It is clear that sampling a person for a serum value in one shot will not give you a correct account of the population's serum distribution> You will not be able to say with the slightest certainty that the prevalence of hyper-serum is %x.
Some have developed strategies for adjusting for intra-indvidiual difference, for instance, - Woteki CE. 1991. The importance of within-person variability in estimating prevalence. in Monitoring Dietary Intakes, I. Macdonald, ed. Springer-Verlag, New York, pp. 99-109. 38. cites a method as: Adjusted value= MeanX+(Xi-MeanX)(Sb/Sobs) My own belief is that you *can* use single-shot serum values to predict disease risk: but you have to use group these with a value that has areliable distribution: ex. group serum cholesterol by subject's bodyweight, which does not vary appreciably among individuals. For instance divide your subjects into those with high bodyweight and low bodyweight and use t-test to compare means of cholesterol levels. Although the distribution curve for cholesterol will be off, the mean will be accurate. What you cannot do is compare one-shot cholesterol to one-shot dietary factors. Is there any criticisms to the above: the t-test should be sufficient to compare to groups and tell you exactly what the probability of no-difference due to intraindividual difference is. Also, would you need to take into account power and sample size? Any info, references (journals or books) appreciated. I have done a bit of investigating, the best article i have found is "Statistical methods to assess and minimize the role of intra-individual ariability" J Chron Dis ol 31, p 399-418 Which directly addresses this issue of group means. I am looking for more current info. . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
