John Ioannidis, Tufts University /Stanford Prevention Research Center / University of Ioannina, studies research studies. From the abstract:
“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 […]”
After a theoretical consideration of the limitations of studies and sources for their low probability of being true, he summarizes the problems through six corollaries.
Now, if only someone could explain to me the 2×2 tables…
Selected quotes from the article:
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.
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) than in scientific fields with small studies, such as most research of molecular predictors (sample sizes 100-fold smaller).
Corollary 2: The smaller the effect sizes in a scientific field, the less likely the research findings are to be true.
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.
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.
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.
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.
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.
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.
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.
Corollary 6: The hotter a scientific field (with more scientific teams involved), the less likely the research findings are to be true.
Thus, each team may prioritize on pursuing and disseminating its most impressive “positive” results. “Negative” results may become attractive for dissemination 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 to describe this phenomenon of rapidly alternating extreme research claims and extremely opposite refutations. Empirical evidence suggests that this sequence of extreme opposites is very common in molecular genetics.
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.
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.
Too large and too highly significant effects may actually be more likely to be signs of large bias in most fields of modern research.
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.”