By S. Nassir Ghaemi
Available and clinically appropriate, A Clinician's advisor to statistical data and Epidemiology in psychological overall healthiness describes statistical options in undeniable English with minimum mathematical content material, making it ideal for the busy physician. utilizing transparent language in favour of advanced terminology, barriers of statistical concepts are emphasised, in addition to the significance of interpretation - in preference to 'number-crunching' - in research. Uniquely for a textual content of this sort, there's large insurance of causation and the conceptual, philosophical and political components concerned, with forthright dialogue of the pharmaceutical industry's position in psychiatric learn. via making a better figuring out of the area of analysis, this ebook empowers future health execs to make their very own judgments on which data to think - and why.
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Additional info for A Clinician's Guide to Statistics and Epidemiology in Mental Health: Measuring Truth and Uncertainty
The numbers alone cannot tell this story; the researcher needs to think about the illness. Recall classic examples from medical epidemiology, repeated here from Chapter 4 so that this distinction is clear. Here is an example of effect modification: cigarette smoking frequently causes blood clots in women on birth control pills. Being female itself is not a cause of blood clots; nor do oral contraceptives themselves have a large risk; but those two variables (gender and oral contraceptive status) together increase this risk of cigarette smoking greatly.
This is confounding bias. Let us suppose that the risk of cancer is higher in women smokers than in men smokers; this is no longer confounding bias, but EM. There is some interaction between gender and cigarette smoking, such that women are more prone biologically to the harmful effects of cigarettes (this is a hypothetical example). But we have no reason to believe that being female per se leads to cancer, as opposed to being male. Gender itself does not cause cancer; it is not a confounding factor; it merely modifies the risk of cancer with the exposure, cigarette smoking.
The difference is really conceptual. In confounding bias, the exposure really has no relation to the outcome at all; it is only through the confounding factor that any relation exists. 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.