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Efficacy and Acceptability of Antidepressants in Acute Depression – What Does the Largest Ever Research Study on Antidepressants Tell Us?
Posted on:March 23, 2018
Last Updated: September 21, 2020
Time to read: 10 minutes
psychscenehub.com/psychinsights/efficacy-acceptability-antidepressants-network-meta-analysis/
Antidepressants are widely used treatments for major depressive disorder. However, there is considerable debate on their effectiveness because their short-term benefits are modest and the long-term benefits and harms are under-researched.
In 1998, Irving Kirsch published a meta-analysis of 19 placebo-controlled double-blind clinical trials [1]. In this publication, the mean change in depressive scores was compared between placebo and antidepressant. Here, it was shown that the placebo response was calculated to be responsible for 75% of the response to active antidepressant medication. [1]
This article reached a controversial conclusion, and the methodology was criticised, whereby the analysis was subject to significant clinical, methodological and statistical heterogeneity. [2]
However, in 2008, Kirsch repeated his work using a different set of clinical trials by requesting the FDA to divulge unpublished research from pharmaceutical companies [3]. In this data set, the placebo response was shown to be responsible for 82% of the response observed with antidepressant therapy.
Drug–placebo differences in antidepressant efficacy increase as a function of baseline severity, but are relatively small even for severely depressed patients. The relationship between initial severity and antidepressant efficacy is attributable to decreased responsiveness to placebo among very severely depressed patients, rather than to increased responsiveness to medication (Kirsch et al 2008).
Fournier and colleagues then supported the observation that antidepressants show a magnitude of benefit only for cases of severe depression (HDRS>25). [4]
The magnitude of benefit of antidepressant medication compared with placebo increases with severity of depression symptoms and may be minimal or nonexistent, on average, in patients with mild or moderate symptoms. For patients with very severe depression, the benefit of medications over placebo is substantial.
The efficacy of antidepressants has therefore been debated for at least a decade.
In February 2018, a systematic review and network meta-analysis was published by Cipriani and colleagues, which compared the efficacy and acceptability of antidepressants to treat major depressive disorder. [5]
This network meta-analysis was more extensive and included a comprehensive list of 21 antidepressants and placebo which were compared by using the most advanced statistical methodology for network meta-analysis to date.
NETWORK META ANALYSIS
Systematic reviews and meta-analyses of Randomised controlled trials (RCT’s) lie at the top of the hierarchy of evidence-based medicine and are essential tools in highlighting the effectiveness of interventions. You can view a video about how to appraise a systematic review and meta-analysis.
The purposes of a meta-analysis are:
To resolve conflicting conclusions by examining quality, subjects and interventions
To increase power for major endpoints and subgroup analyses. This is especially important for clinical trials as many randomised controlled trials have small sample sizes that do not allow for definitive conclusions to be reached on a positive or negative effect of the intervention.
To sharpen boundaries of the effect size in the case of positive
studies as a Meta-analysis arrives closer to the true effect size in the population as the sample size increases. (Increase precision)
To answer new questions and develop new hypotheses
Chalmers who was instrumental in the development of meta-analysis in 1988 highlighted the importance of meta-analyses that later led to the change in practice towards anticoagulation in patients with acute MI. [6]
An example of a strongly positive effect in a meta-analysis is the use of intravenous streptokinase for patients with acute myocardial infarction. The saving of lives had been demonstrated in small studies for at least five years before it was confirmed by the very large study known as GISSI.
It is also of interest that intravenous streptokinase is rarely used in the United States, in contrast to lidocaine. I guess the reason is that doctors do not like to see their patients bleed and they do like to see arrhythmias disappear as a result of something they did.
They are unable to notice that in the former case they are saving the lives of one to two additional patients out of every 100 admitted for an acute myocardial infarction, and possibly losing one or two in the latter case. The individual practitioner cannot notice that kind of reduction or increase in death rate. So that is the usefulness of meta-analysis. (Chalmers, 1988)
Network Meta-Analysis (NMA) and Multiple Treatment Comparisons (MTC) of RCT’s
NMA’s and MTC’s have been introduced to facilitate indirect comparisons of multiple interventions that have not been studied in head to head studies.
rest in link
Posted on:March 23, 2018
Last Updated: September 21, 2020
Time to read: 10 minutes
psychscenehub.com/psychinsights/efficacy-acceptability-antidepressants-network-meta-analysis/
Antidepressants are widely used treatments for major depressive disorder. However, there is considerable debate on their effectiveness because their short-term benefits are modest and the long-term benefits and harms are under-researched.
In 1998, Irving Kirsch published a meta-analysis of 19 placebo-controlled double-blind clinical trials [1]. In this publication, the mean change in depressive scores was compared between placebo and antidepressant. Here, it was shown that the placebo response was calculated to be responsible for 75% of the response to active antidepressant medication. [1]
This article reached a controversial conclusion, and the methodology was criticised, whereby the analysis was subject to significant clinical, methodological and statistical heterogeneity. [2]
However, in 2008, Kirsch repeated his work using a different set of clinical trials by requesting the FDA to divulge unpublished research from pharmaceutical companies [3]. In this data set, the placebo response was shown to be responsible for 82% of the response observed with antidepressant therapy.
Drug–placebo differences in antidepressant efficacy increase as a function of baseline severity, but are relatively small even for severely depressed patients. The relationship between initial severity and antidepressant efficacy is attributable to decreased responsiveness to placebo among very severely depressed patients, rather than to increased responsiveness to medication (Kirsch et al 2008).
Fournier and colleagues then supported the observation that antidepressants show a magnitude of benefit only for cases of severe depression (HDRS>25). [4]
The magnitude of benefit of antidepressant medication compared with placebo increases with severity of depression symptoms and may be minimal or nonexistent, on average, in patients with mild or moderate symptoms. For patients with very severe depression, the benefit of medications over placebo is substantial.
The efficacy of antidepressants has therefore been debated for at least a decade.
In February 2018, a systematic review and network meta-analysis was published by Cipriani and colleagues, which compared the efficacy and acceptability of antidepressants to treat major depressive disorder. [5]
This network meta-analysis was more extensive and included a comprehensive list of 21 antidepressants and placebo which were compared by using the most advanced statistical methodology for network meta-analysis to date.
NETWORK META ANALYSIS
Systematic reviews and meta-analyses of Randomised controlled trials (RCT’s) lie at the top of the hierarchy of evidence-based medicine and are essential tools in highlighting the effectiveness of interventions. You can view a video about how to appraise a systematic review and meta-analysis.
The purposes of a meta-analysis are:
To resolve conflicting conclusions by examining quality, subjects and interventions
To increase power for major endpoints and subgroup analyses. This is especially important for clinical trials as many randomised controlled trials have small sample sizes that do not allow for definitive conclusions to be reached on a positive or negative effect of the intervention.
To sharpen boundaries of the effect size in the case of positive
studies as a Meta-analysis arrives closer to the true effect size in the population as the sample size increases. (Increase precision)
To answer new questions and develop new hypotheses
Chalmers who was instrumental in the development of meta-analysis in 1988 highlighted the importance of meta-analyses that later led to the change in practice towards anticoagulation in patients with acute MI. [6]
An example of a strongly positive effect in a meta-analysis is the use of intravenous streptokinase for patients with acute myocardial infarction. The saving of lives had been demonstrated in small studies for at least five years before it was confirmed by the very large study known as GISSI.
It is also of interest that intravenous streptokinase is rarely used in the United States, in contrast to lidocaine. I guess the reason is that doctors do not like to see their patients bleed and they do like to see arrhythmias disappear as a result of something they did.
They are unable to notice that in the former case they are saving the lives of one to two additional patients out of every 100 admitted for an acute myocardial infarction, and possibly losing one or two in the latter case. The individual practitioner cannot notice that kind of reduction or increase in death rate. So that is the usefulness of meta-analysis. (Chalmers, 1988)
Network Meta-Analysis (NMA) and Multiple Treatment Comparisons (MTC) of RCT’s
NMA’s and MTC’s have been introduced to facilitate indirect comparisons of multiple interventions that have not been studied in head to head studies.
rest in link