Showing posts with label request for proposal. Show all posts
Showing posts with label request for proposal. Show all posts
Wednesday, January 13, 2016
Friday, October 9, 2015
Blind analysis: Hide results to seek the truth
This would be useful but right now stroke needs plain analysis as to what the hell needs fixing. Once we know the problems to solve we can put out requests for proposals to researchers and have them follow this type of blinding. But first we have to acknowledge that everything in stroke is fucking screwed up. And our stroke associations are not tackling any of the problems.
http://www.nature.com/news/blind-analysis-hide-results-to-seek-the-truth-1.18510
Decades ago, physicists including Richard Feynman noticed something worrying. New estimates of basic physical constants were often closer to published values than would be expected given standard errors of measurement1. They realized that researchers were more likely to 'confirm' past results than refute them — results that did not conform to their expectation were more often systematically discarded or revised.
To minimize this problem, teams of particle physicists and cosmologists developed methods of blind analysis: temporarily and judiciously removing data labels and altering data values to fight bias and error2. By the early 2000s, the technique had become widespread in areas of particle and nuclear physics. Since 2003, one of us (S.P.) has, with colleagues, been using blind analysis for measurements of supernovae that serve as a 'cosmic yardstick' in studies of the unexpected acceleration of the Universe's expansion3.
In several subfields of particle physics and cosmology, a new sort of analytical culture is forming: blind analysis is often considered the only way to trust many results. It is also being used in some clinical-trial protocols (the term 'triple-blinding' sometimes refers to this4), and is increasingly used in forensic laboratories as well.
But the concept is hardly known in the biological, psychological and social sciences. One of us (R.M.) has considerable experience conducting empirical research on legal and public-policy controversies in which concerns about bias are rampant (for example, drug legalization), but first encountered the concept when the two of us co-taught a transdisciplinary course at the University of California, Berkeley, on critical thinking and the role of science in democratic group decision-making. We came to recognize that the methods that physicists were using might improve trust and integrity in many sciences, including those with high-stakes analyses that are easily plagued by bias.
Many motivations distort what inferences we draw from data. These include the desire to support one's theory, to refute one's competitors, to be first to report a phenomenon, or simply to avoid publishing 'odd' results. Such biases can be conscious or unconscious. They can occur irrespective of whether choices are motivated by the search for truth, by the good mentor's desire to help their student write a strong PhD thesis, or just by naked self-interest5.
We argue that blind analysis should be used more broadly in empirical research. Working blind while selecting data and developing and debugging analyses offers an important way to keep scientists from fooling themselves.
Blind analysis ensures that all analytical
decisions have been completed, and all programmes and procedures
debugged, before relevant results are revealed to the experimenter. One
investigator — or, more typically, a suitable computer program —
methodically perturbs data values, data labels or both, often with
several alternative versions of perturbation. The rest of the team then
conducts as much analysis as possible 'in the dark'. Before unblinding,
investigators should agree that they are sufficiently confident of their
analysis to publish whatever the result turns out to be, without
further rounds of debugging or rethinking. (There is no barrier to
conducting extra analyses once data are unblinded, but doing so risks
bias, so researchers should label such further analyses as
'post-blind'.)
There are many ways to do blind analysis. The computer need not (and probably will not) be blinded to data values; it is the display of results that masks information. Techniques must obscure meaningful results while showing enough of the data's structure to allow researchers to find and debug measurement artefacts, irrelevant variables, spurious correlates and other problems. For example, researchers who analyse clinical-trial results without knowing which patients received a placebo should still be able to identify implausible values.
The best methods for blinding depend on the properties of the data (for example, the type of statistical distribution, lower and upper bounds, whether values are discrete or continuous and whether cases were randomly assigned to experimental conditions or passively observed). Both data values and labels can be manipulated to develop a suitable strategy (see 'Blinding strategies').
http://www.nature.com/news/blind-analysis-hide-results-to-seek-the-truth-1.18510
Decades ago, physicists including Richard Feynman noticed something worrying. New estimates of basic physical constants were often closer to published values than would be expected given standard errors of measurement1. They realized that researchers were more likely to 'confirm' past results than refute them — results that did not conform to their expectation were more often systematically discarded or revised.
To minimize this problem, teams of particle physicists and cosmologists developed methods of blind analysis: temporarily and judiciously removing data labels and altering data values to fight bias and error2. By the early 2000s, the technique had become widespread in areas of particle and nuclear physics. Since 2003, one of us (S.P.) has, with colleagues, been using blind analysis for measurements of supernovae that serve as a 'cosmic yardstick' in studies of the unexpected acceleration of the Universe's expansion3.
In several subfields of particle physics and cosmology, a new sort of analytical culture is forming: blind analysis is often considered the only way to trust many results. It is also being used in some clinical-trial protocols (the term 'triple-blinding' sometimes refers to this4), and is increasingly used in forensic laboratories as well.
But the concept is hardly known in the biological, psychological and social sciences. One of us (R.M.) has considerable experience conducting empirical research on legal and public-policy controversies in which concerns about bias are rampant (for example, drug legalization), but first encountered the concept when the two of us co-taught a transdisciplinary course at the University of California, Berkeley, on critical thinking and the role of science in democratic group decision-making. We came to recognize that the methods that physicists were using might improve trust and integrity in many sciences, including those with high-stakes analyses that are easily plagued by bias.
Many motivations distort what inferences we draw from data. These include the desire to support one's theory, to refute one's competitors, to be first to report a phenomenon, or simply to avoid publishing 'odd' results. Such biases can be conscious or unconscious. They can occur irrespective of whether choices are motivated by the search for truth, by the good mentor's desire to help their student write a strong PhD thesis, or just by naked self-interest5.
We argue that blind analysis should be used more broadly in empirical research. Working blind while selecting data and developing and debugging analyses offers an important way to keep scientists from fooling themselves.
Who knows what
Some forms of blinding are well known: for example, shielding both patients and clinicians from knowing who receives an experimental drug or a placebo (double-blinding), or removing names and affiliations from scientific manuscripts to keep peer reviewers from being swayed by authors' identities. But these practices apply to the collection and source of data, rather than the analysis.“Blinding analyses could be as simple as asking a colleague to scramble labels.”
There are many ways to do blind analysis. The computer need not (and probably will not) be blinded to data values; it is the display of results that masks information. Techniques must obscure meaningful results while showing enough of the data's structure to allow researchers to find and debug measurement artefacts, irrelevant variables, spurious correlates and other problems. For example, researchers who analyse clinical-trial results without knowing which patients received a placebo should still be able to identify implausible values.
The best methods for blinding depend on the properties of the data (for example, the type of statistical distribution, lower and upper bounds, whether values are discrete or continuous and whether cases were randomly assigned to experimental conditions or passively observed). Both data values and labels can be manipulated to develop a suitable strategy (see 'Blinding strategies').
Sunday, May 4, 2014
Developing drugs to reduce brain impairment after stroke
Whom is going to write a request for proposal to researchers to solve this problem in humans? Dr. Jessup, Mr. Baranski? Isn't that exactly what a stroke association should be doing? Doesn't your board of directors have the best interests of stroke survivors at the top of the reasons for your organization existence?
http://medicalxpress.com/news/2014-03-drugs-brain-impairment.html
Current treatment for ischaemic stroke, which results from a blood clot, aren't very effective. But research published by my colleagues and I today in the journal Nature Communications shows an emerging drug treatment is effective in mice and could one day reduce the neurological impact in people who've suffered an ischaemic stroke.
By 2020, the World Health Organization predicts that worldwide, the number of years lost to disability resulting from stroke will reach 61 million. The economic burden is similarly massive, costing Australia $49.3 billion a year. So finding better treatments is crucial.
Brain inflammation after stroke
Quick treatment is one way to enhance the prospect of recovering from a stroke.
If patients are treated within around three hours of the stroke, the stroke-inducing clots can be broken down relatively efficiently using a substance called tissue plasminogen activator (tPA). This allows the blood to start flowing again, supplying the brain with the oxygen required to keep the tissue alive.
But after the clot is removed and blood starts flowing, the body produces an unwanted neuroimmune response. This occurs because the damaged brain tissue contains elevated levels of molecules known as proinflammatory cytokines, which regulate the body's response to infection, inflammation and trauma.
These cytokines are able to recruit many other immune cells to the area, leading to further cell death.
Limiting the initial release of these cytokines should therefore help to decrease the excessive local inflammatory response, leading to a decrease in tissue damage and better patient outcomes.
More at link.
http://medicalxpress.com/news/2014-03-drugs-brain-impairment.html
Current treatment for ischaemic stroke, which results from a blood clot, aren't very effective. But research published by my colleagues and I today in the journal Nature Communications shows an emerging drug treatment is effective in mice and could one day reduce the neurological impact in people who've suffered an ischaemic stroke.
By 2020, the World Health Organization predicts that worldwide, the number of years lost to disability resulting from stroke will reach 61 million. The economic burden is similarly massive, costing Australia $49.3 billion a year. So finding better treatments is crucial.
Brain inflammation after stroke
Quick treatment is one way to enhance the prospect of recovering from a stroke.
If patients are treated within around three hours of the stroke, the stroke-inducing clots can be broken down relatively efficiently using a substance called tissue plasminogen activator (tPA). This allows the blood to start flowing again, supplying the brain with the oxygen required to keep the tissue alive.
But after the clot is removed and blood starts flowing, the body produces an unwanted neuroimmune response. This occurs because the damaged brain tissue contains elevated levels of molecules known as proinflammatory cytokines, which regulate the body's response to infection, inflammation and trauma.
These cytokines are able to recruit many other immune cells to the area, leading to further cell death.
Limiting the initial release of these cytokines should therefore help to decrease the excessive local inflammatory response, leading to a decrease in tissue damage and better patient outcomes.
More at link.
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