Literature DB >> 10456376

Biological interpretation of relative risk.

S F Lanes1.   

Abstract

There is widespread interest in assessing the clinical importance of a study result. This goal is impeded, however, by a lack of clarity about the biological interpretability of epidemiological effect measures, such as the relative risk. A relative risk is often interpreted merely as a measure of some vague statistical association, without a view toward a biological effect as an object of measurement. Not infrequently, if it is not statistically significant, the relative risk estimate is ignored completely. A key to biological interpretation is appreciating the theoretical framework stipulating that outcome rates derived from 2 comparison groups actually represent measures of different effects in the same population. For instance, by using a placebo group to estimate the number of background cases that occurred in the treatment group, an estimate of the number of excess cases that occurred as a result of treatment can be made. This kind of biological entity can be derived from a relative risk, and can be more easily evaluated as to its clinical importance than a statistical association or a statement about statistical significance. Interpretation then becomes a more directed task, with a focus on the validity of certain ancillary hypotheses upon which biological interpretability rests.

Mesh:

Year:  1999        PMID: 10456376     DOI: 10.2165/00002018-199921020-00001

Source DB:  PubMed          Journal:  Drug Saf        ISSN: 0114-5916            Impact factor:   5.606


  10 in total

1.  Practical implications of modes of statistical inference for causal effects and the critical role of the assignment mechanism.

Authors:  D B Rubin
Journal:  Biometrics       Date:  1991-12       Impact factor: 2.571

2.  The interpretation of epidemiologic studies.

Authors:  M Angell
Journal:  N Engl J Med       Date:  1990-09-20       Impact factor: 91.245

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Authors:  K J Rothman
Journal:  Am J Epidemiol       Date:  1976-12       Impact factor: 4.897

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Authors:  M J Gardner; D G Altman
Journal:  Br Med J (Clin Res Ed)       Date:  1986-03-15

5.  Beyond the confidence interval.

Authors:  C Poole
Journal:  Am J Public Health       Date:  1987-02       Impact factor: 9.308

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Authors:  K J Rothman
Journal:  Ann Intern Med       Date:  1986-09       Impact factor: 25.391

7.  Confidence intervals for reporting results of clinical trials.

Authors:  R Simon
Journal:  Ann Intern Med       Date:  1986-09       Impact factor: 25.391

8.  A show of confidence.

Authors:  K J Rothman
Journal:  N Engl J Med       Date:  1978-12-14       Impact factor: 91.245

Review 9.  Is statistical significance testing useful in interpreting data?

Authors:  D A Savitz
Journal:  Reprod Toxicol       Date:  1993       Impact factor: 3.143

10.  The case for confidence intervals in controlled clinical trials.

Authors:  M Borenstein
Journal:  Control Clin Trials       Date:  1994-10
  10 in total

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