July 30 PRACTICE VIDEO for JSM talk (All materials for Practice JSM session here)
July 30 PRACTICE VIDEO for JSM talk (All materials for Practice JSM session here)
Science isn’t about predicting one-off events like election results, but that doesn’t mean the way to make election forecasts scientific (which they should be) is to build “theories of voting.” A number of people have sent me articles on statistical aspects of the recent U.S. election, but I don’t have much to say and I like to keep my blog non-political. I won’t violate this rule in making a couple of comments on Faye Flam’s Nov. 11 article: “Why Science Couldn’t Predict a Trump Presidency”[i].
For many people, Donald Trump’s surprise election victory was a jolt to very idea that humans are rational creatures. It tore away the comfort of believing that science has rendered our world predictable. The upset led two New York Times reporters to question whether data science could be trusted in medicine and business. A Guardian columnist declared that big data works for physics but breaks down in the realm of human behavior. Continue reading
Larry Laudan: “When the ‘Not-Guilty’ Falsely Pass for Innocent”, the Frequency of False Acquittals (guest post)
Professor Larry Laudan
Lecturer in Law and Philosophy
University of Texas at Austin
“When the ‘Not-Guilty’ Falsely Pass for Innocent” by Larry Laudan
While it is a belief deeply ingrained in the legal community (and among the public) that false negatives are much more common than false positives (a 10:1 ratio being the preferred guess), empirical studies of that question are very few and far between. While false convictions have been carefully investigated in more than two dozen studies, there are virtually no well-designed studies of the frequency of false acquittals. The disinterest in the latter question is dramatically borne out by looking at discussions among intellectuals of the two sorts of errors. (A search of Google Books identifies some 6.3k discussions of the former and only 144 treatments of the latter in the period from 1800 to now.) I’m persuaded that it is time we brought false negatives out of the shadows, not least because each such mistake carries significant potential harms, typically inflicted by falsely-acquitted recidivists who are on the streets instead of in prison.
In criminal law, false negatives occur under two circumstances: when a guilty defendant is acquitted at trial and when an arrested, guilty defendant has the charges against him dropped or dismissed by the judge or prosecutor. Almost no one tries to measure how often either type of false negative occurs. That is partly understandable, given the fact that the legal system prohibits a judicial investigation into the correctness of an acquittal at trial; the double jeopardy principle guarantees that such acquittals are fixed in stone. Thanks in no small part to the general societal indifference to false negatives, there have been virtually no efforts to design empirical studies that would yield reliable figures on false acquittals. That means that my efforts here to estimate how often they occur must depend on a plethora of indirect indicators. With a bit of ingenuity, it is possible to find data that provide strong clues as to approximately how often a truly guilty defendant is acquitted at trial and in the pre-trial process. The resulting inferences are not precise and I will try to explain why as we go along. As we look at various data sources not initially designed to measure false negatives, we will see that they nonetheless provide salient information about when and why false acquittals occur, thereby enabling us to make an approximate estimate of their frequency.
My discussion of how to estimate the frequency of false negatives will fall into two parts, reflecting the stark differences between the sources of errors in pleas and the sources of error in trials. (All the data to be cited here deal entirely with cases of crimes of violence.) Continue reading
Fraudulent until proved innocent: Is this really the new “Bayesian Forensics”? (rejected post)
Imagine. The New York Times reported a few days ago that the FBI erroneously identified criminals 96% of the time based on probability assessments using forensic hair samples (up until 2000). Sometimes the hair wasn’t even human, it might have come from a dog, a cat or a fur coat! I posted on the unreliability of hair forensics a few years ago. The forensics of bite marks aren’t much better.[i] John Byrd, forensic analyst and reader of this blog had commented at the time that: “At the root of it is the tradition of hiring non-scientists into the technical positions in the labs. They tended to be agents. That explains a lot about misinterpretation of the weight of evidence and the inability to explain the import of lab findings in court.” DNA is supposed to cure all that. So is it? I don’t know, but apparently the FBI “has agreed to provide free DNA testing where there is either a court order or a request for testing by the prosecution.”[ii] See the FBI report.
Here’s the op-ed from the New York Times from April 27, 2015:
The odds were 10-million-to-one, the prosecution said, against hair strands found at the scene of a 1978 murder of a Washington, D.C., taxi driver belonging to anyone but Santae Tribble. Based largely on this compelling statistic, drawn from the testimony of an analyst with the Federal Bureau of Investigation, Mr. Tribble, 17 at the time, was convicted of the crime and sentenced to 20 years to life.
But the hair did not belong to Mr. Tribble. Some of it wasn’t even human. In 2012, a judge vacated Mr. Tribble’s conviction and dismissed the charges against him when DNA testing showed there was no match between the hair samples, and that one strand had come from a dog.
Mr. Tribble’s case — along with the exoneration of two other men who served decades in prison based on faulty hair-sample analysis — spurred the F.B.I. to conduct a sweeping post-conviction review of 2,500 cases in which its hair-sample lab reported a match.
The preliminary results of that review, which Spencer Hsu of The Washington Post reported last week, are breathtaking: out of 268 criminal cases nationwide between 1985 and 1999, the bureau’s “elite” forensic hair-sample analysts testified wrongly in favor of the prosecution, in 257, or 96 percent of the time. Thirty-two defendants in those cases were sentenced to death; 14 have since been executed or died in prison.
The agency is continuing to review the rest of the cases from the pre-DNA era. The Justice Department is working with the Innocence Project and the National Association of Criminal Defense Lawyers to notify the defendants in those cases that they may have grounds for an appeal. It cannot, however, address the thousands of additional cases where potentially flawed testimony came from one of the 500 to 1,000 state or local analysts trained by the F.B.I. Peter Neufeld, co-founder of the Innocence Project, rightly called this a “complete disaster.”
Law enforcement agencies have long known of the dubious value of hair-sample analysis. A 2009 report by the National Research Council found “no scientific support” and “no uniform standards” for the method’s use in positively identifying a suspect. At best, hair-sample analysis can rule out a suspect, or identify a wide class of people with similar characteristics.
Yet until DNA testing became commonplace in the late 1990s, forensic analysts testified confidently to the near-certainty of matches between hair found at crime scenes and samples taken from defendants. The F.B.I. did not even have written standards on how analysts should testify about their findings until 2012.
Head, Methodology and Statistics Group,
Competence Center for Methodology and Statistics (CCMS), Luxembourg
Is Pooling Fooling?
‘And take the case of a man who is ill. I call two physicians: they differ in opinion. I am not to lie down, and die between them: I must do something.’ Samuel Johnson, in Boswell’s A Journal of a Tour to the Hebrides
A common dilemma facing meta-analysts is what to put together with what? One may have a set of trials that seem to be approximately addressing the same question but some features may differ. For example, the inclusion criteria might have differed with some trials only admitting patients who were extremely ill but with other trials treating the moderately ill as well. Or it might be the case that different measurements have been taken in different trials. An even more extreme case occurs when different, if presumed similar, treatments have been used.
It is helpful to make a point of terminology here. In what follows I shall be talking about pooling results from various trials. This does not involve naïve pooling of patients across trials. I assume that each trial will provide a valid within- trial comparison of treatments. It is these comparisons that are to be pooled (appropriately).
A possible way to think of this is in terms of a Bayesian model with a prior distribution covering the extent to which results might differ as features of trials are changed. I don’t deny that this is sometimes an interesting way of looking at things (although I do maintain that it is much more tricky than many might suppose) but I would also like to draw attention to the fact that there is a frequentist way of looking at this problem that is also useful.
Suppose that we have k ‘null’ hypotheses that we are interested in testing, each being capable of being tested in one of k trials. We can label these Hn1, Hn2, … Hnk. We are perfectly entitled to test the null hypothesis Hjoint that they are all jointly true. In doing this we can use appropriate judgement to construct a composite statistic based on all the trials whose distribution is known under the null. This is a justification for pooling. Continue reading
“Only those samples which fit the model best in cross validation were included” (whistleblower) “I suspect that we likely disagree with what constitutes validation” (Potti and Nevins)
So it turns out there was an internal whistleblower in the Potti scandal at Duke after all (despite denials by the Duke researchers involved ). It was a medical student Brad Perez. It’s in the Jan. 9, 2015 Cancer Letter*. Ever since my first post on Potti last May (part 1), I’ve received various e-mails and phone calls from people wishing to confide their inside scoops and first-hand experiences working with Potti (in a statistical capacity) but I was waiting for some published item. I believe there’s a court case still pending (anyone know?)
Now here we have a great example of something I am increasingly seeing: Challenges to the scientific credentials of data analysis are dismissed as mere differences in statistical philosophies or as understandable disagreements about stringency of data validation.[i] This is further enabled by conceptual fuzziness as to what counts as meaningful replication, validation, legitimate cross-validation.
If so, then statistical philosophy is of crucial practical importance.[ii]
Significance Levels are Made a Whipping Boy on Climate Change Evidence: Is .05 Too Strict? (Schachtman on Oreskes)
Given the daily thrashing significance tests receive because of how preposterously easy it is claimed to satisfy the .05 significance level requirement, it’s surprising[i] to hear Naomi Oreskes blaming the .05 standard as demanding too high a burden of proof for accepting climate change. “Playing Dumb on Climate Change,” N.Y. Times Sunday Rev. at 2 (Jan. 4, 2015). Is there anything for which significance levels do not serve as convenient whipping boys? Thanks to lawyer Nathan Schachtman for alerting me to her opinion piece today (congratulations to Oreskes!),and to his current blogpost. I haven’t carefully read her article, but one claim jumped out: scientists, she says, “practice a form of self-denial, denying themselves the right to believe anything that has not passed very high intellectual hurdles.” If only! *I add a few remarks at the end. Anyhow here’s Schachtman’s post:
“Playing Dumb on Statistical Significance”
by Nathan Schachtman
Naomi Oreskes is a professor of the history of science in Harvard University. Her writings on the history of geology are well respected; her writings on climate change tend to be more adversarial, rhetorical, and ad hominem. See, e.g., Naomi Oreskes,Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues from Tobacco Smoke to Global Warming(N.Y. 2010). Oreskes’ abuse of the meaning of significance probability for her own rhetorical ends is on display in today’s New York Times. Naomi Oreskes, “Playing Dumb on Climate Change,” N.Y. Times Sunday Rev. at 2 (Jan. 4, 2015).
Oreskes wants her readers to believe that those who are resisting her conclusions about climate change are hiding behind an unreasonably high burden of proof, which follows from the conventional standard of significance in significance probability. In presenting her argument, Oreskes consistently misrepresents the meaning of statistical significance and confidence intervals to be about the overall burden of proof for a scientific claim:
“Typically, scientists apply a 95 percent confidence limit, meaning that they will accept a causal claim only if they can show that the odds of the relationship’s occurring by chance are no more than one in 20. But it also means that if there’s more than even a scant 5 percent possibility that an event occurred by chance, scientists will reject the causal claim. It’s like not gambling in Las Vegas even though you had a nearly 95 percent chance of winning.”
Although the confidence interval is related to the pre-specified Type I error rate, alpha, and so a conventional alpha of 5% does lead to a coefficient of confidence of 95%, Oreskes has misstated the confidence interval to be a burden of proof consisting of a 95% posterior probability. The “relationship” is either true or not; the p-value or confidence interval provides a probability for the sample statistic, or one more extreme, on the assumption that the null hypothesis is correct. The 95% probability of confidence intervals derives from the long-term frequency that 95% of all confidence intervals, based upon samples of the same size, will contain the true parameter of interest.
Oreskes is an historian, but her history of statistical significance appears equally ill considered. Here is how she describes the “severe” standard of the 95% confidence interval: Continue reading
S. Stanley Young, PhD
Bioinformatics National Institute of Statistical Sciences Research Triangle Park, NC
Are there mortality co-benefits to the Clean Power Plan? It depends.
Some years ago, I listened to a series of lectures on finance. The professor would ask a rhetorical question, pause to give you some time to think, and then, more often than not, answer his question with, “It depends.” Are there mortality co-benefits to the Clean Power Plan? Is mercury coming from power plants leading to deaths? Well, it depends.
So, rhetorically, is an increase in CO2 a bad thing? There is good and bad in everything. Well, for plants an increase in CO2 is a good thing. They grow faster. They convert CO2 into more food and fiber. They give off more oxygen, which is good for humans. Plants appear to be CO2 starved.
It is argued that CO2 is a greenhouse gas and an increase in CO2 will raise temperatures, ice will melt, sea levels will rise, and coastal area will flood, etc. It depends. In theory yes, in reality, maybe. But a lot of other events must be orchestrated simultaneously. Obviously, that scenario depends on other things as, for the last 18 years, CO2 has continued to go up and temperatures have not. So it depends on other factors, solar radiance, water vapor, El Nino, sun spots, cosmic rays, earth presession, etc., just what the professor said.
So suppose ambient temperatures do go up a few degrees. On balance, is that bad for humans? The evidence is overwhelming that warmer is better for humans. One or two examples are instructive. First, Cox et al., (2013) with the title, “Warmer is healthier: Effects on mortality rates of changes in average fine particulate matter (PM2.5) concentrations and temperatures in 100 U.S. cities.” To quote from the abstract of that paper, “Increases in average daily temperatures appear to signiﬁcantly reduce average daily mortality rates, as expected from previous research.” Here is their plot of daily mortality rate versus Max temperature. It is clear that as the maximum temperature in a city goes up, mortality goes down. So if the net effect of increasing CO2 is increasing temperature, there should be a reduction in deaths. Continue reading
Until a few weeks ago, I’d never even heard of a “power morcellator.” Nor was I aware of the controversy that has pitted defenders of a woman’s right to choose a minimally invasive laparoscopic procedure in removing fibroids—enabled by the power morcellator–and those who decry the danger it poses in spreading an undetected uterine cancer throughout a woman’s abdomen. The most outspoken member of the anti-morcellation group is surgeon Hooman Noorchashm. His wife, Dr. Amy Reed, had a laparoscopic hysterectomy that resulted in morcellating a hidden cancer, progressing it to Stage IV sarcoma. Below is their video (link is here), followed by a recent FDA warning. I may write this in stages or parts. (I will withhold my view for now, I’d like to know what you think.)
Morcellation: (The full Article is here.)
UPDATED Laparoscopic Uterine Power Morcellation in Hysterectomy and Myomectomy: FDA Safety Communication
The following information updates our April 17, 2014 communication.
Date Issued: Nov. 24, 2014
Product: Laparoscopic power morcellators are medical devices used during different types of laparoscopic (minimally invasive) surgeries. These can include certain procedures to treat uterine fibroids, such as removing the uterus (hysterectomy) or removing the uterine fibroids (myomectomy). Morcellation refers to the division of tissue into smaller pieces or fragments and is often used during laparoscopic surgeries to facilitate the removal of tissue through small incision sites.
Purpose: When used for hysterectomy or myomectomy in women with uterine fibroids, laparoscopic power morcellation poses a risk of spreading unsuspected cancerous tissue, notably uterine sarcomas, beyond the uterus. The FDA is warning against using laparoscopic power morcellators in the majority of women undergoing hysterectomy or myomectomy for uterine fibroids. Health care providers and patients should carefully consider available alternative treatment options for the removal of symptomatic uterine fibroids.
Summary of Problem and Scope: Uterine fibroids are noncancerous growths that develop from the muscular tissue of the uterus. Most women will develop uterine fibroids (also called leiomyomas) at some point in their lives, although most cause no symptoms1. In some cases, however, fibroids can cause symptoms, including heavy or prolonged menstrual bleeding, pelvic pressure or pain, and/or frequent urination, requiring medical or surgical therapy.
Many women choose to undergo laparoscopic hysterectomy or myomectomy because these procedures are associated with benefits such as a shorter post-operative recovery time and a reduced risk of infection compared to abdominal hysterectomy and myomectomy2. Many of these laparoscopic procedures are performed using a power morcellator.
Based on an FDA analysis of currently available data, we estimate that approximately 1 in 350 women undergoing hysterectomy or myomectomy for the treatment of fibroids is found to have an unsuspected uterine sarcoma, a type of uterine cancer that includes leiomyosarcoma. At this time, there is no reliable method for predicting or testing whether a woman with fibroids may have a uterine sarcoma.
If laparoscopic power morcellation is performed in women with unsuspected uterine sarcoma, there is a risk that the procedure will spread the cancerous tissue within the abdomen and pelvis, significantly worsening the patient’s long-term survival. While the specific estimate of this risk may not be known with certainty, the FDA believes that the risk is higher than previously understood. Continue reading
Head, Methodology and Statistics Group
Competence Center for Methodology and Statistics (CCMS)
Responder despondency: myths of personalized medicine
The road to drug development destruction is paved with good intentions. The 2013 FDA report, Paving the Way for Personalized Medicine has an encouraging and enthusiastic foreword from Commissioner Hamburg and plenty of extremely interesting examples stretching back decades. Given what the report shows can be achieved on occasion, given the enthusiasm of the FDA and its commissioner, given the amazing progress in genetics emerging from the labs, a golden future of personalized medicine surely awaits us. It would be churlish to spoil the party by sounding a note of caution but I have never shirked being churlish and that is exactly what I am going to do. Continue reading
Here are the slides from my presentation (May 17) at the Scientism workshop in NYC. (They’re sketchy since we were trying for 25-30 minutes.) Below them are some mini notes on some of the talks.
We are pleased to announce our guest speaker at Thursday’s seminar (April 24, 2014): “Statistics and Scientific Integrity”:
Author of Resampling-Based Multiple Testing, Westfall and Young (1993) Wiley.
The main readings for the discussion are:
- Young, S. & Karr, A. (2011). Deming, Data and Observational Studies. Signif. 8 (3), 116–120.
- Begley & Ellis (2012) Raise standards for preclinical cancer research. Nature 483: 531-533.
- Ioannidis (2005). Why most published research ﬁndings are false. PLoS Med 2(8): e124.
- Peng, R. D., Dominici, F. & Zeger, S. L. (2006). “Reproducible Epidemiologic Research” American Journal of Epidemiology 163 (9), 783-789.
S. Stanley Young, PhD Assistant Director for Bioinformatics National Institute of Statistical Sciences Research Triangle Park, NC
Much is made by some of the experimental biologists that their art is oh so sophisticated that mere mortals do not have a chance [to successfully replicate]. Bunk. Agriculture replicates all the time. That is why food is so cheap. The world is growing much more on fewer acres now than it did 10 years ago. Materials science is doing remarkable things using mixtures of materials. Take a look at just about any sports equipment. These two areas and many more use statistical methods: design of experiments, randomization, blind reading of results, etc. and these methods replicate, quite well, thank you. Read about Edwards Deming. Experimental biology experiments are typically run by small teams in what is in effect a cottage industry. Herr professor is usually not in the lab. He/she is busy writing grants. A “hands” guy is in the lab. A computer guy does the numbers. No one is checking other workers’ work. It is a cottage industry to produce papers.
There is a famous failure to replicate that appeared in Science. A pair of non-estrogens was reported to have a strong estrogenic effect. Six labs wrote into Science saying the could not replicate the effect. I think the back story is as follows. The hands guy tested a very large number of pairs of chemicals. The most extreme pair looked unusual. Lab boss said, write it up. Every assay has some variability, so they reported extreme variability as real. Failure to replicate in six labs. Science editors says, what gives. Lab boss goes to hands guy and says run the pair again. No effect. Lab boss accuses hands guy of data fabrication. They did not replicate their own finding before rushing to publish. I asked the lab for the full data set, but they refused to provide the data. The EPA is still chasing this will of the wisp, environmental estrogens. False positive results with compelling stories can live a very long time. See [i].
Begley and Ellis visited labs. They saw how the work was done. There are instances where something was tried over and over and when it worked “as expected”, it was a rap. Write the paper and move on. I listened to a young researcher say that she tried for 6 months to replicate results of a paper. Informal conversations with scientists support very poor replication.
One can say that the jury is out as there have been few serious attempts to systematically replicate. There is now starting systematic replication. I say less than 50% of experimental biology claims will replicate.
[i]Hormone Hysterics. Tulane University researchers published a 1996 study claiming that combinations of manmade chemicals (pesticides and PCBs) disrupted normal hormonal processes, causing everything from cancer to infertility to attention deficit disorder.
Media, regulators and environmentalists hailed the study as “astonishing.” Indeed it was as it turned out to be fraud, according to an October 2001 report by federal investigators. Though the study was retracted from publication, the law it spawned wasn’t and continues to be enforced by the EPA. Read more…
The recent joint statement(1) by the Pharmaceutical Research and Manufacturers of America (PhRMA) and the European Federation of Pharmaceutical Industries and Associations(EFPIA) represents a further step in what has been a slow journey towards (one assumes) will be the achieved goal of sharing clinical trial data. In my inaugural lecture of 1997 at University College London I called for all pharmaceutical companies to develop a policy for sharing trial results and I have repeated this in many places since(2-5). Thus I can hardly complain if what I have been calling for for over 15 years is now close to being achieved.
However, I have now recently been thinking about it again and it seems to me that there are some problems that need to be addressed. One is the issue of patient confidentiality. Ideally, covariate information should be exploitable as such often increases the precision of inferences and also the utility of decisions based upon them since they (potentially) increase the possibility of personalising medical interventions. However, providing patient-level data increases the risk of breaching confidentiality. This is a complicated and difficult issue about which, however, I have nothing useful to say. Instead I want to consider another matter. What will be the influence on the quality of the inferences we make of enabling many subsequent researchers to analyse the same data?
One of the reasons that many researchers have called for all trials to be published is that trials that are missing tend to be different from those that are present. Thus there is a bias in summarising evidence from published trial only and it can be a difficult task with no guarantee of success to identify those that have not been published. This is a wider reflection of the problem of missing data within trials. Such data have long worried trialists and the Food and Drug Administration (FDA) itself has commissioned a report on the subject from leading experts(6). On the European side the Committee for Medicinal Products for Human Use (CHMP) has a guideline dealing with it(7).
However, the problem is really a particular example of data filtering and it also applies to statistical analysis. If the analyses that are present have been selected from a wider set, then there is a danger that they do not provide an honest reflection of the message that is in the data. This problem is known as that of multiplicity and there is a huge literature dealing with it, including regulatory guidance documents(8, 9).
Within drug regulation this is dealt with by having pre-specified analyses. The broad outlines of these are usually established in the trial protocol and the approach is then specified in some detail in the statistical analysis plan which is required to be finalised before un-blinding of the data. The strategies used to control for multiplicity will involve some combination of defining a significance testing route (an order in which test must be performed and associated decision rules) and reduction of the required level of significance to detect an event.
I am not a great fan of these manoeuvres, which can be extremely complex. One of my objections is that it is effectively assumed that the researchers who chose them are mandated to circumscribe the inferences that scientific posterity can make(10). I take the rather more liberal view that provided that everything that is tested is reported one can test as much as one likes. The problem comes if there is selective use of results and in particular selective reporting. Nevertheless, I would be the first to concede the value of pre-specification in clarifying the thinking of those about to embark on conducting a clinical trial and also in providing a ‘template of trust’ for the regulator when provided with analyses by the sponsor.
However, what should be our attitude to secondary analyses? From one point of view these should be welcome. There is always value in looking at data from different perspectives and indeed this can be one way of strengthening inferences in the way suggested nearly 50 years ago by Platt(11). There are two problems, however. First, not all perspectives are equally valuable. Some analyses in the future, no doubt, will be carried out by those with little expertise and in some cases, perhaps, by those with a particular viewpoint to justify. There is also the danger that some will carry out multiple analyses (of which, when one consider the possibility of changing endpoints, performing transformations, choosing covariates and modelling framework there are usually a great number) but then only present those that are ‘interesting’. It is precisely to avoid this danger that the ritual of pre-specified analysis is insisted upon by regulators. Must we also insist upon it for those seeking to reanalyse?
To do so would require such persons to do two things. First, they would have to register the analysis plan before being granted access to the data. Second, they would have to promise to make the analysis results available, otherwise we will have a problem of missing analyses to go with the problem of missing trials. I think that it is true to say that we are just beginning to feel our way with this. It may be that the chance has been lost and that the whole of clinical research will be ‘world wide webbed’: there will be a mass of information out there but we just don’t know what to believe. Whatever happens the era of privileged statistical analyses by the original data collectors is disappearing fast.
1. PhRMA, EFPIA. Principles for Responsible Clinical Trial Data Sharing. PhRMA; 2013 [cited 2013 31 August]; Available from: http://phrma.org/sites/default/files/pdf/PhRMAPrinciplesForResponsibleClinicalTrialDataSharing.pdf.
2. Senn SJ. Statistical quality in analysing clinical trials. Good Clinical Practice Journal. [Research Paper]. 2000;7(6):22-6.
3. Senn SJ. Authorship of drug industry trials. Pharm Stat. [Editorial]. 2002;1:5-7.
4. Senn SJ. Sharp tongues and bitter pills. Significance. [Review]. 2006 September 2006;3(3):123-5.
5. Senn SJ. Pharmaphobia: fear and loathing of pharmaceutical research. [pdf] 1997 [updated 31 August 2013; cited 2013 31 August ]; Updated version of paper originally published on PharmInfoNet].
6. Little RJ, D’Agostino R, Cohen ML, Dickersin K, Emerson SS, Farrar JT, et al. The prevention and treatment of missing data in clinical trials. N Engl J Med. 2012 Oct 4;367(14):1355-60.
7. Committee for Medicinal Products for Human Use (CHMP). Guideline on Missing Data in Confirmatory Clinical Trials London: European Medicine Agency; 2010. p. 1-12.
8. Committee for Proprietary Medicinal Products. Points to consider on multiplicity issues in clinical trials. London: European Medicines Evaluation Agency2002.
9. International Conference on Harmonisation. Statistical principles for clinical trials (ICH E9). Statistics in Medicine. 1999;18:1905-42.
10. Senn S, Bretz F. Power and sample size when multiple endpoints are considered. Pharm Stat. 2007 Jul-Sep;6(3):161-70.
11. Platt JR. Strong Inference: Certain systematic methods of scientific thinking may produce much more rapid progress than others. Science. 1964 Oct 16;146(3642):347-53.
Since posting on the High Quality Research act a few weeks ago, I’ve been following it in the news, have received letters from professional committees (asking us to write letters), and now see that Nathan A. Schachtman, Esq., PC posted the following on May 25, 2013 on his legal blog*:
“The High Quality Research Act” (HQRA), which has not been formally introduced in Congress, continues to draw attention. See“Clowns to the left of me, Jokers to the right.” Last week, Sarewitz suggests that “the problem” is the hype about the benefits of pure research and the let down that results from the realization that scientific progress is “often halting and incremental,” with much research not “particularly innovative or valuable.” Fair enough, but why is this Congress such an unsophisticated consumer of scientific research in the 21st century? How can it be a surprise that the scientific community engages in the same rent-seeking behaviors as do other segments of our society? Has it escaped Congress’s attention that scientists are subject to enthusiasms and group think, just like, … congressmen?
Still, Sarewitz believes that the HQRA bill is not particularly threatening to the funding of science:
“In other words, it’s not a very good bill, but neither is it much of a threat. In fact, it’s just the latest skirmish in a long-running battle for political control over publicly funded science — one fought since at least 1947, when President Truman vetoed the first bill to create the NSF because it didn’t include strong enough lines of political accountability.”
This sanguine evaluation misses the effect of the superlatives in the criteria for National Science Foundation funding:
“(1) is in the interests of the United States to advance the national health, prosperity, or welfare, and to secure the national defense by promoting the progress of science;
(2) is the finest quality, is ground breaking, and answers questions or solves problems that are of utmost importance to society at large; and
(3) is not duplicative of other research projects being funded by the Foundation or other Federal science agencies.” Continue reading
After a 6-week hiatus from flying, I’m back in the role of female opt-out[i] in a brand new Delta[ii] terminal with free internet and ipads[iii]. I heard last week that the TSA plans to allow small knives in carry-ons, for the first time since 9/11, as “part of an overall risk-based security approach”. But now it appears that flight attendants, pilot unions, a number of elected officials, and even federal air marshals are speaking out against the move, writing letters and petitions of opposition.
“The Flight Attendants Union Coalition, representing nearly 90,000 flight attendants, and the Coalition of Airline Pilots Associations, which represents 22,000 airline pilots, also oppose the rule change.”
Former flight attendant Tiffany Hawk is “stupefied” by the move, “especially since the process that turns checkpoints into maddening logjams — removing shoes, liquids and computers — remains unchanged,” she wrote in an opinion column for CNN. Link is here. Continue reading
Stanley Young recently shared his summary testimony with me, and has agreed to my posting it.
One-page Summary Young
Testimony of Committee on Science, Space and Technology, 5 March 2013
Scientific Integrity and Transparency
S. Stanley Young, PhD, FASA, FAAAS
Integrity and transparency are two sides of the same coin. Transparency leads to integrity. Transparency means that study protocol, statistical analysis code and data sets used in papers supporting regulation by the EPA should be publicly available as quickly as possible and not just going forward. Some might think that peer review is enough to ensure the validity of claims made in scientific papers. Peer review only says that the work meets the common standards of the discipline and on the face of it, the claims are plausible, Feinstein, Science, 1988. Peer review is not enough. Continue reading
At the end of last year I received a strange email from the editor of the British Medical Journal(BMJ) appealing for ‘evidence’ to persuade the UK parliament of the necessity of making sure that data for clinical trials conducted by the pharmaceutical industry are made readily available to all and sundry. I don’t disagree with this aim. In fact in an article(1) I published over a dozen years ago I wrote ‘No sponsor who refuses to provide end-users with trial data deserves to sell drugs.’(P26)
However, the way in which the BMJ is choosing to collect evidence does not set a good example. It is one I hope that all scientists would disown and one of which even journalists should be ashamed.
The letter reads
“Dear Prof Senn,
We need your help to show the House of Commons Science and Technology Select Committee the true scale of the problem of missing clinical data by collating a list of examples. Continue reading