Related article: than in scientific fields where postulated effects are small, such as
genetic risk factors for multigenetic diseases (relative risks
1.1-1.5) [[52]7]. Modern epidemiology is increasingly obliged to
target smaller effect sizes [[53]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 Atenolol 50 Mg Price 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
[[54]4,[55]8,[56]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 [[57]18-20] or meta-analyses
[[58]21,[59]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)
[[60]23]. Similarly, fields that use commonly agreed, stereotyped
analytical methods (e.g., Kaplan-Meier plots and the log-rank test)
[[61]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 Atenolol 150 Mg 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
[[62]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 Atenolol 200 Mg be true. Conflicts of interest and prejudice may
increase bias, u. Conflicts of interest are very common in biomedical
research [[63]26], and typically they are inadequately and sparsely
reported [[64]26,[65]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 [[66]28].
Corollary 6: The hotter a scientific field (with more scientific teams
involved), the less likely the research findings are to be true. This
seemingly paradoxical corollary follows because, as stated above, the
PPV of isolated findings decreases when many teams of investigators
are involved in the same field. This may explain why we occasionally
see major excitement followed rapidly by severe disappointments in
fields that draw wide attention. With many teams working on the same
field and with massive experimental data being produced, timing is of
the essence in beating competition. Thus, each team may prioritize on