By S. Nassir Ghaemi
Available and clinically suitable, A Clinician's advisor to statistical data and Epidemiology in psychological overall healthiness describes statistical strategies in undeniable English with minimum mathematical content material, making it excellent for the busy healthcare professional. utilizing transparent language in favour of advanced terminology, boundaries of statistical options are emphasised, in addition to the significance of interpretation - instead of 'number-crunching' - in research. Uniquely for a textual content of this sort, there's broad insurance of causation and the conceptual, philosophical and political elements concerned, with forthright dialogue of the pharmaceutical industry's position in psychiatric study. by means of making a better figuring out of the realm of analysis, this ebook empowers well-being execs to make their very own judgments on which data to think - and why.
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Additional resources for A clinician's guide to statistics and epidemiology in mental health : measuring truth and uncertainty
The disadvantage is that one can really only look at one confounder at a time. Stratification is a markedly underused method of addressing confounding bias (Rothman and Greenland, 1998). , smoking) are the same, then that factor cannot confound one’s results. 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).
We call those “large effect sizes”: with just 70 patients one can easily show the benefit or the harm. Most medical effects, though, are smaller: they are medium or small effect sizes, and thus they can get lost in the “noise” of confounding bias. Other factors in the world can either obscure those real effects, or make them appear to be present when they are not. How can we separate real effects from the noise of confounding bias? This is the question that randomization answers. The first RCT: the Kuala Lumpur insane asylum study A historical pause may be useful here.
In other words, at face value, the study seems to show that long-term continuation of antidepressants in bipolar disorder appears to lead to better outcomes. This study was published in the American Journal of Psychiatry (AJP) without any further statistical analysis, and this apparent result was discussed frequently at conferences for years subsequent to its publication. But the study does not pass the ﬁrst test of the Three C’s. The ﬁrst question, and one never asked by the peer reviewers of AJP (see Chapter 15 for a discussion of peer review), is whether there might be any confounding bias in this observational study.
A clinician's guide to statistics and epidemiology in mental health : measuring truth and uncertainty by S. Nassir Ghaemi