By Michael S. Kramer

ISBN-10: 3642613721

ISBN-13: 9783642613722

ISBN-10: 3642648142

ISBN-13: 9783642648144

Here is a booklet for clinicians, scientific investigators, trainees, and graduates who desire to boost their skillability within the making plans, execution, and interpretation of medical and epidemiological learn. Emphasis is put on the layout and research of study experiences related to human matters the place the first curiosity matters ideas of analytic (cause-and- influence) inference. the subject is gifted from the perspective of the clinician and assumes no earlier wisdom of epidemiology, study layout or facts. wide use is made up of illustrative examples from a number of scientific specialties and subspecialties. The publication is split into 3 elements. half I offers with epidemiological study layout and analytic inference, together with such matters as size, premiums, analytic bias, and the most types of observational and experimental epidemiological stories. half II offers the foundations and purposes of biostatistics, with emphasis on statistical inference. half III contains 4 chapters overlaying such subject matters as diagnostic assessments, selection research, survival (life-table) research, and causality.

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Extra resources for Clinical Epidemiology and Biostatistics: A Primer for Clinical Investigators and Decision-Makers

Example text

A rate contains two essential components, a numerator and a denominator. Numerators alone convey little information and may be quite misleading. The number (or count) of individuals sharing some attribute or experiencing some event does not tell us whether that attribute or event is common or rare. Counts convey only anecdotes; rates are required to convey frequency. A report that ten patients with arthritis experienced a serious form of hepatitis (liver inflammation) while taking a certain medication might well alarm an unsophisticated public.

The concept of confounding is fundamental in epidemiology, and I shall have much to say about it throughout this text (especially in Chapter 5). For now, I shall limit the discussion to showing how it may bias a comparison between rates and how so-called crude rates may be stratified or adjusted to reduce or eliminate such bias. 2, which compares the annual death rates in two (hypothetical) small U. S. communities, one a northeastern industrial town (Millville), the other a sun-belt retirement colony (Sunnyvale).

The total number of observed deaths for each community (last row, columns 5 and 8) is then divided by the total number of expected deaths in that community (last row, columns 4 and 7) to obtain a standardized mortality ratio (SMR) for each community. 4. 4 145 .. Observed deaths . 8 per 1000 a Note that the overall death rate in the standard population must be known. It cannot be derived from the stratum-specific death rates without one also knowing the population in each stratum. not required in making this calculation.

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Clinical Epidemiology and Biostatistics: A Primer for Clinical Investigators and Decision-Makers by Michael S. Kramer


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