A Clinician's Guide to Statistics and Epidemiology in Mental by S. Nassir Ghaemi

By S. Nassir Ghaemi

Available and clinically suitable, A Clinician's consultant to statistical data and Epidemiology in psychological well-being describes statistical strategies in simple English with minimum mathematical content material, making it ideal for the busy health care provider. utilizing transparent language in favour of complicated terminology, obstacles 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 elements concerned, with forthright dialogue of the pharmaceutical industry's position in psychiatric learn. by means of making a better figuring out of the area of study, this e-book empowers healthiness execs to make their very own judgments on which data 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

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Rating scales for mania are not used usually in clinical practice), and then we recorded those assessments poorly (the charts might be messy, with brief notes rather than extensive descriptions). With such material, it is likely that at least mild hypomanic or manic episodes would be missed and reported as not existing. The extent of such misclassification bias can be hard to determine. 20 Chapter 5 Randomization Experimental observations can be seen as experience carefully planned in advance.

Antidepressant efficacy). Such a confounding effect may be big enough to completely swamp, or at least lessen the difference on the experimental variable such that a previously statistically significant (but small to moderate in effect size) result is no longer statistically significant. How large can such confounding effects be? The general rule of 10% or larger, irrespective of statistical significance, seems to hold (see Chapter 9). The major concern is not whether there is a statistically significant difference in a potential confounder, but rather whether there is a difference big enough to cause concern that our primary results may be distorted.

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.

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