Assessing and Enhancing the Effectiveness of Antidepressants

June 13, 2014

With over 2 dozen FDA-approved antidepressants on the market, it is reasonable to ask: which antidepressants are most effective?

With over 2 dozen FDA-approved antidepressants on the market, it is reasonable to ask: which antidepressants are most effective? After decades of clinical experience and literally millions of prescriptions written over the years, it stands to reason that 1 or 2 agents have risen from the pack to outshine the rest.

Unfortunately, clinical experience shows this not to be the case. The general consensus is that despite their different mechanisms of action, all current antidepressants seem to have more or less the same effect. The functional equivalency of antidepressants is highlighted in practice guidelines and, understandably, serves as justification for restricted formulary access to more expensive agents.1 As a result, most psychiatrists choose antidepressants not on the basis of efficacy, but rather on the basis of insurance coverage, adverse-effect profiles, or particular clinical features of depression (eg, melancholic, atypical, anxious features), for which some differences in efficacy do exist.

Efficacy vs effectiveness

[[{"type":"media","view_mode":"media_crop","fid":"25025","attributes":{"alt":"antidepressants","class":"media-image media-image-right","id":"media_crop_5130693316609","media_crop_h":"0","media_crop_image_style":"-1","media_crop_instance":"2226","media_crop_rotate":"0","media_crop_scale_h":"139","media_crop_scale_w":"200","media_crop_w":"0","media_crop_x":"0","media_crop_y":"0","style":"margin: 1px; float: right;","title":" ","typeof":"foaf:Image"}}]]The question of how well antidepressants work for the routine treatment of depression can be answered in terms of efficacy or effectiveness. An efficacy trial asks the question, Does the drug work under ideal circumstances? Although such trials are usually brief (6 to 8 weeks) and interventions are standardized and rarely flexible, they serve as the basis for the FDA’s approval of drugs.

“Effectiveness” concerns the success or failure of drugs in the real world. A true effectiveness study asks the question, Does the drug work under usual conditions? Effectiveness trials enroll a more heterogeneous population, often with comorbid mental illness, substance abuse, or other psychiatric diagnoses, and health care professionals are often free to offer concurrent therapies. As a result, effectiveness trials tend to have more generalizability, or external validity, to real patient populations.

Effectiveness trials help clinicians and policymakers select which medications work best for a given indication in real-world conditions. Surprisingly, despite decades of experience with antidepressants, information on their relative effective-ness is lacking, while health care costs continue to escalate. As a result, more emphasis is being placed on comparative effectiveness research, in which alternative treatments are compared under real-world conditions, and costs and adverse effects are measured in addition to clinical outcomes.

Effectiveness studies

Effectiveness trials are often large, expensive, and time-consuming. They sometimes take the form of a practical clinical trial in which multiple clinically relevant treatment regimens are compared across a large population of subjects. One such landmark study is the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial. In this NIMH-funded study, more than 4000 depressed patients in outpatient psychiatry and primary care practices received citalopram for up to 12 weeks; those who did not improve advanced to later phases that offered augmentation with, or a switch to, a second antidepressant or psychotherapy.2

Remission rates in step 1 were low (28%) and decreased further with additional steps. Cumulatively, two-thirds of patients entered remission after 4 steps.3 Direct comparison of the effectiveness of antidepressants was not possible because of the overall lack of randomization and poor statistical power.4 Despite the scope and initial aims of the study, no single antidepressant strategy or combination appeared more advantageous than any other.

Other effectiveness trials have yielded similar results. A 24-week trial that randomized patients to sertraline or citalopram found no significant difference between groups.5 Another 24-week trial of 234 patients randomized to receive sertraline or fluoxetine also found no significant difference.6 In a 2001 study, 573 patients were randomized to 1 of 3 SSRIs for 9 months; sertraline, paroxetine, and fluoxetine were equally effective.7 Patients admitted to a German psychiatric hospital for treatment of depression were followed up for 10 weeks, and response and remission rates were 68.9% and 51.9%, respectively, again with no difference among individual antidepressant agents.8 Effectiveness trials, therefore, seem to confirm the conventional wisdom that no single antidepressant works better than-or worse than-any other.

Meta-analyses of efficacy studies

Efficacy trials rarely resemble real-world conditions and, as such, tend to overestimate how well drugs work. Nevertheless, the aggregation of data from multiple efficacy trials can provide a suggestion as to the relative effectiveness of antidepressants.

In 2005, the Agency for Healthcare Research and Quality commissioned an exhaustive review of antidepressants and their use in MDD.9,10 Close to 300 studies were reviewed, many of them randomized efficacy trials that compared one antidepressant with another. There was sufficient evidence to make 4 drug-drug comparisons: 3 found no significant difference between the two drugs, while another found a small reduction (1.25 points) in the Montgomery-Åsberg Depression Rating Scale (MADRS) score in patients taking escitalopram, relative to citalopram. Findings indicate that there were no significant differences in effectiveness of antidepressants, although individual drugs did differ in terms of onset of action and ease of dosing. A 2011 update found a similarly slight benefit of both sertraline and venlafaxine over fluoxetine, as well as confirmation of escitalopram’s slight superiority over citalopram.11

In a review of 117 randomized trials involving 25,928 patients, Cipriani and colleagues12 identified slight differences between certain pairs of antidepressants. Known as “network analysis,” this technique permits comparisons of 2 drugs according to how well they perform against a common comparator. Specifically, the authors found that mirtazapine, escitalopram, venlafaxine, and sertraline had slightly greater odds of inducing a response than other antidepressants studied. They also compared relative acceptability of antidepressants (by assessing dropouts) and found the most benefit for escitalopram and sertraline, but differences were slight and of questionable clinical significance.

Because a meta-analysis is only as good as the data on which it is based, these meta-analyses must be considered in light of the very real problem of selective publication. This is the tendency for favorable results to be published, while negative or neutral results are not. In an analysis of 74 antidepressant trials registered with the FDA between 1987 and 2004, Turner and colleagues13 found that nearly half (36, or 48.6%) were negative, and the vast majority of these were either not published or were published in a way that made the drug seem favorable. Likewise, industry-sponsored studies are more likely to favor the manufacturer’s drug, often because of nuances in experimental design.14 While most researchers make every effort to include unpublished results in their meta-analyses, the “file-drawer” phenomenon of unpublished negative results may bias the conclusions of analyses that exclude the inaccessible data.15

Enhancing effectiveness

Data appear to confirm 2 stark truths about antidepressants. First, there seem to be no significant differences among them; although future research may uncover patient-specific biomarkers that favor one medication over another, none has yet done so. Second, and somewhat surprisingly, antidepressant effectiveness is quite low. Thus, in the absence of data that can predict the best antidepressant regimen for a patient, enhancing the effectiveness of an antidepressant seems to be the best strategy.

One strategy has nothing to do with antidepressants, but rather involves a reconsideration of what is being treated. Treatment-resistant depression may be better defined as “depression that is resistant to currently available treatments.” Many of these refractory cases may lie on the bipolar spectrum. Study results show that bipolar depression responds poorly to antidepressants, although what counts as “bipolarity” has been the subject of some controversy.16,17

Depression may be multiple conditions, each deserving its own unique treatment approach. Findings suggest that much of the antidepressant response in mild to moderate depression may be due to placebo effect.18 Similarly, patients with a history of trauma may do better with psychotherapy than with medications, while patients with significant anxiety may not respond as well to antidepressants and their depression might resemble a “neurotic” subtype.19,20

Another way to enhance antidepressant effectiveness is to combine antidepressants or use augmentation agents. While combination strategies have intuitive appeal and offer great flexibility, they are not always supported by the available literature. In the Combining Medications to Enhance Depression Outcomes(CO-MED) trial, 665 patients with depression were randomized to receive bupropion plus escitalopram, venlafaxine XR plus mirtazapine, or escitalopram alone. Outcomes at 12 weeks, and again at 7 months, were the same across groups.21 Similarly, while evidence exists for the efficacy of a wide range of augmentation strategies, other analyses have found relatively low effectiveness or excessive cost or adverse-effect burden of some of these approaches.22,23

The more important question may be more about whom we are treating rather than what we treat with. Recent interest in “personalized medicine” seeks to improve depression treatment by using new tools to more accurately identify whom we are treating. It has been estimated that 42% of the variance in antidepressant response can be explained by genetic variation.24 This suggests that nearly half of a patient’s response to an antidepressant may be due to his or her genetic profile. In reality, however, the genetic contribution likely involves an impractically large number of variants, each having a very small effect, that together contribute to the very complex phenotype of antidepressant response. Indeed, 2 meta-analyses, using genome-wide analysis to identify polymorphisms to predict treatment response, found only a 1.2% contribution or no contribution at all.25,26

Another pharmacogenomic approach is to characterize functional variations in patients’ cytochrome P-450 enzymes. Classification of patients as “poor” or “rapid” metabolizers, for instance, may help predict medication choice or dosage. Unfortunately, these approaches are limited. With few possible exceptions, no evidence exists that blood concentrations influence antidepressant outcomes, and there are multiple nongenetic factors that influence drug metabolism, such as diet, other medications, and adherence. Existing studies that show benefit of pharmacogenetic testing are limited because clinicians are unblinded or randomization procedures are poor, a troubling fact given the high rate of placebo response to antidepressant treatment.

Not surprisingly, we can take advantage of patient preferences to enhance treatment outcomes. When patients in a clinical trial receive a treatment they prefer, response rates are significantly higher than when they are randomized to a non-preferred intervention.27 Even when patients’ preferences do not have any bearing on outcome, matching treatments with patients’ preferences increases their willingness to initiate and adhere to a treatment plan.28

Clinical trials are often criticized because the ongoing, regular con-tact between patients and clinicians (frequent office visits, abundant personalized attention, etc) may inflate placebo response rates. Indeed, regular contact with health care pro-fessionals has a therapeutic effect in itself, as do patient expectations. When patients in a clinical trial know they will get 1 of 2 active drugs, response rates are one-third higher than when they know they may be randomized to placebo.29

Finally, the quality of the therapeutic alliance between prescriber and patient is sometimes a better predictor of patient outcome than which drugs are prescribed. One study found that “effective” prescribers obtained better outcomes with placebos than “less effective” prescribers with active antidepressants.30 Asking “which” medication may be less important than the “meaning” of medication to both clinician and patient. The characteristics of communication between prescriber and patient, whether the patient perceives an internal or external locus of control over the outcome, and a host of other factors may be more important than which drug is prescribed.


The generally accepted view that all antidepressants are essentially equivalent in their effectiveness appears valid. Selection of the right antidepressant, therefore, may rely less on matching a patient to a specific medication, and more on a consideration of adverse-effect profiles or medication availability, or on a redefinition of the phenotype of depression altogether. The recent emphasis on personalized antidepressant prescribing seems warranted, but rather than taking a combination or pharmacogenomic approach to medication selection, clinicians should focus more on a personalized approach, establish realistic (but hopeful) expectations, and use patient preferences and beliefs to optimize outcomes.


Dr Balt is Diplomate of the American Board of Psychiatry and Neurology, Diplomate of the American Board of Addiction Medicine, Editor in Chief of The Carlat Psychiatry Report, and Supervising Psychiatrist at John Muir Behavioral Health in Concord, Calif. He reports no personal conflicts of interest concerning the subject matter of this article; however, his wife is employed by Otsuka America, Inc.


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