statistic

BIAS IN MEDICAL RESEARCH ARTICLE ANALYSIS

Writing Assignment

Following this instruction sheet is a very powerful analysis of how bias and statistical manipulation has

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 up a 1-2 page report answering the following questions:

1. Write an overview or brief summary of the article. Indicate your assessment of what the

article is about and the major findings of the article.

2. What is the author’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 research?

4. 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?

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.

FORMAT: 1-2 pages, 1" margins, double spaced, normal font. All papers must be neatly typed and proofread.

DUE DATE: Last Day of the Final Exam, 11:59 pm

GRADING: This extra credit assignment is worth 10 points. Points will be reduced for spelling, grammar, etc., and for lack of content.

PLoS Medicine | www.plosmedicine.org 697 August 2005 | Volume 2 | Issue 8 | e124

sometimes refuted by subsequent

Open access, freely available online

Essay

Why Most Published Research Findings Are False John P. A. Ioannidis

P

ublished research findings are

factors that influence this problem and

some corollaries thereof.

Modeling the Framework for False Positive Findings

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

It can be proven that most claimed research findings are false.

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.

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 − β (one minus

the Type II error rate). The probability

of claiming a relationship when none

truly exists reflects the Type I error

rate, α. 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 − β)R⁄(R − βR + α). A research finding is thus

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

The Essay section contains opinion pieces on topics

of broad interest to a general medical audience.

relationships that investigators claim

exist, rather than null findings.

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. Let R

be the ratio of the number of “true

relationships” to “no relationships”

Citation: Ioannidis JPA (2005) W hy most published research findings are false. PLoS Med 2(8): e124.

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.

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

Competing Interests: The author has declared that no competing interests exist.

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.

However, here we will target

PLoS Medicine | www.plosmedicine.org 698 August 2005 | Volume 2 | Issue 8 | e124

Table 1. Research Findings and True Relationships same question, claims a statistically significant research finding is easy to Research Finding

True Relationship

Yes No Total estimate. For n independent studies of equal power, the 2 × 2 table is shown in

Yes c(1 − β)R/(R + 1) cα/(R + 1) c(R + α − βR)/(R + 1)

No cβR/(R + 1) c(1 − α)/(R + 1) c(1 − α + βR)/(R + 1)

Total cR/(R + 1) c/(R + 1) c

Table 3: PPV = R(1 − βn)⁄(R + 1 − [1 − α]n − Rβn) (not considering bias). With increasing number of independent

studies, PPV tends to decrease, unless DOI: 10.1371/journal.pmed.0020124.t001

more likely true than false if (1 − β)R > α. Since usually the vast majority of

investigators depend on α = 0.05, this

means that a research finding is more

likely true than false if (1 − β)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 findings being indeed true.

We will try to model these two factors in

the context of similar 2 × 2 tables.

Bias

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 − β]R + uβR)⁄(R + α − βR + u − uα + uβR), and PPV decreases with increasing u, unless 1 − β ≤ α, i.e., 1 − β ≤ 0.05 for most

situations. Thus, with increasing bias,

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.

Testing by Several Independent Teams

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

1 − β < α, i.e., typically 1 − β < 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 βn is replaced by the

product of the terms β i 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 finding is indeed true.

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 − β = 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

the chances that a research finding Table 2. Research Findings and True Relationships in the Presence of Bias

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

Research True Relationship

measurement errors relationships DOI: 10.1371/journal.pmed.0020124.t002

Finding Yes No Total

Yes (c[1 − β]R + ucβR)/(R + 1) cα + uc(1 − α)/(R + 1) c(R + α − βR + u − uα + uβR)/(R + 1)

No (1 − u)cβR/(R + 1) (1 − u)c(1 − α)/(R + 1) c(1 − u)(1 − α + βR)/(R + 1)

Total cR/(R + 1) c/(R + 1) c

PLoS Medicine | www.plosmedicine.org 699 August 2005 | Volume 2 | Issue 8 | e124

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.

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 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 Table 3. Research Findings and True Relationships in the Presence of Multiple Studies become attractive for dissemination Research True Relationship 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

DOI: 10.1371/journal.pmed.0020124.t003 to describe this phenomenon of rapidly

Finding Yes No Total

Yes cR(1 − βn)/(R + 1) c(1 − [1 − α]n)/(R + 1) c(R + 1 − [1 − α]n − Rβn)/(R + 1)

No cRβn/(R + 1) c(1 − α)n/(R + 1) c([1 − α]n + Rβn)/(R + 1)

Total cR/(R + 1) c/(R + 1) c

PLoS Medicine | www.plosmedicine.org 700 August 2005 | Volume 2 | Issue 8 | e124

DOI: 10.1371/journal.pmed.0020124.g002

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.

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

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. Let us also suppose that the study has 60% power to find an

association with an odds ratio of 1.3 at

α = 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,

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

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

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!

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

PLoS Medicine | www.plosmedicine.org 0700 August 2005 | Volume 2 | Issue 8 | e124

standardization of laboratory and

statistical methods, outcomes, and

reporting thereof to minimize bias.

Claimed Research Findings May Often Be Simply Accurate Measures of the Prevailing Bias

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?

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

Table 4. PPV of Research Findings for Various Combinations of Power (1 − β), Ratio of True to Not-True Relationships (R), and Bias (u)

1 − β R u Practical Example PPV

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 0.80 1:1 0.10 Adequately powered RCT with little

bias and 1:1 pre-study odds

0.85 test major concepts rather than narrow,

specific questions. A negative finding 0.95 2:1 0.30 Confirmatory meta-analysis of good- 0.85

quality RCTs can then refute not only a specific proposed claim, but a whole field or

0.80 1:3 0.40 Meta-analysis of small inconclusive

studies

0.20 1:5 0.20 Underpowered, but well-performed

phase I/II RCT

0.20 1:5 0.80 Underpowered, poorly performed

phase I/II RCT

0.80 1:10 0.30 Adequately powered exploratory

research with massive testing

0.20 1:1,000 0.20 As in previous example, but

with more limited bias (more

standardized)

0.41

0.23

0.17

0.20

0.0015

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.

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 The estimated PPVs (positive predictive values) are derived assuming α = 0.05 for a single study.

RCT, randomized controlled trial.

DOI: 10.1371/journal.pmed.0020124.t004

statistically significant findings of

any single team. What matters is the

epidemiological study

0.20 1:10 0.30 Underpowered exploratory

epidemiological study

0.12

0.20 1:1,000 0.80 Discovery-oriented exploratory 0.0010

PLoS Medicine | www.plosmedicine.org 0701 August 2005 | Volume 2 | Issue 8 | e124

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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