Abstract
In epidemiological studies of rare diseases (i.e., rare types of cancer) researchers often face major difficulty in obtaining enough cases of the disease to make valid comparisons using odds-ratio estimators. Moreover, they may wish to adjust for the influence of certain extraneous factors so that the effect of the variables of interest can be more clearly visible. This is especially so in case-control studies when it is known that the effects of the risk factor are confounded with such variables as age, sex, and individual physical characteristics of the subjects. These confounding variables often make it difficult (or even impossible) to directly compare the exposed and unexposed groups. Typically, to evaluate the effect of the risk factor in these situations within the odds-ratio framework, methods based on data stratification and within-stratum dichotomization are used. The latter is usually accomplished by classifying cases and controls within each stratum as either exposed or unexposed to the risk factor under investigation. Whereas the stratification is often unavoidable, it may not be practical to dichotomize exposure. Instead, one might wish to consider multiple levels of the exposure variable, based on some appropriate ordinal or even continuous scale (cf., e.g., Greenberg and Tamburro [1]).
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References
Greenberg, R. A. and Tamburro, C. H. (1981) Exposure indices for epidemiological surveillance of carcinogenic agents in an industrial chemical environment. J. Occup. Med. 23, 353–358.
van Elteren, P. (1960) On the combination of independent two-sample tests of Wilcoxon. Bull. Int. Statist. Instit. 37, 351–361.
Cuzick, J. (1985) A method for analyzing case-control studies with ordinal exposure variables. Biometrics 41, 609–621.
Cuzick, J., Bulstrode, J. C., Stratton, I., Thomas, B. S., Bulbrook, R. D., and Hayward, J. L. (1983) A prospective study of urinary androgen levels and ovarian cancer. Int. J. Cancer 32, 13–19.
Kwa, H. G., Cleton, F., Wang, D. Y., Bulbrook, R. D., Bulstrode, J. C., Hayward, J. L., et al. (1981) A prospective study of plasma prolactin levels and subsequent risk of breast cancer. Int. J. Cancer 28, 673–676.
Collings, B. J. and Hamilton, M. A. (1988) Estimating the power of the two-sample Wilcoxon test for location shift. Biometrics 44, 847–860.
Rempala, G., Looney, S., Tamburro, C., and Fortwengler, P (1998) Power of the rank test for multi-strata case-control studies. University of Louisville Department of Mathematics Technical Report no. 1/98.
Randles, R. H. and Wolfe, D. A. (1979) Introduction to the Theory of Nonparametric Statistics. Krieger, Malabar, FL.
Katzenbeisser, W. (1985) The distribution of two-sample location exceedance statistic under Lehmann alternatives. Statistische Hefte 26, 131–138.
Katzenbeisser, W. (1989) The exact power of two-sample location tests based on exceedance statistics against shift alternatives. Statistics 20, 47–54.
Hodges, J. L. and Lehmann, E. L. (1963) Estimates of location based on rank tests. Ann. Math. Statist. 34, 598–611.
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© 2002 Humana Press Inc., Totowa, NJ
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Rempala, G.A., Looney, S.W. (2002). Power of the Rank Test for Multi-Strata Case-Control Studies with Ordinal Exposure Variables. In: Looney, S.W. (eds) Biostatistical Methods. Methods in Molecular Biology™, vol 184. Humana Press. https://doi.org/10.1385/1-59259-242-2:191
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DOI: https://doi.org/10.1385/1-59259-242-2:191
Publisher Name: Humana Press
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