By S. Nassir Ghaemi

ISBN-10: 052170958X

ISBN-13: 9780521709583

There's a professor of psychiatry in the market who does a greater activity than Nassir Ghaemi in transmitting his knowledge on to you - yet in 20 years i haven't discovered one. i've got learn the authors learn papers for years. As an editor, I grew to become acquainted with his ebook "The ideas of Psychiatry" as I thought of the philosophical elements of the sector. His writing is often transparent and his pondering constantly brilliant.

In this short quantity on information and epidemiology his ancient and unique observations and outlines of modern techniques is well worth the rate of buy on my own. an excellent instance is his bankruptcy on meta-analysis. He reminds the reader why this statistical procedure was once invented within the first position and is going directly to speak about major boundaries, major historic evaluations, and the place the strategy might actually help. His evaluations are good concept in and out a couple of short pages he touches on concerns that appear to be not often mentioned within the literature. this can be a massive bankruptcy for a doctor to learn in the course of a time while an increasing number of meta-analyses are thought of the gospel and prove as entrance web page truths.

He additionally offers a "defense and feedback" of facts established medication. He offers a philosophical context for the dialogue and reminds us of "the cult of the Swan-Ganz catheter". someone who used to be an intern or resident in extensive care settings within the Nineteen Eighties and early Nineties can bear in mind the frequent use of this gadget regardless of the inability of facts in randomized medical trials (RCTs). It turned the normal of care regardless of the inability of proof. He can pay homage to Feinstein his unique observations that the facts for evidence-based drugs is going past RCTs.

The ultimate chapters are concise discussions of statistics and epidemiology yet they're whatever yet dry. An instance will be his dialogue of influence estimation and the quantity had to deal with or NNT process he describes the calculation and its benefits. He is going directly to describe the that means of specific numbers and likewise why the context is necessary. He makes use of a well timed instance of the difficulty of antidepressants and whether they bring about suicidality.

This booklet succeeds as a quantity which could quickly deliver the clinician and researcher on top of things on most present subject matters in information and epidemiology in medication. it's not a ebook that experiences mathematical conception. It doesn't offer exhaustive calculations and examples. it's written for clinicians. it's a e-book that may offer a foundation for dialogue and seminars during this box for complicated citizens utilizing the various author's references or fresh literature searches to examine particular techniques. it may well even be built right into a even more finished textual content at the topic. Dr. Ghaemi brings a really exact perspective to the subject material and he has produced a really readable publication that I hugely recommend.

George Dawson, MD

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Extra resources for A Clinician’s Guide to Statistics and Epidemiology in Mental Health: Measuring Truth and Uncertainty

Example text

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 to recognize its conceptual importance for statistics and science.

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.

Further, if a study does not contain any, or hardly any, persons with a potential confounding factor, then it cannot be confounded by that factor (this is called “restriction” as opposed to stratification). One of the benefits of stratification, compared to regression, is that one does not need to make certain assumptions about whether the regression model can be applied to the data (see Appendix). The key weakness is that one cannot correct for multiple confounders simultaneously, but at least one can capture major confounders with this simple method.

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