The Emperor's New Dru g s: An Anal y sis of Antidepressant Medication Data Submitted to the U.S. Food and Drug Administration

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Although antidepressant medication is widely regarded as efficacious, a recent meta-analysis

of published clinical trials indicates that 75percent of the response to antidepressants is

duplicated by placebo (Kirsch & Sapirstein, 1998). These data have been challenged on a

number of grounds, including the restriction of the analyses to patients who had completed

the trials, the limited number of clinical trials assessed, the methodological characteristics of

those trials, and the use of meta-analytic statistical procedures (Klein, 1998).

The present article reports analyses of a data set to which these objections do not apply,

namely, the data submitted to the U.S. Food and Drug Administration(FDA) for approval of

recent antidepressant medications. We analyzed the efficacy data submitted to the FDA for

the six most widely prescribed antidepressants approved between 1987 and 1999 (RxList:

The Internet Drug Index, 1999): fluoxetine (Prozac), paroxetine(Paxil), sertraline (Zoloft),

venlafaxine (Effexor), nefazodone (Serzone), and citalopram (Celexa). These represent all

but one of the selective serotonin reuptakeinhibitors (SSRI) approved during the study

period. The FDA data set includes analyses of data from all patients who attended at least

one evaluation visit, even if they subsequently dropped out of the trial prematurely. Results

are reported from all well controlled efficacy trials of the use of these medications for the

treatment of depression. FDA medical and statistical reviewers had access to the raw data

and evaluated the trials independently. The findings of the primary medical and statistical

reviewers were verified by at least one other reviewer, and the analysis was also assessed by

an independent advisory panel. More important, the FDA data constitute the basis on which

these medications were approved. Approval of these medications implies that these

particular data are strong enough and reliable enough to warrant approval. To the extent that

these data are flawed, the medications should not have been approved. 

 

Iv-B chaos in business creates mutated drugs as an Oy counter innovation to new apparent diseases or contagions as R and B in society. These drugs then mutate until some apparently have a beneficial effect on these diseases, however this Oy drug process is chaotic and and R-B diseases are also some times chaotic. Trying to work out their effectiveness using random based normal curves often leads to errors. For example people can be depressed because of stress or bad things in their lives, it can then be unnatural to remove this depression without improving their lives. If this is the cause of depression then the drug is acting to alter mind states like alcohol to drown a person's sorrows. A drug then that takes a person's mind off problems can deceptively appear to be a treatment. This business can then grow where each is deceiving their other, the B patients deceive the doctors that they are getting better to explore this mind altering feeling rather than attempting to fix the cause of the depression. The doctors and pharmaceutical companies deceive the patients and each other with hype that all end up believing until it crashes into reality when patients don't get better. 

 

When only positive trials are published this allows chaos to grow through random trials by deceptively appearing as a significant treatment.