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Randomization generate, create qr code jis x 0510 none for .net projects Web app Experimental observati QRCode for .NET ons can be seen as experience carefully planned in advance. Ronald Fisher (Fisher, 1971 [1935]; p.

8). The most effective way to solve the problem of confounding is by the study design method of randomization. This is simply stated, but I would venture to say that this simple statement is the most revolutionary and profound discovery of modern medicine. I would include all the rest of medicine s discoveries in the past century penicillin, heart transplants, kidney transplants, immunosuppression, gene therapies, all of it and I would say that all of these specific discoveries are less important than the general idea, the revolutionary idea, of randomization, and this is so because without randomization, most of the rest of medicine s discoveries would not have been discovered: it is the power of randomization that allows us, usually, to differentiate the true from the false, a real breakthrough from a false claim.

. Counting I previously mentioned that medical statistics was founded on the groundbreaking study of Pierre Louis, in Paris of the 1840s, when he counted about 70 patients and showed that those with pneumonia who received bleeding died sooner than those who did not. Some basic facts such as the fallacy of bleeding, or the benefits of penicillin can be established easily enough by just counting some patients. But most medical effects are not as huge as the harm of bleeding or the efficacy of penicillin.

We call those large effect sizes : with just 70 patients one can easily show the benefit or the harm. Most medical effects, though, are smaller: they are medium or small effect sizes, and thus they can get lost in the noise of confounding bias. Other factors in the world can either obscure those real effects, or make them appear to be present when they are not.

How can we separate real effects from the noise of confounding bias This is the question that randomization answers.. The first RCT: the Kuala Lumpur insane asylum study A historical pause may be useful here. Ronald Fisher is usually credited with originating the concept of randomization. Fisher did so in the setting of agricultural studies in the 1920s: certain fields randomly received a certain kind of seed, others fields received other seeds.

A. Bradford Hill is credited with adapting the concept to the first human randomized clinical trial (RCT), a study of streptomycin for pneumonia in 1948. Multiple RCTs in other conditions followed right away in the 1950s, the first in psychiatry involving lithium in 1952 and the antipsychotic chlorpromazine in 1954.

This is the standard history, and it is correct in the sense that Fisher and Hill were clearly the first to formally develop the concept. Section 2: Bias of randomization and t o recognize its conceptual importance for statistics and science. But there is a hidden history, one that is directly relevant to the mental health professions. As a historical matter, the first application of randomization in any scientific study appears to have been published by the American philosopher and physicist Charles Sanders Peirce in the late 1860s (Stigler, 1986).

Peirce did not seem to follow up on his innovation however. Decades passed, and as statistical concepts began to seep into medical consciousness, it seems that the notion of randomization also began to come into being. In 1905, in the main insane asylum of Kuala Lumpur, Malaysia, the physician William Fletcher decided to do an experiment to test his belief that white rice was not, as some claimed, the source of beriberi (Fletcher, 1907).

He chose to do the study in the insane asylum because patients diets and environment could be fully controlled there. He obtained the permission of the government (though not the patients), and lined up all of them, assigning consecutive patients to receive either white or brown rice. For one year, the two groups received identical diets except for the different types of rice.

Fletcher had conducted the first RCT, and it occurred in psychiatric patients, in an assessment of diet (not drug treatment). Further, the result of the RCT refuted, rather than confirmed, the investigator s hypothesis: Fletcher found that beriberi happened in 24/120 (20%) who received white rice, versus only 2/123 (1.6%) who received brown rice.

In the white rice diet group 18/120 (15%) died of beriberi, versus none in the brown rice diet group (Silverman, 1998). Fisher had not invented p-values yet, but if Fletcher had had access to them, he would have seen the chance likelihood of his findings was less than 1 in 1000 (p < 0.0001); as it was, he knew that the difference between 20% and 2% was large enough to matter.

Arguably, Fletcher had stumbled on the most powerful method of modern medical research. Since not all who ate white rice developed beriberi, the absolute effect size was not large enough to make it an obvious connection. But the relative risk (RR) was indeed quite large (applying modern methods, the RR was 12.

3, which is slightly larger than the association of cigarette smoking and lung cancer; the 95% confidence intervals are 3.0 to 50.9, indicating almost total certitude of a threefold or larger effect size).

It took randomization to clear out the noise and let the real effect be seen. At the same time, Fletcher had also discovered the method s premier capacity: its ability to disabuse us of our mistaken clinical observations..

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