By S. Nassir Ghaemi
Obtainable and clinically suitable, A Clinician's consultant to statistical data and Epidemiology in psychological well-being describes statistical techniques in simple English with minimum mathematical content material, making it excellent 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 wide assurance of causation and the conceptual, philosophical and political components concerned, with forthright dialogue of the pharmaceutical industry's position in psychiatric learn. by means of making a larger knowing of the realm of study, this publication empowers future health pros to make their very own judgments on which facts to think - and why.
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Additional info for A Clinician's Guide to Statistics and Epidemiology in Mental Health: Measuring Truth and Uncertainty
While the straight risk ratio is a probability, the odds ratio is a measure of a fair bet that something will happen. Odds ratios and risk ratios are different, and as probabilities increase for risk ratios, odds ratios increase exponentially (see Chapter 9). The relevance of this discussion is that the relative effect sizes that are obtained in regression models are odds ratios, not risk ratios, and thus we need to remember that huge odds do not represent absolute probabilities of that size. The equation for regression models involves logarithms, and the conversion of logarithms to effect sizes produces odds ratios, not risk ratios.
2006). They gave citalopram by itself (without mood stabilizers) versus placebo to nine patients for 3 months; then those who had received one arm of treatment were switched to the other treatment for 3 months; then they were switched back again to the original treatment for another 3 months. The switching of treatments reﬂects a crossover design, but most relevant for our discussion is that the “randomization” initially involved four patients getting one treatment and ﬁve patients getting another.
Unfortunately not. Assuming that randomization eﬀectively removes most confounding bias (see Chapter 5), the logic of inference only diﬀers between the primary outcome of a properly conducted and analyzed RCT and observational research (like epidemiological studies); but the logic of inference is the same for secondary outcomes and post-hoc analyses of RCTs as it is for observational studies. What is that logic? The logic of the need for constantly being aware of, and seeking to correct for, confounding bias.