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Abstract
In epidemiological and occupational health studies it is not uncommon to find that the distribution of exposure to a quantitative risk factor is highly skewed, as, for example, when most of a population suffers little or no exposure to a hazardous agent while a small subset receives very high exposures. As a result the asymptotic significance levels of conditional tests for monotone trends in rates or proportions can be profoundly anticonservative when applied to small or even moderate numbers of events. Monte Carlo (MC) estimation of observed levels of significance (”p-values”) provides a useful method for accurately assessing statistical significance in such situations. We describe a simple technique of importance sampling (IS) which can greatly improve the efficiency of MC estimation in this setting. Use of the IS technique is illustrated with data regarding cancer mortality among atomic bomb survivors.
†(After 15 December 1985: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1124 Columbia Street, Seattle, WA 98104).
†(After 15 December 1985: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1124 Columbia Street, Seattle, WA 98104).
Notes
†(After 15 December 1985: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1124 Columbia Street, Seattle, WA 98104).