The Naked Doctor: an indepth look at the pitfalls of “cutting edge” medicine | Croakey

The Naked Doctor: an indepth look at the pitfalls of “cutting edge” medicine | Croakey: "People don’t understand tiny chances. I have more chance of being dead next Saturday than being both alive and collecting my winnings.

Studies consistently show that both doctors and patients, just like gamblers and stockbrokers, overestimate gains and underestimate losses."

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Doctor Skeptic: Evidence for bias

Doctor Skeptic: Evidence for bias: "My thesis is that the effectiveness of medical interventions is overestimated and that the harms are underestimated; that the perception of medicine is rosier than the reality. The reason for this is multifactorial, but an important contributor to this effect is bias in the scientific record: the 'literature'."

Big pharma mobilising patients in battle over drugs trials data | Business | The Guardian

Big pharma mobilising patients in battle over drugs trials data | Business | The Guardian:
"The pharmaceutical industry has "mobilised" an army of patient groups to lobby against plans to force companies to publish secret documents on drugs trials.

Drugs companies publish only a fraction of their results and keep much of the information to themselves, but regulators want to ban the practice. If companies published all of their clinical trials data, independent scientists could reanalyse their results and check companies' claims about the safety and efficacy of drugs.

Under proposals being thrashed out in Europe, drugs companies would be compelled to release all of their data, including results that show drugs do not work or cause dangerous side-effects."

PLOS Medicine: Why Most Published Research Findings Are False

PLOS Medicine: Why Most Published Research Findings Are False: "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."