial ADM treatment phase and then remitters go through a discontinuation phase in which they are randomly assigned, under double-blind conditions, to either continued treatment (Drug–Drug) or placebo (Drug–Placebo). Since our second prediction deals with the risk of relapse after ADM discontinuation, we only consider the Drug–Placebo arms of discontinuation studies.

In our meta-analysis, we include all the randomized, double-blind, placebo-controlled ADM extension studies we can find since patients in the Placebo–Placebo arms of these studies have not received any pharmacological treatment. However, we limit analysis to ADM discontinuation studies in which patients receive chronic treatment of a single ADM from one of the major standalone classes– the MAOIs, the TCAs, the SNRIs, or the SSRIs. As a practical matter, microdialysis data on the mPFC monoaminergic effects of ADMs from outside these classes is limited.

Table ​Table33 provides basic summary statistics. The PBO studies had the unadjusted relapse rate, which is consistent with our first prediction. However, this is also consistent with the possibility that the PBO studies tended to include patients with a lower risk of relapse (e.g., patients were less likely to have a prior history of depression, or they were more resistant to pharmacological treatment). Moreover, the PBO studies had the highest dropout rate during acute treatment, while the ADM classes had similar dropout rates. The dropout rate pattern suggests a potential selection bias problem for the PBO group, so we include the dropout rate as a covariate in all our regression analyses, unless otherwise specified. The SSRI studies had the second highest raw relapse rate, after the TCAs, while the MAOIs had the second lowest raw relapse rate of the ADM classes.

While the results are robust, their implications depend on their interpretation, so we first discuss several potential confounds. Is it possible, for instance, that the results do not reflect the risk of relapse, but rather withdrawal symptoms? The discontinuation of ADM can cause withdrawal symptoms (also known as discontinuation syndrome) that are unrelated to relapse, but may sometimes be mistaken for relapse (Haddad, 1997). Such mistakes are more likely with untrained physicians (Haddad, 1997), so it should be less of a problem in ADM discontinuation studies where trained psychiatrists are employed to evaluate putative cases of relapse. Regardless, drugs with longer half-lives are less likely to show discontinuation syndrome (Michelson et al., 2000; Fava, 2006). If discontinuation syndrome were being mistaken for relapse, then drugs with longer half-lives should have lower relapse rates. To test this possibility, we added half-life to the best-fitting model and eliminated the perturbational variables (results not shown). The resultant model did not fit as well, and, in fact, half-life was a significant positive predictor of relapse rate. This is because drugs with stronger perturbational effects on 5-HT tend to have longer half-lives. We therefore have no evidence that withdrawal symptoms were mislabeled as relapses.

There is some evidence that ADM treatment resistant patients may have higher relapse rates (Rush et al., 2006). Is it possible that studies using ADMs with stronger perturbational properties had higher relapse rates, not due to the pharmacological properties of the drugs used, but because they also had higher proportions of treatment resistant patients participating in them? There are two ways this could happen. First, studies using stronger ADMs could have been more likely to target treatment resistant patients at the outset. Second, the characteristics of patient samples in discontinuation studies may change during treatment due to dropouts caused by the treatment, causing a potential selection bias problem.

A related issue is whether the PBO class (denoted by the Placebo–Placebo arms of the extension studies) was fundamentally different from the other classes (denoted by the Drug–Placebo arms of the ADM discontinuation studies). For instance, perhaps the PBO studies targeted fundamentally different samples at the outset, or perhaps they had higher dropout during the acute treatment phase so that patient populations were substantially different by the beginning of the extension phase. Consistent with this, the PBO studies did have a higher dropout rate and a lower relapse rate than the ADMs as a group. Of course, this result might also be expected if the PBO studies were quantitatively different only because they were on one end of the perturbational continuum.

To deal with these issues in our analyses, we included two variables as covariates that captured differences in the initial patient populations: whether treatment resistant patients were being targeted, and the minimum number of depressive episodes required for entry into the study. Neither variable was significant in any of our analyses, and their inclusion did not change the statistical significance of any result. We also included the dropout rate during acute treatment because the PBO studies had significantly higher dropout rates, and the dropout rate in the ADM discontinuation studies was positively associated with the duration of treatment. Both findings suggested that the dropout rate during treatment might be a proxy for the degree of selection bias at discontinuation. If selection bias problems were driving the lower relapse rate in the PBO studies or the relationship between relapse rate and perturbational variables, then dropout rate during the treatment phase could predict relapse, either as a main effect or in interaction with binary treatment status (Placebo vs. Drug). Neither term was significant in any of our analyses, nor did they affect the significance of any result.

In any event, the strongest reason for inferring that the PBO studies were not qualitatively different from the other studies is that excluding them did not significantly change the parameter estimates of any variables, including the perturbational variables. This suggests that the PBO studies simply anchor one end of the perturbational continuum and increase the power of our analyses. (Similarly, even though we had to make assumptions about the prefrontal perturbational effects of phenelzine and selegiline based on evidence from other dual MAO-A/B inhibitors, our results did not change significantly if we excluded the MAOI studies.)

Another potential confound is that we are only using published studies in our meta-analysis. It has been shown, for instance, that there is a publication bias of studies dealing with the efficacy of ADMs during acute treatment (Turner et al., 2008). For publication bias to explain our results, the likelihood of publication would have to be related to the perturbational effects of ADMs. This seems to us unlikely, but we cannot rule out this possibility.

Other limitations of this meta-analysis highlight difficulties with the ADM literature on which it is based. To address the question of how ADM discontinuation affects relapse rates, we were forced to rely on between-study relapse rates that may be biased by a number of other between-study differences, such as how relapse is defined. Prior meta-analyses have not dealt directly with this issue, despite evidence that definitional criteria affect relapse rates (Montgomery and Dunbar, 1993; Keller et al., 1998). We constructed a stringency variable that measured the number of assessment hurdles that had to be passed before an increase in symptoms was deemed to be a relapse. As expected, this variable was a significant negative predictor of the relapse rate, and it generally improved the explanatory power of other variables in our analyses (results not shown), suggesting that the failure to take this variable into account can obscure findings. Nevertheless, our findings are necessarily correlational.