By S. Nassir Ghaemi
There is a professor of psychiatry available in the market who does a greater activity than Nassir Ghaemi in transmitting his knowledge on to you - yet in 20 years i have never discovered one. i've got learn the authors learn papers for years. As an editor, I grew to become conversant in his booklet "The innovations of Psychiatry" as I thought of the philosophical features of the sector. His writing is often transparent and his pondering regularly brilliant.
In this short quantity on information and epidemiology his old and unique observations and outlines of contemporary recommendations is definitely worth the cost of buy by myself. a superb instance is his bankruptcy on meta-analysis. He reminds the reader why this statistical approach was once invented within the first position and is going directly to talk about major boundaries, major old opinions, and the place the strategy should help. His evaluations are good suggestion in and out a number of short pages he touches on concerns that appear to be infrequently mentioned within the literature. this can be a massive bankruptcy for a doctor to learn in the course of a time whilst an increasing number of meta-analyses are thought of the gospel and prove as entrance web page truths.
He additionally presents a "defense and feedback" of facts dependent drugs. He offers a philosophical context for the dialogue and reminds us of "the cult of the Swan-Ganz catheter". an individual who used to be an intern or resident in extensive care settings within the Nineteen Eighties and early Nineteen Nineties can bear in mind the frequent use of this machine regardless of the shortcoming of proof in randomized scientific trials (RCTs). It grew to become the normal of care regardless of the shortcoming of facts. He can pay homage to Feinstein his unique observations that the facts for evidence-based medication is going past RCTs.
The ultimate chapters are concise discussions of information and epidemiology yet they're something yet dry. An instance will be his dialogue of impact estimation and the quantity had to deal with or NNT procedure he describes the calculation and its merits. He is going directly to describe the that means of specific numbers and in addition why the context is necessary. He makes use of a well timed instance of the problem of antidepressants and whether they result in suicidality.
This e-book succeeds as a quantity that could swiftly carry the clinician and researcher in control on most present subject matters in statistics and epidemiology in medication. it's not a e-book that reports mathematical idea. It doesn't supply exhaustive calculations and examples. it truly is written for clinicians. it's a ebook that may supply a foundation for dialogue and seminars during this box for complicated citizens utilizing a number of the author's references or contemporary literature searches to examine particular strategies. it will probably even be built right into a even more accomplished textual content at the topic. Dr. Ghaemi brings a really particular point of view to the subject material and he has produced a truly readable ebook 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
Effect modification again Readers should be reminded that interactions between predictors and other variables do not always reflect confounding effects; sometimes they reflect effect modification. As discussed in Chapter 4, this is where it is useful, even necessary, to be a clinician: to appreciate confounding bias versus effect modification, one needs to understand the condition and variables 32 Chapter 6: Regression being studied. In confounding bias, the confounding variable is itself the causal source of the outcome; in effect modification, the effect modifier is not the causal source of the outcome (the experimental variable causes the outcome, but only through interaction with the effect modifier).
It may not be, in fact, that any of these potential confounders actually inﬂuenced the results of the study. However, the researchers and readers of the literature should think about and examine such possibilities. The authors of such studies usually do so in an initial table of demographic and clinical characterisitics (often referred to as “Table One” because it is needed in practically every clinical study, see Chapter 5). The ﬁrst table should generally be a comparison of clinical and demographic variables in the groups being studied to see if there are any diﬀerences, which then might be confounders.
Hence, they cannot be taken at face value. Even if a Table One showed that some measured variables are equal between groups in a small RCT, unmeasured confounders are still likely that could influence the results. Also, because they are small, such RCTs cannot even be adequately assessed through statistical analyses, such as regression models, to reduce confounding bias (see Chapter 6). Their results simply have to stand on their own, as neither valid nor invalid, and as potentially meaningful, but equally potentially meaningless.