Using re-sampling methods in mortality studies.
Traditional methods of computing standardized mortality ratios (SMR) in mortality studies rely upon a number of conventional statistical propositions to estimate confidence intervals for obtained values. Those propositions include a common but arbitrary choice of the confidence level and the assumption that observed number of deaths in the test sample is a purely random quantity. The latter assumption may not be fully justified for a series of periodic "overlapping" studies. We propose a new approach to evaluating the SMR, along with its confidence interval, based on a simple re-sampling technique. The proposed method is most straightforward and requires neither the use of above assumptions nor any rigorous technique, employed by modern re-sampling theory, for selection of a sample set. Instead, we include all possible samples that correspond to the specified time window of the study in the re-sampling analysis. As a result, directly obtained confidence intervals for repeated overlapping studies may be tighter than those yielded by conventional methods. The proposed method is illustrated by evaluating mortality due to a hypothetical risk factor in a life insurance cohort. With this method used, the SMR values can be forecast more precisely than when using the traditional approach. As a result, the appropriate risk assessment would have smaller uncertainties.
Itskovich
Igor
I
Underwriting Standards Department, Northwestern Mutual Life Insurance Company, Milwaukee, Wisconsin, United States of America. igoritskovich@northwesternmutual.com
Roudebush
Brad
B
D000328:Adult; D000368:Aged; D005260:Female; D006801:Humans; D007352:Insurance, Life; D008297:Male; D008875:Middle Aged; D009026:Mortality; D018570:Risk Assessment; D012307:Risk Factors; D012494:Sampling Studies; D015996:Survival Rate; D013997:Time Factors
IARC Sci Publ. 1987;(82):1-406
3329634