Patients with a common type of metal hip implant should have annual health checks for as long as they have the implant, according to the UK body for regulating medical devices. The all-metal devices have been found to wear down at an accelerated rate in some patients, potentially causing damage and deterioration in the bone and tissue around the hip. There are also concerns that they could leak traces of metal into the bloodstream, which the annual medical checks will monitor.
Hours before critical coverage from the British Medical Journal and the BBC, the Medicines and Healthcare products Regulatory Agency (MHRA issued new guidelines on larger forms of ‘metal-on-metal’ (MoM hip implants. Advice on smaller metal devices or those featuring a plastic or ceramic head has not changed. Previously, guidelines suggested larger MoM implants should only be checked annually for five years after surgery. The agency now says the annual check-ups should be continued for the life of the implant. Check-ups, they say, are a precautionary measure to reduce the “small risk” of complications and the need for further surgery.
Together with the recent controversy over PIP breast implants, the news has caused the media and patient groups to call for tighter regulation of medical devices, perhaps bringing the approval process into line with that of medicines. Before they can be approved for wider use drugs must undergo several years of laboratory, animal and human testing .
What types of implants are involved?
There are numerous designs and materials used to make hip implants. In recent days the MHRA has issued major updates to its advice on a type of metal-on-metal (MoM hip replacement. As the name implies, MoM implants feature a joint made of two metal surfaces – a metal ‘ball’ that replaces the ball found at the top of the thigh bone (femur and a metal ‘cup’ that acts like the socket found in the pelvis.
The MHRA’s updated advice concerns the type of MoM implant in which the head of the femur is 36mm or greater. This is often referred to as a ‘large head’ implant. The agency now says that patients fitted with this type of implant should be monitored annually for the life of the implant, and that they should also have tests to measure levels of metal particles (ions in their blood. Patients with these implants who have symptoms should also have MRI or ultrasound scans, and patients without symptoms should have a scan if their blood levels of metal ions are rising. The previous guidance on this type of hip implant, issued in April 2010, advised that patients should be monitored annually for no fewer than five years.
What about other types of hip implants?
Advice on monitoring patients with other types of hip implants remains the same, and guidance has not changed on:
- MoM hip resurfacing implants – where the socket and ball of the hip bone has a metal surface applied to it rather than being totally replaced.
- Total MoM implants where the replacement ball is less than 36mm wide.
- A particular range of hip replacements called DePuy ASR – these hip replacements were recalled by their manufacturer, DePuy, in 2010 because of high failure rates. The company made three types of ASR implant.
- Implants featuring plastic or ceramic heads.
How many people are affected?
It is estimated that, in total, 49,000 people in the UK have been given metal-on-metal implants with a width of 36mm or above. This represents a minority of the patients given hip replacements, who mostly have devices featuring plastic, ceramics or smaller metal heads.
In 2010 there were 68,907 new hip replacements fitted, and approximately 1,300 of these surgeries used an MoM implant sized 36mm or above – a rate of around 2%.
What exactly is the problem with MoM implants?
All hip implants will wear down over time as the ball and cup slide against each other during walking and running. Although many people live the rest of their lives without needing their implant to be replaced, any implant may eventually need surgery to remove or replace its components. Surgery to remove or replace part of the implant is known as ‘revision’ and, of the 76,759 procedures performed in 2010, some 7,852 were revision surgeries.
However, data now suggest that large head MoM hip implants (those with a width of 36mm or greater wear down at a faster rate than other types of implants. As friction acts upon their surfaces it can cause tiny metal particles (medically referred to as ‘debris’ to break off and enter the space around the implant. Individuals are thought to react differently to the presence of these metal particles, but, in some people, they can trigger inflammation and discomfort in the area around the implant. Over time this can cause damage and deterioration in the bone and tissue surrounding the implant and joint. This, in turn, may cause the implant to become loose and cause painful symptoms, meaning that further surgery is required.
News coverage has also focused on the MHRA’s recommendation to check for the presence of metal ions in the bloodstream, potentially released either from debris or the implant itself. Ions are electrically charged molecules. Levels of ions in the bloodstream, particularly of the cobalt and chromium used in the surface of the implants, may, therefore, indicate how much wear there is to the artificial hip.
There has been no definitive link between ions from MoM implants and illness, although there has been a small number of cases in which high levels of metal ions in the bloodstream have been associated with symptoms or illnesses elsewhere in the body, including effects on the heart, nervous system and thyroid gland.
The MHRA points out that most patients with MoM implants have well functioning hips and are thought to be at low risk of developing serious problems. However, a small number of patients with these hip implants develop soft tissue reactions to the debris associated with some MoM implants.
How are medical devices regulated?
In the UK, the MHRA is the government agency responsible for ensuring that medical devices work and are safe. The MHRA audits the performance of private sector organisations (called notified bodies that assess and approve medical devices. Once a product is on the market and in use, the MHRA has a system for receiving reports of problems with these products, and will issue warnings if these problems are confirmed through their investigations. It also inspects companies that manufacture products to ensure they comply with regulations.
This system differs greatly from that for testing and approving drugs. Drugs require several years of research testing and trials before they can be approved for clinical use.
What action have regulators taken?
The MHRA has convened an expert advisory group to look at the problems associated with MoM implants. This meets regularly to assess new scientific evidence and reports from doctors and medical staff treating patients. The agency says it is continuing to monitor closely all the latest evidence about these devices and may issue further advice in the future.
In the US, the Food and Drug Administration (FDA says it is gathering additional information about adverse events in patients with MoM implants. In the meantime, it advises patients with MoM hip implants who have no symptoms to attend follow-up appointments as normal with their surgeon. Patients who develop symptoms should see their surgeon promptly for further evaluation.
What actions have critics called for?
In light of the PIP breast implant controversy and this new information on hip implants, there is currently intense scrutiny on the way medical devices are regulated in the UK and Europe, with patient groups and the media arguing that medical devices should be regulated in a similar way to medicines.
Clearing a medicine for use in the UK is a lengthy process involving several stages of laboratory and animal testing, and then carefully controlled and monitored tests in humans. Only once there is enough evidence to suggest that a medicine is reasonably safe can it enter clinical use, and even then patients will be monitored to look at the longer-term effects of the drug.
However, medical devices are not required to go through human trials before entering use, and can currently be approved on the basis of mechanical tests and animal research. While certain devices, such as hip implants, have been monitored through systems such as the National Joint Registry, in light of the recent health concerns over PIP breast implants, patient groups are calling for more testing before devices are allowed into clinical use, and closer mandatory monitoring schemes to ensure their safety once they enter the market.
Links To The Headlines
Annual blood tests for hip patients over poison fears. The Daily Telegraph, February 29 2012
Hip replacement toxic risk could affect 50,000. The Independent, February 29 2012
MHRA: Metal hip implant patients need life-long checks. BBC News, February 29 2012
Metal scare over hip replacement joints. The Guardian, February 29 2012
Toxic metal hip implants 'could affect thousands more people than PIP breast scandal. Daily Mail, February 29 2012
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February 29, 2012 If I get sick or injured, I want to know that the antibiotics that I need to take are going to work. Unfortunately, with all the antibiotics that industrial agriculture feeds to livestock, today's antibiotics are at risk of becoming ineffective. Take action today to keep antibiotics working in the future! According to the U.S. Food & Drug Administration (FDA , 80 percent of antibiotics in the U.S. are sold for use in livestock production. Often, antibiotics are fed to entire flocks or herds of animals to prevent illnesses they may never acquire or have little risk of contracting. The overuse of antibiotics encourages the development of antibiotic-resistant bacteria. This is a global threat to human health and must be stopped.Bacteria, like everything else in nature, mutate naturally and do so in such a way to continue their own existence. Not all bacteria are destroyed by antibiotics, and the surviving bacteria then multiply, creating a new strain that the antibiotics cannot kill. We're seeing more and more types of antibiotic-resistant bacteria , but we can help stop this. The FDA is seeking comments on the use of one specific type of antibiotic in livestock. The deadline for comments is Tuesday. Will you submit a comment today? Send an email to the FDA today to stop the overuse of antibiotics: http://action.foodandwaterwatch.org/p/dia/action/public/?action_KEY=9495 Thanks for taking action,  Meredith Begin Education & Outreach Organizer Food & Water Watch mbegin(at fwwatch(dot org | |||||||||||||
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Food & Water Watch, 1616 P Street, NW Suite 300 Washington, DC 20036 • (202 683-2500 |
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On February 28, federal health officials added new safety alerts to the prescribing information for statin drugs, citing increased risks of memory loss, diabetes and muscle pain. It is the first time the United States Food and Drug Administration (FDA has officially... |
At the risk of being labeled obsessed myself, I’m still on the Gibbons et al article [ Suicidal Thoughts and Behavior With Antidepressant Treatment] published on-line this month in the Archives of General Psychiatry about treatment emergent suicidality with the SSRIs. They said of their data sources for this meta-analysis, " we obtained complete longitudinal data for RCTs of fluoxetine hydrochloride conducted by Eli Lilly and Co, the Treatment for Adolescents With Depression Study of fluoxetine in children by the National Institute of Mental Health, and adult studies for venlafaxine hydrochloride conducted by Wyeth. " In the abstract, they say, " Data Sources: All intent-to-treat person-level longitudinal data of major depressive disorder from 12 adult, 4 geriatric, and 4 youth randomized controlled trials of fluoxetine hydrochloride and 21 adult trials of venlafaxine hydrochloride. " I wasn’t interested in the adult data, but went looking for the mentioned studies for children and adolescents other than TADS. There are four studies listed and reviewed in the FDA Medical Review for Prozac’s approval for MDD and OCD for children and adolescents in January 2003 and also in the FDA Hammonds Review in August 2004 prior to the black box warning [published full text on-line]:
STUDY DX SPONSOR YEAR N PBO FLX DURATION HCCJ MDD Lilly 1984 40 19 21 6 weeks X065 MDD NIMH? 1991 96 48 48 8 weeks HCJE MDD Lilly 1998 219 110 109 13 weeks HCJW OCD Lilly 1999 103 71 32 9 weeks subtotal 458 248 210 TADS MDD NIMH 2000 433 206 227 36 weeks total 891 454 437
… subtotal 458 248 210 TADS MDD NIMH 2000 439 216 223 12 weeks total 897 464 433
Feels like an orchestrated campaign to me. A biostatistics driven article with no data? full text on-line? data coming soon? a Medscape piece titled No Link Between Antidepressant and Suicide in Kids? with commentary and glossy photos?…
Submitted by Brandon Smith from
Alt-Market
Americans Will Need “Black Markets” To Survive
As Americans, we live in two worlds; the world of mainstream fantasy, and the world of day-to-day reality right outside our front doors. One disappears the moment we shut off our television. The other, does not…
When dealing with the economy, it is the foundation blocks that remain when the proverbial house of cards flutters away in the wind, and these basic roots are what we should be most concerned about. While much of what we see in terms of economic news is awash in a sticky gray cloud of disinformation and uneducated opinion, there are still certain constants that we can always rely on to give us a sense of our general financial environment. Two of these constants are supply and demand. Central banks like the private Federal Reserve may have the ability to flood markets with fiat liquidity to skew indexes and stocks, and our government certainly has the ability to interpret employment numbers in such a way as to paint the rosiest picture possible, but ultimately, these entities cannot artificially manipulate the public into a state of demand when they are, for all intents and purposes, dead broke.
In contrast, the establishment does have the ability to make specific demands or necessities illegal to possess, and can even attempt to restrict their supply. Though, in most cases this leads not to the control they seek, but a sudden and sharp loss of regulation through the growth of covert trade. The people need what the people need, and no government, no matter how titanic, can stop them from getting these commodities when demand is strong enough.
This process of removing necessary or desirable items from a trade environment leads inevitably to counter-prohibition often in the form of strict cash transactions, barter markets, or “black markets” as they are normally derided by those in power. The problem for economic totalitarians is that the harder they squeeze the masses, the more intricate the rebellion becomes, especially when all they want is to participate in free markets the way our forefathers intended.
The so called “drug war” is proof positive of the impossibility of locking down a product, especially one that has no moral bearing on the people who are involved in its use. Only when a considerable majority of a populace can be convinced of the inherent immoral nature of an illicit item can its trade finally be squelched. During any attempt to outlaw a form of commerce, a steady stream of informants convinced of their service to the “greater good” is required for success. Dishonorable governments, therefore, do not usually engage in direct confrontation with black markets. Instead, they seek to encourage the public to view trade outside mainstream legal standards as “taboo”. They must condition us to react with guilt or misplaced righteousness in the face of black market activity, and associate its conduct as dangerous and destructive to the community, turning citizens into an appendage of the bureaucratic eye.
But, what happens when black markets, due to calamity, become a pillar of survival for a society? What happens when the mainstream economy no longer meets the available demand? What happens when this condition has been deliberately engineered by the power structure to hasten cultural desperation and dependence?
In this event, black markets not only sustain a nation through times of weakness, but they also become a form of revolution; a method for fighting back against the centralization of oppressive oligarchies and diminishing their ability to bottleneck important resources. Black markets are a means of fighting back, and are as important as any weapon in the battle for liberty. Here are just a few reasons why such organizational actions may be required in the near future…
The Mainstream Economy Is Slowly Killing Us
There are, unfortunately, some Americans out there who have not caught on yet to the grave circumstances in which we live. Obviously, the stock market seems to have nearly recovered from its epic collapse in 2008 and 2009, and employment, according to the Labor Department, is on the mend. The numbers say it all, right? Wrong! The numbers say very little, especially when they are a product of “creative mathematics”.
Despite the extreme spike in the Dow Jones since 2010, and all the talk of recovery, what the mainstream rarely mentions are the details surrounding this miraculous return from the dead for stocks.
One of the most important factors to consider when gauging the health of the markets is “volume”; the amount of shares being traded and the amount of investors active on any given business day. Since the very beginning of the Dow’s meteoric rise, the markets have been stricken with undeniably low volume interspersed with all too brief moments of activity. In fact, this past January recorded the lowest NYSE volume since 1999:
http://www.bloomberg.com/news/2012-01-23/stock-trading-is-lowest-in-u-s-since-2008.html
Market volume has tumbled over 20% since last year, and is down over 50% from 2008 when the debt implosion began:
http://blogs.wsj.com/marketbeat/2012/02/24/trading-volume-anemic-this-year/
So then, if trade is sinking, why has the Dow jumped to nearly 13,000? Low volume is the key. In a low volume market, less individual investors are present to counteract the buying and selling of larger players, like international banks. When this happens, the big boys are able to trigger market spikes, or market drops, literally at will. Add to this the high probability that much of the stimulus that the Federal Reserve has regurgitated into the ether probably ended up in the coffers of corporate banks which then used the funny money to snap up equities, and presto! Instant market rally! But, a rally that is illusory and unstable.
Improving employment numbers are yet another financial hologram. As most of us in the Liberty Movement are well aware, the Labor Department does not calculate true unemployment in the U.S. Instead, it merely calculates those people who currently receive unemployment benefits. Once a person hits the extension limit (99 weeks in many states on his benefits, he is removed from the rolls, and is no longer counted in the “official” unemployment percentage. While Barack Obama and MSM pundits are quick to point out the drop in jobless to 8.3%, what they conveniently fail to mention is that MILLIONS of Americans have been unemployed for so long that they have been removed from the statistics entirely, and this condition is what has caused the primary fall in jobless percentages, not burgeoning business growth.
Roughly 11 million Americans who are jobless have nonetheless been excluded from the statistical government tally because of a loss of benefits:
According to the Congressional Budget Office, over 40% of the currently unemployed have been so for over 6 months. It also points out that America is suffering the worst case of long term unemployment since the Great Depression:
http://www.cbo.gov/sites/default/files/cbofiles/attachments/02-16-Unemployment.pdf
More than 10.5 million people in the U.S. also receive disability payments, which automatically removes them from the unemployment count, making it seem as though jobs are being created, rather than lost:
http://www.foxnews.com/politics/2012/02/19/report-millions-jobless-file-for-disability-when-unemployment-benefits-run-out/
Around 8.2 million Americans only work part time, meaning they work less hours than are generally considered to be necessary for self-support. These people are still counted as “employed” even if they work a few hours a week.
True unemployment, according to John Williams of Shadowstats, is hovering near 23%:
http://www.shadowstats.com/alternate_data/unemployment-charts
Combine these circumstances with the ever weakening dollar, price inflation in foods and other commodities, and rocketing energy costs, and you have an economy that is strangling the life out of the middle-class and the poor in this country. It is only a matter of time before the populace begins searching for alternative means of subsistence, even if that entails “illegal” activities.
Government Cracking Down On Freedom Of Trade
I was recently walking through the parking lot of a grocery store and ran into a group of women huddled intently around the back of a mini-van. One of the women was reaching into a cooler and handing out glass containers filled with milk. I approached to ask if she was selling raw milk, and if so, how much was she charging. Of course, they turned startled and wide eyed as if I had just stumbled upon their secret opium ring. Somehow it had slipped my mind how ferocious the FDA has become when tracking down raw milk producers. The fact that these women were absolutely terrified of being caught with something as innocuous as MILK was disturbing to me. How could we as a society allow this insanity on the part of our government to continue?
That moment reminded me of the utter irrelevance of petty law, as well as the determination of human beings to defy such law.
The Orwellian hammer has been thrust in the face of those who trade in raw milk, organic produce, and herbal supplements, while small businesses are annihilated by government dues and red tape. In the meantime, law enforcement officials have been sent strapped to shut down children’s lemonade stands (no, seriously :
http://www.cbsnews.com/2100-500164_162-20079838.html
Government legislation which would give the FDA jurisdiction over personal gardens has been fielded. Retail gold and silver purchases of over $600 are now tracked and taxed. The IRS even believes it has the right to tax barter exchanges, even though they do not explain how bartered goods could be legally qualified as “income”, or how they can conceive of ever being able to trace such private trade:
http://www.irs.gov/newsroom/article/0,,id=205581,00.html
Want to choose what kind of currency you would like to use to protect your buying power? Not if the Department Of Justice’s Anne Tompkins has anything to say about it. After the railroading of Liberty Dollar founder Bernard von NotHaus, she stated:
“Attempts to undermine the legitimate currency of this country are simply a unique form of domestic terrorism…”
“While these forms of anti-government activities do not involve violence, they are every bit as insidious and represent a clear and present danger to the economic stability of this country,” she added. “We are determined to meet these threats through infiltration, disruption, and dismantling of organizations which seek to challenge the legitimacy of our democratic form of government.”
http://www.fbi.gov/charlotte/press-releases/2011/defendant-convicted-of-minting-his-own-currency
As our economic situation grows increasingly precarious in this country, more and more people will turn towards localized non-corporate, non-mainstream business methods and products. And, the government will no doubt attempt to greatly restrict or tax these alternatives. This mentality is driven in part by their insatiable appetite for money, but mostly, it’s about domination. They do what they do because they fear decentralized markets, and the ability of the citizenry to conceive of choices outside the system. Slaves are not supposed to choose the economy they will participate in…
A “black market” is only a trade dynamic that the government disapproves of, and the government disapproves of most things these days. Frankly, its time to stop worrying about what Washington D.C. consents to. They have unfailingly demonstrated through rhetoric and action that they are not interested in the fiscal or social health of this nation, and so, we must take matters into our own hands.
Black Market Advantages
If the events in EU nations such as Greece, Spain, and Italy are any indication, the U.S., with its massive debt to GDP ratio (real debt includes entitlement programs , is looking at one of two possible scenarios: default, austerity measures, and high taxes, or, hyperinflation, and then default, austerity measures, and high taxes. In the past we have mentioned barter networking and alternative market programs springing up in countries like Greece and Spain allowing the people to cope with the faltering economy. Much of this trade is done away from the watchful eyes of government, simply because they cannot afford the gnashing buffalo-sized bites that bureaucrats would take from their savings in the process. When a government goes rogue, and causes the people harm, the people are in no way obligated to continue supporting that government.
Black markets give the citizenry a means to protest the taxation of a government that no longer represents them. In a country stricken with austerity, these networks allow the public to thrive without having to pay for the mistakes or misdeeds of political officials and corporate swindlers. In a hyperinflationary environment, black markets (or barter markets that have been deemed unlawful , can be used to supplant the imploding fiat currency altogether, and energize community markets that would otherwise be unable to function. Ultimately, black markets feed and clothe the grassroots movement towards economic responsibility, and every man and woman with any sense of independence should rally around this resource with the intention to fight should it ever be threatened.
“Legality” is arbitrary in the face of inherent conscience, or what some call “natural law”. Without arbitrary legality, and unjust and unwarranted regulation, many federal alphabet agencies would not exist, including the FDA, the IRS, the EPA, the BLM, etc. These institutions do not matter. What they say has no meaning. What matters is what is honorable, what is factual, and what is right. Our loyalty, as Americans, is to our principles and our heritage. Beyond that, we don’t owe anyone anything. A black market in one place and time is a legitimate market in another. For now, private localized trade is able to flow with only minor interference, but there will come a day when even the most practical and harmless personal transactions will be visited with administrative reproach and vitriol. Alternative market champions will be accused of “extremism”, and undermining the mainstream economy. We will be vilified as separatists, isolationists, terrorists, and traitors. I believe it will be far more surreal than what we can possibly imagine now.
They are welcome to call us whatever they like. Honestly……who cares? Let the paper pushers do their angry little dance. The goal is freedom; in life, in politics, and in trade. If we do not change how this country does business ourselves, the results will be far more frightening than any government agent at our doorstep, and the costs will be absolute…
The study was funded by the US National Institute of Mental Health and US Agency for Healthcare Research and Quality. Gibbons and his colleagues from the University of Miami and Columbia University also used data from a National Institute of Health collaborative study of fluoxetine and venlafaxine.
Relative to placebo, fluoxetine seemed to have no effect either way on suicidal thoughts and behaviour in children but it did lead to a lowering in the severity of depressive symptoms. In adult and geriatric patients taking antidepressants, there was a significant reduction in depression and suicide risk over time compared with patients taking placebo.
Gibbons said the differences between his study and that of the FDA might be partly explained by the fact that if a patient overdosed with one of the drugs under study and ended up in the emergency room, this event would be recorded. But if a patient tried to overdose on a placebo, it would be up to that patient to reveal the event.
Open Access
Essay info
Why Most Published Research Findings Are False
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John P. A. Ioannidis
Abstract Top
Summary
There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.
Citation: Ioannidis JPA (2005 Why Most Published Research Findings Are False. PLoS Med 2(8 : e124. doi:10.1371/journal.pmed.0020124
Published: August 30, 2005
Copyright: © 2005 John P. A. Ioannidis. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Competing interests: The author has declared that no competing interests exist.
Abbreviation: PPV, positive predictive value
John P. A. Ioannidis is in the Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece, and Institute for Clinical Research and Health Policy Studies, Department of Medicine, Tufts-New England Medical Center, Tufts University School of Medicine, Boston, Massachusetts, United States of America. E-mail: jioannid@cc.uoi.gr
Published research findings are sometimes refuted by subsequent evidence, with ensuing confusion and disappointment. Refutation and controversy is seen across the range of research designs, from clinical trials and traditional epidemiological studies [ 1–3] to the most modern molecular research [ 4, 5]. There is increasing concern that in modern research, false findings may be the majority or even the vast majority of published research claims [ 6–8]. However, this should not be surprising. It can be proven that most claimed research findings are false. Here I will examine the key factors that influence this problem and some corollaries thereof.
Modeling the Framework for False Positive Findings Top
Several methodologists have pointed out [ 9–11] that the high rate of nonreplication (lack of confirmation of research discoveries is a consequence of the convenient, yet ill-founded strategy of claiming conclusive research findings solely on the basis of a single study assessed by formal statistical significance, typically for a p -value less than 0.05. Research is not most appropriately represented and summarized by p -values, but, unfortunately, there is a widespread notion that medical research articles should be interpreted based only on p -values. Research findings are defined here as any relationship reaching formal statistical significance, e.g., effective interventions, informative predictors, risk factors, or associations. “Negative” research is also very useful. “Negative” is actually a misnomer, and the misinterpretation is widespread. However, here we will target relationships that investigators claim exist, rather than null findings.
It can be proven that most claimed research findings are false
As has been shown previously, the probability that a research finding is indeed true depends on the prior probability of it being true (before doing the study , the statistical power of the study, and the level of statistical significance [ 10, 11]. Consider a 2 ? 2 table in which research findings are compared against the gold standard of true relationships in a scientific field. In a research field both true and false hypotheses can be made about the presence of relationships. 1 Let R be the ratio of the number of “true relationships” to “no relationships” among those tested in the field. R is characteristic of the field and can vary a lot depending on whether the field targets highly likely relationships or searches for only one or a few true relationships among thousands and millions of hypotheses that may be postulated. Let us also consider, for computational simplicity, circumscribed fields where either there is only one true relationship (among many that can be hypothesized or the power is similar to find any of the several existing true relationships. The pre-study probability of a relationship being true is R /( R + 1 . The probability of a study finding a true relationship reflects the power 1 - ? (one minus the Type II error rate . The probability of claiming a relationship when none truly exists reflects the Type I error rate, ?. Assuming that c relationships are being probed in the field, the expected values of the 2 ? 2 table are given in Table 1. After a research finding has been claimed based on achieving formal statistical significance, the post-study probability that it is true is the positive predictive value, PPV. The PPV is also the complementary probability of what Wacholder et al. have called the false positive report probability [ 10]. According to the 2 ? 2 table, one gets PPV = (1 - ? R /( R - ?R + ? . A research finding is thus more likely true than false if (1 - ? R > ?. Since usually the vast majority of investigators depend on a = 0.05, this means that a research finding is more likely true than false if (1 - ? R > 0.05.
Table 1. Research Findings and True Relationships
doi:10.1371/journal.pmed.0020124.t001
What is less well appreciated is that bias and the extent of repeated independent testing by different teams of investigators around the globe may further distort this picture and may lead to even smaller probabilities of the research findings being indeed true. We will try to model these two factors in the context of similar 2 ? 2 tables.
Bias Top
First, let us define bias as the combination of various design, data, analysis, and presentation factors that tend to produce research findings when they should not be produced. Let u be the proportion of probed analyses that would not have been “research findings,” but nevertheless end up presented and reported as such, because of bias. Bias should not be confused with chance variability that causes some findings to be false by chance even though the study design, data, analysis, and presentation are perfect. Bias can entail manipulation in the analysis or reporting of findings. Selective or distorted reporting is a typical form of such bias. We may assume that u does not depend on whether a true relationship exists or not. This is not an unreasonable assumption, since typically it is impossible to know which relationships are indeed true. In the presence of bias ( Table 2 , one gets PPV = ([1 - ?] R + u ? R /( R + ? ? ? R + u ? u ? + u ? R , and PPV decreases with increasing u , unless 1 ? ? ? ?, i.e., 1 ? ? ? 0.05 for most situations. Thus, with increasing bias, the chances that a research finding is true diminish considerably. This is shown for different levels of power and for different pre-study odds in Figure 1. Conversely, true research findings may occasionally be annulled because of reverse bias. For example, with large measurement errors relationships are lost in noise [ 12], or investigators use data inefficiently or fail to notice statistically significant relationships, or there may be conflicts of interest that tend to “bury” significant findings [ 13]. There is no good large-scale empirical evidence on how frequently such reverse bias may occur across diverse research fields. However, it is probably fair to say that reverse bias is not as common. Moreover measurement errors and inefficient use of data are probably becoming less frequent problems, since measurement error has decreased with technological advances in the molecular era and investigators are becoming increasingly sophisticated about their data. Regardless, reverse bias may be modeled in the same way as bias above. Also reverse bias should not be confused with chance variability that may lead to missing a true relationship because of chance.
Figure 1. PPV (Probability That a Research Finding Is True as a Function of the Pre-Study Odds for Various Levels of Bias, u
Panels correspond to power of 0.20, 0.50, and 0.80.
doi:10.1371/journal.pmed.0020124.g001
Table 2. Research Findings and True Relationships in the Presence of Bias
doi:10.1371/journal.pmed.0020124.t002
Testing by Several Independent Teams Top
Several independent teams may be addressing the same sets of research questions. As research efforts are globalized, it is practically the rule that several research teams, often dozens of them, may probe the same or similar questions. Unfortunately, in some areas, the prevailing mentality until now has been to focus on isolated discoveries by single teams and interpret research experiments in isolation. An increasing number of questions have at least one study claiming a research finding, and this receives unilateral attention. The probability that at least one study, among several done on the same question, claims a statistically significant research finding is easy to estimate. For n independent studies of equal power, the 2 ? 2 table is shown in Table 3: PPV = R (1 ? ? n /( R + 1 ? [1 ? ?] n ? R ? n (not considering bias . With increasing number of independent studies, PPV tends to decrease, unless 1 - ?
< a, i.e., typically 1 ? ? < 0.05. This is shown for different levels of power and for different pre-study odds in Figure 2. For n studies of different power, the term ? n is replaced by the product of the terms ? i for i = 1 to n , but inferences are similar.
Figure 2. PPV (Probability That a Research Finding Is True as a Function of the Pre-Study Odds for Various Numbers of Conducted Studies, n
Panels correspond to power of 0.20, 0.50, and 0.80.
doi:10.1371/journal.pmed.0020124.g002
Table 3. Research Findings and True Relationships in the Presence of Multiple Studies
doi:10.1371/journal.pmed.0020124.t003
Corollaries Top
A practical example is shown in Box 1. Based on the above considerations, one may deduce several interesting corollaries about the probability that a research finding is indeed true.
Box 1. An Example: Science at Low Pre-Study Odds
Let us assume that a team of investigators performs a whole genome association study to test whether any of 100,000 gene polymorphisms are associated with susceptibility to schizophrenia. Based on what we know about the extent of heritability of the disease, it is reasonable to expect that probably around ten gene polymorphisms among those tested would be truly associated with schizophrenia, with relatively similar odds ratios around 1.3 for the ten or so polymorphisms and with a fairly similar power to identify any of them. Then R = 10/100,000 = 10?4, and the pre-study probability for any polymorphism to be associated with schizophrenia is also R /( R + 1 = 10?4. 1 Let us also suppose that the study has 60% power to find an association with an odds ratio of 1.3 at ? = 0.05. Then it can be estimated that if a statistically significant association is found with the p -value barely crossing the 0.05 threshold, the post-study probability that this is true increases about 12-fold compared with the pre-study probability, but it is still only 12 ? 10 ?4 .
Now let us suppose that the investigators manipulate their design, analyses, and reporting so as to make more relationships cross the p = 0.05 threshold even though this would not have been crossed with a perfectly adhered to design and analysis and with perfect comprehensive reporting of the results, strictly according to the original study plan. Such manipulation could be done, for example, with serendipitous inclusion or exclusion of certain patients or controls, post hoc subgroup analyses, investigation of genetic contrasts that were not originally specified, changes in the disease or control definitions, and various combinations of selective or distorted reporting of the results. Commercially available “data mining” packages actually are proud of their ability to yield statistically significant results through data dredging. In the presence of bias with u = 0.10, the post-study probability that a research finding is true is only 4.4 ? 10?4. Furthermore, even in the absence of any bias, when ten independent research teams perform similar experiments around the world, if one of them finds a formally statistically significant association, the probability that the research finding is true is only 1.5 ? 10?4, hardly any higher than the probability we had before any of this extensive research was undertaken!
Corollary 1: The smaller the studies conducted in a scientific field, the less likely the research findings are to be true. Small sample size means smaller power and, for all functions above, the PPV for a true research finding decreases as power decreases towards 1 ? ? = 0.05. Thus, other factors being equal, research findings are more likely true in scientific fields that undertake large studies, such as randomized controlled trials in cardiology (several thousand subjects randomized [ 14] than in scientific fields with small studies, such as most research of molecular predictors (sample sizes 100-fold smaller [ 15].
Corollary 2: The smaller the effect sizes in a scientific field, the less likely the research findings are to be true. Power is also related to the effect size. Thus research findings are more likely true in scientific fields with large effects, such as the impact of smoking on cancer or cardiovascular disease (relative risks 3–20 , than in scientific fields where postulated effects are small, such as genetic risk factors for multigenetic diseases (relative risks 1.1–1.5 [ 7]. Modern epidemiology is increasingly obliged to target smaller effect sizes [ 16]. Consequently, the proportion of true research findings is expected to decrease. In the same line of thinking, if the true effect sizes are very small in a scientific field, this field is likely to be plagued by almost ubiquitous false positive claims. For example, if the majority of true genetic or nutritional determinants of complex diseases confer relative risks less than 1.05, genetic or nutritional epidemiology would be largely utopian endeavors.
Corollary 3: The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true. As shown above, the post-study probability that a finding is true (PPV depends a lot on the pre-study odds (R . Thus, research findings are more likely true in confirmatory designs, such as large phase III randomized controlled trials, or meta-analyses thereof, than in hypothesis-generating experiments. Fields considered highly informative and creative given the wealth of the assembled and tested information, such as microarrays and other high-throughput discovery-oriented research [ 4, 8, 17], should have extremely low PPV.
Corollary 4: The greater the flexibility in designs, definitions, outcomes, and analytical modes in a scientific field, the less likely the research findings are to be true. Flexibility increases the potential for transforming what would be “negative” results into “positive” results, i.e., bias, u . For several research designs, e.g., randomized controlled trials [ 18–20] or meta-analyses [ 21, 22], there have been efforts to standardize their conduct and reporting. Adherence to common standards is likely to increase the proportion of true findings. The same applies to outcomes. True findings may be more common when outcomes are unequivocal and universally agreed (e.g., death rather than when multifarious outcomes are devised (e.g., scales for schizophrenia outcomes [ 23]. Similarly, fields that use commonly agreed, stereotyped analytical methods (e.g., Kaplan-Meier plots and the log-rank test [ 24] may yield a larger proportion of true findings than fields where analytical methods are still under experimentation (e.g., artificial intelligence methods and only “best” results are reported. Regardless, even in the most stringent research designs, bias seems to be a major problem. For example, there is strong evidence that selective outcome reporting, with manipulation of the outcomes and analyses reported, is a common problem even for randomized trails [ 25]. Simply abolishing selective publication would not make this problem go away.
Corollary 5: The greater the financial and other interests and prejudices in a scientific field, the less likely the research findings are to be true. Conflicts of interest and prejudice may increase bias, u . Conflicts of interest are very common in biomedical research [ 26], and typically they are inadequately and sparsely reported [ 26, 27]. Prejudice may not necessarily have financial roots. Scientists in a given field may be prejudiced purely because of their belief in a scientific theory or commitment to their own findings. Many otherwise seemingly independent, university-based studies may be conducted for no other reason than to give physicians and researchers qualifications for promotion or tenure. Such nonfinancial conflicts may also lead to distorted reported results and interpretations. Prestigious investigators may suppress via the peer review process the appearance and dissemination of findings that refute their findings, thus condemning their field to perpetuate false dogma. Empirical evidence on expert opinion shows that it is extremely unreliable [ 28].
Corollary 6: The hotter a scientific field (with more scientific teams involved , the less likely the research findings are to be true. This seemingly paradoxical corollary follows because, as stated above, the PPV of isolated findings decreases when many teams of investigators are involved in the same field. This may explain why we occasionally see major excitement followed rapidly by severe disappointments in fields that draw wide attention. With many teams working on the same field and with massive experimental data being produced, timing is of the essence in beating competition. Thus, each team may prioritize on pursuing and disseminating its most impressive “positive” results. “Negative” results may become attractive for dissemination only if some other team has found a “positive” association on the same question. In that case, it may be attractive to refute a claim made in some prestigious journal. The term Proteus phenomenon has been coined to describe this phenomenon of rapidly alternating extreme research claims and extremely opposite refutations [ 29]. Empirical evidence suggests that this sequence of extreme opposites is very common in molecular genetics [ 29].
These corollaries consider each factor separately, but these factors often influence each other. For example, investigators working in fields where true effect sizes are perceived to be small may be more likely to perform large studies than investigators working in fields where true effect sizes are perceived to be large. Or prejudice may prevail in a hot scientific field, further undermining the predictive value of its research findings. Highly prejudiced stakeholders may even create a barrier that aborts efforts at obtaining and disseminating opposing results. Conversely, the fact that a field is hot or has strong invested interests may sometimes promote larger studies and improved standards of research, enhancing the predictive value of its research findings. Or massive discovery-oriented testing may result in such a large yield of significant relationships that investigators have enough to report and search further and thus refrain from data dredging and manipulation.
Most Research Findings Are False for Most Research Designs and for Most Fields Top
In the described framework, a PPV exceeding 50% is quite difficult to get. Table 4 provides the results of simulations using the formulas developed for the influence of power, ratio of true to non-true relationships, and bias, for various types of situations that may be characteristic of specific study designs and settings. A finding from a well-conducted, adequately powered randomized controlled trial starting with a 50% pre-study chance that the intervention is effective is eventually true about 85% of the time. A fairly similar performance is expected of a confirmatory meta-analysis of good-quality randomized trials: potential bias probably increases, but power and pre-test chances are higher compared to a single randomized trial. Conversely, a meta-analytic finding from inconclusive studies where pooling is used to “correct” the low power of single studies, is probably false if R ? 1:3. Research findings from underpowered, early-phase clinical trials would be true about one in four times, or even less frequently if bias is present. Epidemiological studies of an exploratory nature perform even worse, especially when underpowered, but even well-powered epidemiological studies may have only a one in five chance being true, if R = 1:10. Finally, in discovery-oriented research with massive testing, where tested relationships exceed true ones 1,000-fold (e.g., 30,000 genes tested, of which 30 may be the true culprits [ 30, 31], PPV for each claimed relationship is extremely low, even with considerable standardization of laboratory and statistical methods, outcomes, and reporting thereof to minimize bias.
Table 4. PPV of Research Findings for Various Combinations of Power (1 - ? , Ratio of True to Not-True Relationships (R , and Bias (u
doi:10.1371/journal.pmed.0020124.t004
Claimed Research Findings May Often Be Simply Accurate Measures of the Prevailing Bias Top
As shown, the majority of modern biomedical research is operating in areas with very low pre- and post-study probability for true findings. Let us suppose that in a research field there are no true findings at all to be discovered. History of science teaches us that scientific endeavor has often in the past wasted effort in fields with absolutely no yield of true scientific information, at least based on our current understanding. In such a “null field,” one would ideally expect all observed effect sizes to vary by chance around the null in the absence of bias. The extent that observed findings deviate from what is expected by chance alone would be simply a pure measure of the prevailing bias.
For example, let us suppose that no nutrients or dietary patterns are actually important determinants for the risk of developing a specific tumor. Let us also suppose that the scientific literature has examined 60 nutrients and claims all of them to be related to the risk of developing this tumor with relative risks in the range of 1.2 to 1.4 for the comparison of the upper to lower intake tertiles. Then the claimed effect sizes are simply measuring nothing else but the net bias that has been involved in the generation of this scientific literature. Claimed effect sizes are in fact the most accurate estimates of the net bias. It even follows that between “null fields,” the fields that claim stronger effects (often with accompanying claims of medical or public health importance are simply those that have sustained the worst biases.
For fields with very low PPV, the few true relationships would not distort this overall picture much. Even if a few relationships are true, the shape of the distribution of the observed effects would still yield a clear measure of the biases involved in the field. This concept totally reverses the way we view scientific results. Traditionally, investigators have viewed large and highly significant effects with excitement, as signs of important discoveries. Too large and too highly significant effects may actually be more likely to be signs of large bias in most fields of modern research. They should lead investigators to careful critical thinking about what might have gone wrong with their data, analyses, and results.
Of course, investigators working in any field are likely to resist accepting that the whole field in which they have spent their careers is a “null field.” However, other lines of evidence, or advances in technology and experimentation, may lead eventually to the dismantling of a scientific field. Obtaining measures of the net bias in one field may also be useful for obtaining insight into what might be the range of bias operating in other fields where similar analytical methods, technologies, and conflicts may be operating.
How Can We Improve the Situation? Top
Is it unavoidable that most research findings are false, or can we improve the situation? A major problem is that it is impossible to know with 100% certainty what the truth is in any research question. In this regard, the pure “gold” standard is unattainable. However, there are several approaches to improve the post-study probability.
Better powered evidence, e.g., large studies or low-bias meta-analyses, may help, as it comes closer to the unknown “gold” standard. However, large studies may still have biases and these should be acknowledged and avoided. Moreover, large-scale evidence is impossible to obtain for all of the millions and trillions of research questions posed in current research. Large-scale evidence should be targeted for research questions where the pre-study probability is already considerably high, so that a significant research finding will lead to a post-test probability that would be considered quite definitive. Large-scale evidence is also particularly indicated when it can test major concepts rather than narrow, specific questions. A negative finding can then refute not only a specific proposed claim, but a whole field or considerable portion thereof. Selecting the performance of large-scale studies based on narrow-minded criteria, such as the marketing promotion of a specific drug, is largely wasted research. 1 Moreover, one should be cautious that extremely large studies may be more likely to find a formally statistical significant difference for a trivial effect that is not really meaningfully different from the null [ 32–34].
Second, most research questions are addressed by many teams, and it is misleading to emphasize the statistically significant findings of any single team. What matters is the totality of the evidence. Diminishing bias through enhanced research standards and curtailing of prejudices may also help. However, this may require a change in scientific mentality that might be difficult to achieve. In some research designs, efforts may also be more successful with upfront registration of studies, e.g., randomized trials [ 35]. Registration would pose a challenge for hypothesis-generating research. Some kind of registration or networking of data collections or investigators within fields may be more feasible than registration of each and every hypothesis-generating experiment. Regardless, even if we do not see a great deal of progress with registration of studies in other fields, the principles of developing and adhering to a protocol could be more widely borrowed from randomized controlled trials.
Finally, instead of chasing statistical significance, we should improve our understanding of the range of R values—the pre-study odds—where research efforts operate [ 10]. Before running an experiment, investigators should consider what they believe the chances are that they are testing a true rather than a non-true relationship. Speculated high R values may sometimes then be ascertained. As described above, whenever ethically acceptable, large studies with minimal bias should be performed on research findings that are considered relatively established, to see how often they are indeed confirmed. I suspect several established “classics” will fail the test [ 36].
Nevertheless, most new discoveries will continue to stem from hypothesis-generating research with low or very low pre-study odds. We should then acknowledge that statistical significance testing in the report of a single study gives only a partial picture, without knowing how much testing has been done outside the report and in the relevant field at large. Despite a large statistical literature for multiple testing corrections [ 37], usually it is impossible to decipher how much data dredging by the reporting authors or other research teams has preceded a reported research finding. Even if determining this were feasible, this would not inform us about the pre-study odds. Thus, it is unavoidable that one should make approximate assumptions on how many relationships are expected to be true among those probed across the relevant research fields and research designs. The wider field may yield some guidance for estimating this probability for the isolated research project. Experiences from biases detected in other neighboring fields would also be useful to draw upon. Even though these assumptions would be considerably subjective, they would still be very useful in interpreting research claims and putting them in context.
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