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.
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References
Rubin DB. Neyman (1923) and causal inference in experiments and observational studies [comment]. Stat Sci 1990; 5: 472–80
Lewis D. Causation. J Philos 1973; 70: 566–7
Rothman KJ. Causes. Am J Epidemiol 1976; 104: 587–92
Rubin DB. Bayesian inference for causal effects: the role of randomization. Ann Stat 1978; 6: 34–58
Holland PW. Statistics and causal inference. Am Stat Assoc 1986; 81: 945–60
Greenland S, Robins JM. Identifiability, exchangeability, and epidemiologic confounding. Int J Epidemiol 1986; 15: 412–8
Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol 1974; 66: 688–701
Rothman KJ, Greenland S. Mod Epidemiol, Philadelphia: Lippincott-Raven, 1998: 58–62
Rubin DB. Practical implication of modes of statistical inference for causal effect and the critical role of the assignment mechanism. Biometrics 1991; 47: 1213–34
Popper KR. The logic of scientific discovery. New York: Harper & Roe, 1968. [Originally published as Logik der Forschung. Vienna: Springer, 1934]
Angell M. The interpretation of epidemiologic studies. N Engl J Med 1990; 323: 823–5
Morrison DE, Henkel RE, editors. The significance test controversy. Chicago: Aldine Publishing Company, 1970
Rothman KJ. A show of confidence. N Engl J Med 1978; 299: 1362–3
Salsburg DS. The religion of statistics as practiced in medical journals. Am Stat 1985; 39: 220–3
Rothman KJ. Significance questing. Ann Int Med 1986; 105: 445–7
Simon R. Confidence intervals for reporting results of clinical trials. Ann Intern Med 1986; 105: 429–35
Gardner NJ, Airman DG. Confidence intervals rather than pvalues: estimation rather than hypothesis testing. BMJ 1986; 292: 746–50
Poole C. Beyond the confidence interval. Am J Pub Health 1987; 77: 195–9
Savitz D. Is statistical significance testing useful in interpreting data? Reprod Toxicol 1993; 7: 95–100
Cohen J. The earth is round (p<.05). Am Psychol 1994; 47: 997–1003
Borenstein M. The case for confidence intervals in controlled clinical trials. Control Clin Trials 1994; 15: 411–28
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Lanes, S.F. Biological Interpretation of Relative Risk. Drug-Safety 21, 75–79 (1999). https://doi.org/10.2165/00002018-199921020-00001
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DOI: https://doi.org/10.2165/00002018-199921020-00001