Most published research false

It has been said that only 1% of articles in scientific journals are scientifically sound [2]. Whatever the exact percentage, a paper from Greece [3], replete with Greek mathematical symbols and philosophy, makes a number of important points which are useful to think of as a series of little laws (some of which we explore more fully later) to use when considering evidence.

• The smaller the studies conducted in a scientific field, the less likely the research findings are to be true.

• The smaller the effect sizes in a scientific field, the less likely the research findings are to be true.

• The greater the number and the fewer the selection of tested relationships in a scientific field, the less likely the research findings are to be true.

• 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.

• The greater the financial and other interests and prejudices in a scientific field, the less likely the research findings are to be true. (These might include research grants or the promise of future research grants.)

• The hotter a scientific field (the more scientific teams involved), the less likely the research findings are to be true.

Ioannidis then performs a pile of calculations and simulations and demonstrates the likelihood of us getting at the truth from different typical study types (Table 1.1). This ranges from odds of 2:1 on (67% likely to be true) from a systematic review of good-quality randomized trials, through 1:3 against (25%

likely to be true) from a systematic review of small inconclusive randomized trials, to even lower levels for other study architectures.

There are many traps and pitfalls to negotiate when assessing evidence, and it is all too easy to be misled by an apparently perfect study that later turns out to be wrong or by a meta-analysis with impeccable credentials that seems to be trying to pull the wool over our eyes. Often, early outstanding results are followed by others that are less impressive. It is almost as if there is a law that states that first results are always spectacular and subsequent ones are mediocre: the law of initial results. It now seems that there may be some truth in this.

Three major general medical journals (New England Journal of Medicine, JAMA, and Lancet) were searched for studies with more than 1000 citations published between 1990 and 2003 [4]. This is an extraordinarily high number of citations when you think that most papers are cited once if at all, and that a citation of more than a few hundred times is almost as rare as hens' teeth.

Of the 115 articles published, 49 were eligible for the study because they were reports of original clinical research (like tamoxifen for breast cancer prevention or stent versus balloon angioplasty). Studies had sample sizes as low as nine and as high as 87,000. There were two case series, four cohort studies, and 43 randomized trials. The randomized trials were very varied in size, though, from 146 to 29,133 subjects (median 1817). Fourteen of the 43 randomized trials (33%) had fewer than 1000 patients and 25 (58%) had fewer than 2500 patients.

Of the 49 studies, seven were contradicted by later research. These seven contradicted studies included

Table 1.1 Likelihood of truth of research findings from various

typical study architectures

Example

Ratio of true to not true

Confirmatory meta-analysis of good-quality RCTs

2:1

Adequately powered RCT with little bias and 1:1 prestudy odds

1:1

Meta-analysis of small, inconclusive studies

1:3

Underpowered and poorly performed phase I-II RCT

1:5

Underpowered but well-performed phase I-II RCT

1:5

Adequately powered exploratory epidemiologic study

1:10

Underpowered exploratory epidemiologic study

1:10

Discovery-orientated exploratory research with massive testing

1:1000

one case series with nine patients, three cohort studies with 40,000-80,000 patients, and three randomized trials, with 200, 875 and 2002 patients respectively. So only three of 43 randomized trials were contradicted (7%), compared with half the case series and three-quarters of the cohort studies.

A further seven studies found effects stronger than subsequent research. One of these was a cohort study with 800 patients. The other six were randomized trials, four with fewer than 1000 patients and two with about 1500 patients.

Most of the observational studies had been contradicted, or subsequent research had shown substantially smaller effects, but most randomized studies had results that had not been challenged. Of the nine randomized trials that were challenged, six had fewer than 1000 patients, and all had fewer than 2003 patients. Of 23 randomized trials with 2002 patients or fewer, nine were contradicted or challenged. None of the 20 randomized studies with more than 2003 patients were challenged.

There is much more in these fascinating papers, but it is more detailed and more complex without becoming necessarily much easier to understand. There is nothing that contradicts what we already know, namely that if we accept evidence of poor quality, without validity or where there are few events or numbers of patients, we are likely, often highly likely, to be misled.

If we concentrate on evidence of high quality, which is valid, and with large numbers, that will hardly ever happen. As Ioannidis also comments, if instead of chasing some ephemeral statistical significance we concentrate our efforts where there is good prior evidence, our chances of getting the true result are better. This may be why clinical trials on pharmaceuticals are so often significant statistically, and in the direction of supporting a drug. Yet even in that very special circumstance, where so much treasure is expended, years of work with positive results can come to naught when the big trials are done and do not produce the expected answer.

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