Download A clinician's guide to statistics and epidemiology in mental by S. Nassir Ghaemi PDF

By S. Nassir Ghaemi

Available and clinically appropriate, A Clinician's consultant to stats and Epidemiology in psychological well-being describes statistical innovations in undeniable English with minimum mathematical content material, making it excellent for the busy physician. utilizing transparent language in favour of advanced terminology, obstacles of statistical options are emphasised, in addition to the significance of interpretation - rather than 'number-crunching' - in research. Uniquely for a textual content of this sort, there's huge insurance of causation and the conceptual, philosophical and political components concerned, with forthright dialogue of the pharmaceutical industry's position in psychiatric learn. by means of making a larger figuring out of the realm of study, this booklet empowers wellbeing and fitness execs to make their very own judgments on which facts to think - and why.

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Extra resources for A clinician's guide to statistics and epidemiology in mental health : measuring truth and uncertainty

Example text

If a patient had a history of antidepressant-induced mania that was common or severe, some clinicians might not continue the antidepressant. Perhaps if the patient had bipolar disorder type I, some clinicians would be less likely to continue antidepressants than if the patient had bipolar disorder type II. These are issues of selection bias, or so called confounding by indication: the doctor decides what to do non-randomly. Another way to frame the issue is this: we don’t know how many patients did worse because they were taken off antidepressants versus how many were taken off because they were doing worse.

Another way of putting this is that in confounding bias, the confounding factor causes the outcome; the exposure does not cause the outcome at all. The confounding factor is not on the causal pathway of an exposure and outcome. In other words, it is not the case that the exposure causes the outcome through the mediation of the confounding factor; the confounding factor is not merely a mechanism whereby the exposure causes the outcome. To repeat a classic example, numerous epidemiological studies find an association between coffee drinking and cancer, but this is due to the confounding effect of cigarette smoking: more coffee drinkers smoke cigarettes, and it is the cigarettes, completely and entirely, that cause the cancer; coffee itself has not increased cancer risk.

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).

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