By Michael S. Kramer
Here is a booklet for clinicians, medical investigators, trainees, and graduates who desire to enhance their talent within the making plans, execution, and interpretation of scientific and epidemiological learn. Emphasis is put on the layout and research of analysis reviews regarding human matters the place the first curiosity issues ideas of analytic (cause-and- impression) inference. the subject is gifted from the point of view of the clinician and assumes no earlier wisdom of epidemiology, examine layout or facts. large use is made from illustrative examples from quite a few scientific specialties and subspecialties. The publication is split into 3 components. half I bargains with epidemiological learn layout and analytic inference, together with such matters as dimension, premiums, analytic bias, and the most varieties of observational and experimental epidemiological experiences. half II offers the rules and functions of biostatistics, with emphasis on statistical inference. half III contains 4 chapters overlaying such issues as diagnostic assessments, determination research, survival (life-table) research, and causality.
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Additional info for Clinical Epidemiology and Biostatistics: A Primer for Clinical Investigators and Decision-Makers
Rates may be specified by age, sex, race/ethnic origin, or any other attribute of interest. The rate of TB skin-test positivity among white men 20-34 years of age is an example of a race-, sex-, and age-specific rate. 2. All "members" of the denominator group should be eligible to have the attribute or to experience the event counted in the numerator. In constructing uterine cancer rates, for example, women with prior hysterectomies and men should be removed from the denominator. ) Occasionally, the sources of data for the numerator and denominator are different, and the requirements for constructing a rate are violated.
Rate or mean). Furthermore, the larger the size of the study sample, the more reproducible the sample estimate will be. Although assessing the reproducibility of a descriptor entails statistical inference (to be discussed further in Chapter 10), no causal inference is involved in descriptive studies. In an analytic study, one or more groups are studied for the express purpose of drawing inferences about the association between two or more variables, particularly about a cause-and-effect association.
For example, a group's adoption of a certain exercise or diet regimen may require many years before resulting in any subsequent reduction in cardiovascular mortality. If most individuals are followed up for only a year or two after beginning the diet, no beneficial effect may be seen, even if tens of thousands of individuals participate in the study. In other words, a large number of individuals followed up for a short period of time will lead to an underestimate of the true incidence. Adjustment of incidence for differential durations of follow-up is accomplished by means of life-table techniques.
Clinical Epidemiology and Biostatistics: A Primer for Clinical Investigators and Decision-Makers by Michael S. Kramer