BIAS IN MEDICAL RESEARCH ARTICLE ANALYSISWritten Assignment This assignment contains a very powerful analysis of how bias and statistical manipulation have resulted in flawed medical studies (hint: do not attempt to understand the statistics he presents. Focus on the concepts the author is trying to address). Read the article and write a 3 page report answering the following questions:Write a summary of the article. Indicate your assessment of what the article is about and the major findings of the article.What is the authorâ??s thesis or main point expressed in this article? Why does he believe that most medical research is flawed?How does this article relate to what you have learned in class about experimental research?Before reading this article, what were your beliefs and attitudes toward the quality and accuracy of research studies findings published in professional journals? How has reading this article changed, if at all, your beliefs and attitudes toward published research findings?What does the author believe can be done to improve the quality and objectivity of research? Do you agree or disagree with his recommendations? Explain.FORMAT: 3 pages, 1″ margins, double spaced, normal font. All papers must be neatly typed and proofread.DUE DATE: By the day of the Final ExamGRADING: This writing assignment is worth 30 points. Points will be reduced for spelling, grammar, etc., and for lack of content.
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MARK W. TENGLER, M.S.
BIAS IN MEDICAL RESEARCH ARTICLE ANALYSIS
Following this instruction sheet is a very powerful analysis of how bias and statistical
manipulation have resulted in flawed medical studies. (hint: do not attempt to understand the statistics
he presents. Focus on the concepts the author is trying to address). Read the article and write a 3 page
report answering the following questions:
1. Write an summary of the article. Indicate your assessment of what the article is about
and the major findings of the article.
2. What is the authorâ??s thesis or main point expressed in this article? Why does he believe
that most medical research is flawed?
3. How does this article relate to what you have learned in class about experimental
4. Before reading this article, what were your beliefs and attitudes toward the quality and
accuracy of research study findings published in professional journals? How has reading
this article affected, if at all, your beliefs and attitudes toward published research
5. What does the author believe can be done to improve the quality and objectivity of
research? Do you agree or disagree with his recommendations? Explain.
3 pages, 1″ margins, double spaced, normal font. All papers must be neatly
typed and proofread.
By the day of the Final Exam
This writing assignment is worth 30 points. Points will be reduced for
spelling, grammar, etc., and for lack of content.
Open access, freely available online
Why Most Published Research Findings
John P. A. Ioannidis
There is increasing concern that most
current published research ï¬ndings 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 scientiï¬c
ï¬eld. In this framework, a research ï¬nding
is less likely to be true when the studies
conducted in a ï¬eld are smaller; when
effect sizes are smaller; when there is a
greater number and lesser preselection
of tested relationships; where there is
greater ï¬?exibility in designs, deï¬nitions,
outcomes, and analytical modes; when
there is greater ï¬nancial and other
interest and prejudice; and when more
teams are involved in a scientiï¬c ï¬eld
in chase of statistical signiï¬cance.
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 scientiï¬c
ï¬elds, claimed research ï¬ndings 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.
ublished research ï¬ndings 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
[1â??3] to the most modern molecular
research [4,5]. There is increasing
concern that in modern research, false
ï¬ndings may be the majority or even
the vast majority of published research
claims [6â??8]. However, this should
not be surprising. It can be proven
that most claimed research ï¬ndings
are false. Here I will examine the key
The Essay section contains opinion pieces on topics
of broad interest to a general medical audience.
PLoS Medicine | www.plosmedicine.org
factors that inï¬?uence this problem and
some corollaries thereof.
Modeling the Framework for False
Several methodologists have
pointed out [9â??11] that the high
rate of nonreplication (lack of
conï¬rmation) of research discoveries
is a consequence of the convenient,
yet ill-founded strategy of claiming
conclusive research ï¬ndings solely on
the basis of a single study assessed by
formal statistical signiï¬cance, 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
It can be proven that
most claimed research
ï¬ndings are false.
should be interpreted based only on
p-values. Research ï¬ndings are deï¬ned
here as any relationship reaching
formal statistical signiï¬cance, e.g.,
effective interventions, informative
predictors, risk factors, or associations.
â??Negativeâ? research is also very useful.
â??Negativeâ? is actually a misnomer, and
the misinterpretation is widespread.
However, here we will target
relationships that investigators claim
exist, rather than null ï¬ndings.
As has been shown previously, the
probability that a research ï¬nding
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
signiï¬cance [10,11]. Consider a 2 Ã? 2
table in which research ï¬ndings are
compared against the gold standard
of true relationships in a scientiï¬c
ï¬eld. In a research ï¬eld both true and
false hypotheses can be made about
the presence of relationships. Let R
be the ratio of the number of â??true
relationshipsâ? to â??no relationshipsâ?
among those tested in the ï¬eld. R
is characteristic of the ï¬eld and can
vary a lot depending on whether the
ï¬eld 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 ï¬elds where either there
is only one true relationship (among
many that can be hypothesized) or
the power is similar to ï¬nd any of the
several existing true relationships. The
pre-study probability of a relationship
being true is Râ?(R 1). The probability
of a study ï¬nding a true relationship
reï¬?ects the power 1 â?? Î² (one minus
the Type II error rate). The probability
of claiming a relationship when none
truly exists reï¬?ects the Type I error
rate, Î±. Assuming that c relationships
are being probed in the ï¬eld, the
expected values of the 2 Ã? 2 table are
given in Table 1. After a research
ï¬nding has been claimed based on
achieving formal statistical signiï¬cance,
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 . According to the 2
Ã? 2 table, one gets PPV = (1 â?? Î²)Râ?(R
â?? Î²R Î±). A research ï¬nding is thus
Citation: Ioannidis JPA (2005) Why most published
research ï¬ndings are false. PLoS Med 2(8): e124.
Copyright: Â© 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.
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: email@example.com
Competing Interests: The author has declared that
no competing interests exist.
August 2005 | Volume 2 | Issue 8 | e124
Table 1. Research Findings and True Relationships
c(1 â?? Î²)R/(R 1)
c(1 â?? Î±)/(R 1)
c(R Î± â?? Î²R)/(R 1)
c(1 â?? Î± Î²R)/(R 1)
more likely true than false if (1 â?? Î²)R
> Î±. Since usually the vast majority of
investigators depend on Î± = 0.05, this
means that a research ï¬nding is more
likely true than false if (1 â?? Î²)R > 0.05.
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 ï¬ndings being indeed true.
We will try to model these two factors in
the context of similar 2 Ã? 2 tables.
First, let us deï¬ne bias as the
combination of various design, data,
analysis, and presentation factors that
tend to produce research ï¬ndings
when they should not be produced.
Let u be the proportion of probed
analyses that would not have been
â??research ï¬ndings,â? but nevertheless
end up presented and reported as
such, because of bias. Bias should not
be confused with chance variability
that causes some ï¬ndings 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 ï¬ndings.
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 â?? Î²]R
uÎ²R)â?(R Î± â?? Î²R u â?? uÎ± uÎ²R), and
PPV decreases with increasing u, unless
1 â?? Î² â?¤ Î±, i.e., 1 â?? Î² â?¤ 0.05 for most
situations. Thus, with increasing bias,
the chances that a research ï¬nding
is true diminish considerably. This is
shown for different levels of power and
for different pre-study odds in Figure 1.
Conversely, true research ï¬ndings
may occasionally be annulled because
of reverse bias. For example, with large
measurement errors relationships
PLoS Medicine | www.plosmedicine.org
are lost in noise , or investigators
use data inefï¬ciently or fail to notice
statistically signiï¬cant relationships, or
there may be conï¬?icts of interest that
tend to â??buryâ? signiï¬cant ï¬ndings .
There is no good large-scale empirical
evidence on how frequently such
reverse bias may occur across diverse
research ï¬elds. However, it is probably
fair to say that reverse bias is not as
common. Moreover measurement
errors and inefï¬cient 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.
Testing by Several Independent
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 ï¬nding, and
this receives unilateral attention.
The probability that at least one
study, among several done on the
same question, claims a statistically
signiï¬cant research ï¬nding is easy to
estimate. For n independent studies of
equal power, the 2 Ã? 2 table is shown in
Table 3: PPV = R(1 â?? Î²n)â?(R 1 â?? [1 â??
Î±]n â?? RÎ²n) (not considering bias). With
increasing number of independent
studies, PPV tends to decrease, unless
1 â?? Î² < Î±, i.e., typically 1 â?? Î² < 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 Î²n is replaced by the product of the terms Î²i for i = 1 to n, but inferences are similar. Corollaries 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 ï¬nding is indeed true. Corollary 1: The smaller the studies conducted in a scientiï¬c ï¬eld, the less likely the research ï¬ndings are to be true. Small sample size means smaller power and, for all functions above, the PPV for a true research ï¬nding decreases as power decreases towards 1 â?? Î² = 0.05. Thus, other factors being equal, research ï¬ndings are more likely true in scientiï¬c ï¬elds that undertake large studies, such as randomized controlled trials in cardiology (several thousand subjects randomized)  than in scientiï¬c ï¬elds with small studies, such as most research of molecular predictors (sample sizes 100fold smaller) . Corollary 2: The smaller the effect sizes in a scientiï¬c ï¬eld, the less likely the research ï¬ndings are to be true. Power is also related to the effect size. Thus research ï¬ndings are more likely true in scientiï¬c ï¬elds with large effects, such as the impact of smoking on cancer or cardiovascular disease (relative risks 3â??20), than in scientiï¬c ï¬elds where postulated effects are small, such as genetic risk factors for multigenetic diseases (relative risks 1.1â??1.5) . Modern epidemiology is increasingly obliged to target smaller Table 2. Research Findings and True Relationships in the Presence of Bias Research Finding True Relationship Yes No Total Yes No Total (c[1 â?? Î²]R ucÎ²R)/(R 1) (1 â?? u)cÎ²R/(R 1) cR/(R 1) cÎ± uc(1 â?? Î±)/(R 1) (1 â?? u)c(1 â?? Î±)/(R 1) c/(R 1) c(R Î± â?? Î²R u â?? uÎ± uÎ²R)/(R 1) c(1 â?? u)(1 â?? Î± Î²R)/(R 1) c DOI: 10.1371/journal.pmed.0020124.t002 0697 August 2005 | Volume 2 | Issue 8 | e124 effect sizes . Consequently, the proportion of true research ï¬ndings is expected to decrease. In the same line of thinking, if the true effect sizes are very small in a scientiï¬c ï¬eld, this ï¬eld 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 scientiï¬c ï¬eld, the less likely the research ï¬ndings are to be true. As shown above, the post-study probability that a ï¬nding is true (PPV) depends a lot on the pre-study odds (R). Thus, research ï¬ndings are more likely true in conï¬rmatory 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 discoveryoriented research [4,8,17], should have extremely low PPV. Corollary 4: The greater the ï¬?exibility in designs, deï¬nitions, outcomes, and analytical modes in a scientiï¬c ï¬eld, the less likely the research ï¬ndings are to be true. Flexibility increases the potential for transforming what would be â??negativeâ? results into â??positiveâ? results, i.e., bias, u. For several research designs, e.g., randomized controlled trials [18â??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 ï¬ndings. The same applies to outcomes. True ï¬ndings 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) . Similarly, ï¬elds that use commonly agreed, stereotyped analytical methods (e.g., KaplanMeier plots and the log-rank test)  may yield a larger proportion of true ï¬ndings than ï¬elds where analytical methods are still under experimentation (e.g., artiï¬cial intelligence methods) and only â??bestâ? 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 . Simply abolishing selective publication would not make this problem go away. Corollary 5: The greater the ï¬nancial and other interests and prejudices in a scientiï¬c ï¬eld, the less likely the research ï¬ndings are to be true. Conï¬?icts of interest and prejudice may increase bias, u. Conï¬?icts of interest are very common in biomedical research , and typically they are inadequately and sparsely reported [26,27]. Prejudice may not necessarily have ï¬nancial roots. Scientists in a given ï¬eld may be prejudiced purely because of their belief in a scientiï¬c theory or commitment to their own ï¬ndings. Many otherwise seemingly independent, university-based studies may be conducted for no other reason than to give physicians and researchers qualiï¬cations for promotion or tenure. Such nonï¬nancial conï¬?icts may also lead to distorted reported results and interpretations. Prestigious investigators may suppress via the peer review process the appearance and dissemination of ï¬ndings that refute their ï¬ndings, thus condemning their ï¬eld to perpetuate false dogma. Empirical evidence on expert opinion shows that it is extremely unreliable . Corollary 6: The hotter a scientiï¬c ï¬eld (with more scientiï¬c teams involved), the less likely the research ï¬ndings are to be true. Table 3. Research Findings and True Relationships in the Presence of Multiple Studies Research Finding True Relationship Yes No Total Yes No Total cR(1 â?? Î²n)/(R 1) cRÎ²n/(R 1) cR/(R 1) c(1 â?? [1 â?? Î±]n)/(R 1) c(1 â?? Î±)n/(R 1) c/(R 1) c(R 1 â?? [1 â?? Î±]n â?? RÎ²n)/(R 1) c([1 â?? Î±]n RÎ²n)/(R 1) c DOI: 10.1371/journal.pmed.0020124.t003 PLoS Medicine | www.plosmedicine.org 0698 DOI: 10.1371/journal.pmed.0020124.g001 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. This seemingly paradoxical corollary follows because, as stated above, the PPV of isolated ï¬ndings decreases when many teams of investigators are involved in the same ï¬eld. This may explain why we occasionally see major excitement followed rapidly by severe disappointments in ï¬elds that draw wide attention. With many teams working on the same ï¬eld 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 â??positiveâ? results. â??Negativeâ? results may become attractive for dissemination only if some other team has found a â??positiveâ? 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 August 2005 | Volume 2 | Issue 8 | e124 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 = 10â??4, and the pre-study probability for any polymorphism to be associated with schizophrenia is al ... Purchase answer to see full attachment
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