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

By S. Nassir Ghaemi

Obtainable and clinically proper, A Clinician's advisor to stats and Epidemiology in psychological overall healthiness describes statistical techniques in undeniable English with minimum mathematical content material, making it ideal for the busy health care professional. utilizing transparent language in favour of advanced terminology, obstacles of statistical suggestions are emphasised, in addition to the significance of interpretation - in place of 'number-crunching' - in research. Uniquely for a textual content of this sort, there's vast insurance of causation and the conceptual, philosophical and political components concerned, with forthright dialogue of the pharmaceutical industry's position in psychiatric study. by means of making a higher realizing of the area of study, this booklet empowers future health execs to make their very own judgments on which facts to think - and why.

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

Obtainable and clinically correct, A Clinician's advisor to stats and Epidemiology in psychological wellbeing and fitness describes statistical options in undeniable English with minimum mathematical content material, making it excellent for the busy health care provider. utilizing transparent language in favour of advanced terminology, barriers of statistical recommendations are emphasised, in addition to the significance of interpretation - in preference to 'number-crunching' - in research.

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

Sample text

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. 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. 1. 1 Effect modification. Effect modifier When a variable affects the relationship between exposure and outcome, then a conceptual assessment needs to be made about whether the third variable directly causes the outcome but is not caused by the exposure (then it is a confounding factor), or whether the third variable does not cause the exposure and seems to modify the exposure’s effects (then it is an effect modifier).

Suppose 25% of one group in our sample had a history of hospitalization for the illness being studied (and thus could be seen as more severely ill than those without past hospitalization); if the other group had a 31% rate of past hospitalization, the difference between the two groups is 6%, and we would be concerned about a difference between the groups of even 3% (10% of the absolute rate, which is 25% in one group and 31% in another group, or around 30% overall), and thus we definitely would be concerned about the observed 6% difference between the groups in past hospitalization (31% – 25% = 6%).

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