Influenza A: Doctors recommend Tamiflu. But is evidence-based medicine evidence-based?
Continuing a series of publications on health can not ignore the topic of flu.
The worldwide use of the antiviral drug, Tamiflu, has risen sharply since the outbreak of the H1N1 virus (“swine” or “Mexican” flu) in 2009. Initially it was thought that this would lead to a reduction in hospitalizations and complications of the flu, such as pneumonia, during a pandemic.
But it turned out that Tamiflu (Oseltamivir) reduces the duration of flu symptoms by only half a day, and there is no evidence that it reduces the number of hospitalizations or complications of the flu. This is evidenced by data from the Cochrane Review of Studies , published by the Cochrane Community (an independent, global health research network) and the British Medical Journal (BMJ) back in April 2014.
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The Cochrane Review is called Neuraminidase inhibitors for the prevention and treatment of influenza in healthy adults and children, based on the Tamiflu (Oseltamivir) tests and the Relenza (Zanamivir) tests. More than 24 thousand people participated in these tests, and his results disputed the “historical” assumptions that neuraminidase inhibitors are effective in fighting the flu. Evidence also suggests that there is insufficient reason to support the use of Tamiflu in preventing the transmission of the virus from person to person.
This raises even more questions about the effectiveness of drug regulatory and public health decision-making. Claims to the effectiveness of Tamiflu raised the question of the effectiveness of decisions made by governments around the world in order to have stocks of such drugs in case of a pandemic. The US spent more than $ 1.3 billion on the purchase of a strategic stock of antiviral drugs, while the UK government spent almost $ 424 million (about 40 million doses) on Tamiflu stocks and another ÂŁ 136m on Relenza.
In 2009, lack of access to complete data was an obstacle to the efforts of Cochrane researchers to test the safety and effectiveness of Tamiflu. The BMJ magazine and the Cochrane community published a joint appeal to governments and health policy makers around the world asking them (in the light of the Cochrane review) could they make the same recommendations today for Tamiflu?
Taking Tamiflu, compared with placebo, leads to relief of flu-like symptoms just half a day faster (reducing the duration of symptoms from 7 to 6.3 days in adults, while the effect in children was even more uncertain). There is also no evidence of a decrease in hospital admissions or serious complications of the flu (pneumonia, bronchitis, sinusitis or otitis in either adults or children). Tamiflu also increases the risk of nausea and vomiting in adults by about 4%, and among children by 5%. There is an increased risk of psychiatric events — by about 1%. There is also much evidence that Tamiflu prevents some people from producing enough of their own antibodies to fight infection.
Dr. David Tovey, Editor-in-Chief, Cochrane Community, said: “We now have the most reliable data available on neuraminidase inhibitors. This shows the importance of ensuring that test data is transparent and accessible. ”
Doctors Tom Jefferson, Karl Henehan (Heneghan) and Peter Doshi, the authors of this Cochrane review, said: “Statements about the efficacy of using drugs cannot be based on biased or insufficient information. We risk too much - the health of our people and unnecessary economic waste. And so our findings require wider public discussion. We urge people not to believe the results of single trials or comments by decision makers in the health sector. ”
Dr. Fiona Godley (Godlee), chief editor, “BMJ” magazine, said: “This review is the result of many years of struggle to discover and use research data that has not been previously published or even hidden from view. Future decisions about the procurement and use of drugs, especially on a massive scale, should be based on a complete picture of the evidence. We need complete data from clinical trials for all drugs currently used. The Cochrane Review highlights the enormous challenge we face. We need the support of organizations and pharmaceutical companies to reassess all the available data, even if it means a return 20 years ago. Otherwise, we risk stepping on another rake in a series of pandemics. Can we afford it? ”
So what is the essence of the claims of “BMJ” and the Cochrane community?
Today, few people believe that medicine can only be called “evidence-based medicine”. To understand whether a new method of treating a disease is indeed better than the former, research doctors evaluate the evidence available. It is necessary to take into account not the fact that, for example, “this medicine helped Aunt Masha”, but the fact that the result of treatment was confirmed in a statistically significant number of such patients, who also have similar parameters (physiological, lifestyle, effects of other significant influencing factors).
In 1972, epidemiologist Archie Cochrane (Archie Cochrane) proposed to compile a central international registry of clinical trials. Back in 1938, a rebellious young medical student Archie Cochran walked through the streets of London with a poster on which was written: "All effective methods of treatment should be free." His book “Effectiveness and efficiency” did not receive sufficient recognition in its time, but now it forms the basis of the modern understanding of medicine based on evidence.
Archie Cochran photo and label of the Cochrane Community.
Proponents of evidence-based medicine want a meaningful “last word” to resolve disputes about what is best. But not all medical research is equal - there is a clear hierarchy of such evidence:
* expert opinion and reports on individual events refer to the "lower tier";
* qualitatively conducted randomized controlled trials are in the middle;
* at the top of this hierarchy are meta-analyzes - studies that contain the results of several studies that addressed the same question;
* and at the very top of this hierarchy are meta-analyzes conducted by a group called “Cochrane Collaboration”.
To be a member of the Cochrane Collaboration, researchers or research groups are required to follow very strict rules on how meta-analyzes should be conducted and presented. This is why Cochrane reviews are generally considered the best of the meta-analyzes.
The team at Boston University decided to find out the real difference between Cochrane and non-Cochrane meta-analyzes.
Imagine you have five small clinical trials, and everyone has found a common positive benefit for, say, taking aspirin to prevent heart attacks. But each of the studies covered only a small number of subjects, no one could state with certainty that the beneficial effects were not the result of randomness. Statisticians say that such studies are considered “insufficient.”
There is a good way to increase the statistical power of these studies - to combine these five small studies into one. This is exactly what meta-analysis does. Combining a few small studies, and accepting the average result of these studies can sometimes tip the scales, and the medical community will know with confidence whether this solution works.
Meta-analyzes are effective and cheap because they do not require the launch of new experimental processes. Rather, it is a matter of finding several similar studies that have already been published, but this can be surprisingly difficult. Researchers must be persistent and methodical in their search. Finding research and determining whether they are good enough to trust them becomes a critical issue.
This is actually the main reason why Cochrane Collaboration was established. Cochrane collaboration meta-analyzes must adhere to very high standards of “transparency”, methodological rigor and reproducibility. Unfortunately, few people take the time and effort to join Cochrane collaboration, which means that the vast majority of meta-analyzes do not necessarily adhere to their standards. But is it really important? How different can meta-analyzes be?
Researchers at Boston University found that almost 40% of Cochrane and non-Cochrane meta-analyzes differ in their final statistical responses. This means that typical readers (doctors and health care managers), for example, may receive fundamentally different information - depending on whose meta-analysis they have read, by chance.
Secondly, these differences proved to be systematic. No Cochrane reviews, on average, suggested that the tests they tested were statistically more powerful, and were more likely to cure diseases or prevent some medical complications, compared with the findings of the Cochrane reviews. At the same time, non-Cochrane reviews were less accurate, which means that there is a high chance that this is the result of randomness.
Scientists from Boston write: “Meta-analysis is nothing more than just an estimate of the weighted average of each of the components of the study. But we were surprised to find that about 63% of the included studies were unique to Cochrane and non-Cochrane meta-analyzes. It would seem that two types of meta-analyzes, using similar search criteria, taking a similar period of time and similar databases, could find the same documents for analysis. But it turned out that among the studies included in the Cochrane and non-Cochrane meta-analyzes, only less than 37% of the works coincided. ”
Obviously, most or all of these differences are due to the fact that the Cochrane community insists on more stringent criteria. Meta-analysis is, first of all, research, it includes and does not discard data, which, for example, led to a bad result.
Interestingly, the analyzes that reported a much greater effect are usually quoted in other papers at a much higher rate than the analyzes that reported on the small size of the effect. The large and fatty effect attracts more attention than results showing minimal or ambiguous results. The medical community, after all, is made up of ordinary people.
The results of researchers at Boston University show that Archie Cochran was absolutely right. Methodological consistency, rigor and transparency are essential. Without this, there is the risk of concluding that something is working when it is not at all.
This also shows us, once again, how difficult it is to form a unified interpretation of medical literature. Meta-analyzes are often used as a final word on a given topic, as an arbiter overcoming ambiguity. But, two meta-analyzes (on the same topic) can come to different conclusions. If we consider meta-analysis as a “gold standard” in our current era of “evidence-based medicine,” how should the average doctor or politician or even a patient react when two “gold standards” contradict each other? - And the Boston doctors make the only possible conclusion: "Let the buyer be vigilant." But, where are the criteria for such vigilance?
The findings of physicians from Boston fully confirm the result of a previously conducted study by Stanford University , published in the journal PLOS Biology in 2013. It suggests that scientific data may be distorted due to significant error in animal research reports, and can create a deceptive impression that their results can be used for people.
Testing a new therapeutic intervention (for example, a drug or surgical procedure) in humans is expensive, risky and ethically difficult, so the vast majority of drugs and techniques are first tested on animals. Unfortunately, the cost and the same ethical issues limit the size of research in animals, limiting their statistical power. But, as a result, the scientific literature contains many studies that are either unsure of their results or contradictory. A way to circumvent these limitations would be to conduct a "meta-analysis."
Doctors from Stanford University studied 160 previously published meta-analyzes of animal studies that evaluated potential agents to treat a number of serious disorders of neurological disorders (multiple sclerosis, stroke, Parkinson's disease, Alzheimer's disease and spinal cord injuries). These meta-analyzes covered 1000 original previously published animal studies. The authors of this “meta-analysis of meta-analyzes” used the most accurate studies in each of the meta-analyzes as an estimate of the true size of the effect of a particular treatment. They then evaluated the statistical significance of the findings of these studies. It was disturbing that the authors found that the statistical significance of the work was more than doubled.
The authors suggest that this is the result of intentional fraud by scientists who conduct original research. The overestimation of the statistical significance of research occurs due to two main factors:
* one is that when conducting research, scientists tend to choose a data analysis method that can give them the “best” result;
* The second occurs because scientists usually want to publish the results in more comprehensive journals. And such journals, as a rule, prefer research with positive rather than negative results. Many studies with negative results were not even submitted for publication or, if submitted, were not published or were published with great delay, thereby reducing their chance of including them in the meta-analysis.
Obviously, it is precisely the distortions of the results reported in the journal PLOS Biology that lead to the absence of the “advancement” of research from animals to human clinical trials. It also seems unlikely that this phenomenon described is limited to studies of neurological disorders. Most likely, this is a common feature of research reporting.
The authors have proposed several means of eliminating the distortions that they have identified. First, research must adhere to rigorous methodological principles for conducting research and analyzing their results. Secondly, studies (both in animals and in humans) must be registered in advance and must publish their results (including negative ones).
The trouble is, these proposals were formulated by Archie Cochran 40 years before the scientists at Stanford, and almost 3 years have passed since the publication of the work of the Stanford students themselves. And, who, as they say, is still there ...
Therefore, reading the next “tips” about what you should be treated for and what you should eat, be vigilant and remember the lines from the verses of Vladimir Vladimirovich Mayakovsky, the poet of the times of socialism: “After all, if the stars light up, it means someone needs it…” Of course, both Archie Cochran and Vladimir Mayakovsky sought and encouraged others to do high things. But, unfortunately, we are increasingly under the influence of people, either unprofessional or "not man-made", and the word "stars" is increasingly being used only in quotes ...
The worldwide use of the antiviral drug, Tamiflu, has risen sharply since the outbreak of the H1N1 virus (“swine” or “Mexican” flu) in 2009. Initially it was thought that this would lead to a reduction in hospitalizations and complications of the flu, such as pneumonia, during a pandemic.
But it turned out that Tamiflu (Oseltamivir) reduces the duration of flu symptoms by only half a day, and there is no evidence that it reduces the number of hospitalizations or complications of the flu. This is evidenced by data from the Cochrane Review of Studies , published by the Cochrane Community (an independent, global health research network) and the British Medical Journal (BMJ) back in April 2014.
')
The Cochrane Review is called Neuraminidase inhibitors for the prevention and treatment of influenza in healthy adults and children, based on the Tamiflu (Oseltamivir) tests and the Relenza (Zanamivir) tests. More than 24 thousand people participated in these tests, and his results disputed the “historical” assumptions that neuraminidase inhibitors are effective in fighting the flu. Evidence also suggests that there is insufficient reason to support the use of Tamiflu in preventing the transmission of the virus from person to person.
This raises even more questions about the effectiveness of drug regulatory and public health decision-making. Claims to the effectiveness of Tamiflu raised the question of the effectiveness of decisions made by governments around the world in order to have stocks of such drugs in case of a pandemic. The US spent more than $ 1.3 billion on the purchase of a strategic stock of antiviral drugs, while the UK government spent almost $ 424 million (about 40 million doses) on Tamiflu stocks and another ÂŁ 136m on Relenza.
In 2009, lack of access to complete data was an obstacle to the efforts of Cochrane researchers to test the safety and effectiveness of Tamiflu. The BMJ magazine and the Cochrane community published a joint appeal to governments and health policy makers around the world asking them (in the light of the Cochrane review) could they make the same recommendations today for Tamiflu?
Taking Tamiflu, compared with placebo, leads to relief of flu-like symptoms just half a day faster (reducing the duration of symptoms from 7 to 6.3 days in adults, while the effect in children was even more uncertain). There is also no evidence of a decrease in hospital admissions or serious complications of the flu (pneumonia, bronchitis, sinusitis or otitis in either adults or children). Tamiflu also increases the risk of nausea and vomiting in adults by about 4%, and among children by 5%. There is an increased risk of psychiatric events — by about 1%. There is also much evidence that Tamiflu prevents some people from producing enough of their own antibodies to fight infection.
Dr. David Tovey, Editor-in-Chief, Cochrane Community, said: “We now have the most reliable data available on neuraminidase inhibitors. This shows the importance of ensuring that test data is transparent and accessible. ”
Doctors Tom Jefferson, Karl Henehan (Heneghan) and Peter Doshi, the authors of this Cochrane review, said: “Statements about the efficacy of using drugs cannot be based on biased or insufficient information. We risk too much - the health of our people and unnecessary economic waste. And so our findings require wider public discussion. We urge people not to believe the results of single trials or comments by decision makers in the health sector. ”
Dr. Fiona Godley (Godlee), chief editor, “BMJ” magazine, said: “This review is the result of many years of struggle to discover and use research data that has not been previously published or even hidden from view. Future decisions about the procurement and use of drugs, especially on a massive scale, should be based on a complete picture of the evidence. We need complete data from clinical trials for all drugs currently used. The Cochrane Review highlights the enormous challenge we face. We need the support of organizations and pharmaceutical companies to reassess all the available data, even if it means a return 20 years ago. Otherwise, we risk stepping on another rake in a series of pandemics. Can we afford it? ”
So what is the essence of the claims of “BMJ” and the Cochrane community?
Today, few people believe that medicine can only be called “evidence-based medicine”. To understand whether a new method of treating a disease is indeed better than the former, research doctors evaluate the evidence available. It is necessary to take into account not the fact that, for example, “this medicine helped Aunt Masha”, but the fact that the result of treatment was confirmed in a statistically significant number of such patients, who also have similar parameters (physiological, lifestyle, effects of other significant influencing factors).
In 1972, epidemiologist Archie Cochrane (Archie Cochrane) proposed to compile a central international registry of clinical trials. Back in 1938, a rebellious young medical student Archie Cochran walked through the streets of London with a poster on which was written: "All effective methods of treatment should be free." His book “Effectiveness and efficiency” did not receive sufficient recognition in its time, but now it forms the basis of the modern understanding of medicine based on evidence.
Archie Cochran photo and label of the Cochrane Community.
Proponents of evidence-based medicine want a meaningful “last word” to resolve disputes about what is best. But not all medical research is equal - there is a clear hierarchy of such evidence:
* expert opinion and reports on individual events refer to the "lower tier";
* qualitatively conducted randomized controlled trials are in the middle;
* at the top of this hierarchy are meta-analyzes - studies that contain the results of several studies that addressed the same question;
* and at the very top of this hierarchy are meta-analyzes conducted by a group called “Cochrane Collaboration”.
To be a member of the Cochrane Collaboration, researchers or research groups are required to follow very strict rules on how meta-analyzes should be conducted and presented. This is why Cochrane reviews are generally considered the best of the meta-analyzes.
The team at Boston University decided to find out the real difference between Cochrane and non-Cochrane meta-analyzes.
Imagine you have five small clinical trials, and everyone has found a common positive benefit for, say, taking aspirin to prevent heart attacks. But each of the studies covered only a small number of subjects, no one could state with certainty that the beneficial effects were not the result of randomness. Statisticians say that such studies are considered “insufficient.”
There is a good way to increase the statistical power of these studies - to combine these five small studies into one. This is exactly what meta-analysis does. Combining a few small studies, and accepting the average result of these studies can sometimes tip the scales, and the medical community will know with confidence whether this solution works.
Meta-analyzes are effective and cheap because they do not require the launch of new experimental processes. Rather, it is a matter of finding several similar studies that have already been published, but this can be surprisingly difficult. Researchers must be persistent and methodical in their search. Finding research and determining whether they are good enough to trust them becomes a critical issue.
This is actually the main reason why Cochrane Collaboration was established. Cochrane collaboration meta-analyzes must adhere to very high standards of “transparency”, methodological rigor and reproducibility. Unfortunately, few people take the time and effort to join Cochrane collaboration, which means that the vast majority of meta-analyzes do not necessarily adhere to their standards. But is it really important? How different can meta-analyzes be?
Researchers at Boston University found that almost 40% of Cochrane and non-Cochrane meta-analyzes differ in their final statistical responses. This means that typical readers (doctors and health care managers), for example, may receive fundamentally different information - depending on whose meta-analysis they have read, by chance.
Secondly, these differences proved to be systematic. No Cochrane reviews, on average, suggested that the tests they tested were statistically more powerful, and were more likely to cure diseases or prevent some medical complications, compared with the findings of the Cochrane reviews. At the same time, non-Cochrane reviews were less accurate, which means that there is a high chance that this is the result of randomness.
Scientists from Boston write: “Meta-analysis is nothing more than just an estimate of the weighted average of each of the components of the study. But we were surprised to find that about 63% of the included studies were unique to Cochrane and non-Cochrane meta-analyzes. It would seem that two types of meta-analyzes, using similar search criteria, taking a similar period of time and similar databases, could find the same documents for analysis. But it turned out that among the studies included in the Cochrane and non-Cochrane meta-analyzes, only less than 37% of the works coincided. ”
Obviously, most or all of these differences are due to the fact that the Cochrane community insists on more stringent criteria. Meta-analysis is, first of all, research, it includes and does not discard data, which, for example, led to a bad result.
Interestingly, the analyzes that reported a much greater effect are usually quoted in other papers at a much higher rate than the analyzes that reported on the small size of the effect. The large and fatty effect attracts more attention than results showing minimal or ambiguous results. The medical community, after all, is made up of ordinary people.
The results of researchers at Boston University show that Archie Cochran was absolutely right. Methodological consistency, rigor and transparency are essential. Without this, there is the risk of concluding that something is working when it is not at all.
This also shows us, once again, how difficult it is to form a unified interpretation of medical literature. Meta-analyzes are often used as a final word on a given topic, as an arbiter overcoming ambiguity. But, two meta-analyzes (on the same topic) can come to different conclusions. If we consider meta-analysis as a “gold standard” in our current era of “evidence-based medicine,” how should the average doctor or politician or even a patient react when two “gold standards” contradict each other? - And the Boston doctors make the only possible conclusion: "Let the buyer be vigilant." But, where are the criteria for such vigilance?
The findings of physicians from Boston fully confirm the result of a previously conducted study by Stanford University , published in the journal PLOS Biology in 2013. It suggests that scientific data may be distorted due to significant error in animal research reports, and can create a deceptive impression that their results can be used for people.
Testing a new therapeutic intervention (for example, a drug or surgical procedure) in humans is expensive, risky and ethically difficult, so the vast majority of drugs and techniques are first tested on animals. Unfortunately, the cost and the same ethical issues limit the size of research in animals, limiting their statistical power. But, as a result, the scientific literature contains many studies that are either unsure of their results or contradictory. A way to circumvent these limitations would be to conduct a "meta-analysis."
Doctors from Stanford University studied 160 previously published meta-analyzes of animal studies that evaluated potential agents to treat a number of serious disorders of neurological disorders (multiple sclerosis, stroke, Parkinson's disease, Alzheimer's disease and spinal cord injuries). These meta-analyzes covered 1000 original previously published animal studies. The authors of this “meta-analysis of meta-analyzes” used the most accurate studies in each of the meta-analyzes as an estimate of the true size of the effect of a particular treatment. They then evaluated the statistical significance of the findings of these studies. It was disturbing that the authors found that the statistical significance of the work was more than doubled.
The authors suggest that this is the result of intentional fraud by scientists who conduct original research. The overestimation of the statistical significance of research occurs due to two main factors:
* one is that when conducting research, scientists tend to choose a data analysis method that can give them the “best” result;
* The second occurs because scientists usually want to publish the results in more comprehensive journals. And such journals, as a rule, prefer research with positive rather than negative results. Many studies with negative results were not even submitted for publication or, if submitted, were not published or were published with great delay, thereby reducing their chance of including them in the meta-analysis.
Obviously, it is precisely the distortions of the results reported in the journal PLOS Biology that lead to the absence of the “advancement” of research from animals to human clinical trials. It also seems unlikely that this phenomenon described is limited to studies of neurological disorders. Most likely, this is a common feature of research reporting.
The authors have proposed several means of eliminating the distortions that they have identified. First, research must adhere to rigorous methodological principles for conducting research and analyzing their results. Secondly, studies (both in animals and in humans) must be registered in advance and must publish their results (including negative ones).
The trouble is, these proposals were formulated by Archie Cochran 40 years before the scientists at Stanford, and almost 3 years have passed since the publication of the work of the Stanford students themselves. And, who, as they say, is still there ...
Therefore, reading the next “tips” about what you should be treated for and what you should eat, be vigilant and remember the lines from the verses of Vladimir Vladimirovich Mayakovsky, the poet of the times of socialism: “After all, if the stars light up, it means someone needs it…” Of course, both Archie Cochran and Vladimir Mayakovsky sought and encouraged others to do high things. But, unfortunately, we are increasingly under the influence of people, either unprofessional or "not man-made", and the word "stars" is increasingly being used only in quotes ...
Source: https://habr.com/ru/post/371653/
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