An interesting application! I used BMI with NHANES to scare my
students in my survey stats class :)).
In this situation, I would trust -nlcom- results better. I am not that
sure that -summarize- uses the proper weight algebra, and the
differences are of course greater if you have drastically different
weights (and NHANES, to my knowledge, is known for some rather wild
design specification and weight differentials).
So have you tried that LMS method? Being-a-good-guy rules of Statalist
suggest that you give full references. I have heard of the method, but
don't know the details. If I were doing this, I would try some smooth
functions of age to model means, variances and asymmetry -- I like
B-splines, and other people like fractional polynomials better (which
might actually show the desired behavior -- rapid growth through
childhood and adolescence, slight decay in height with age, and they
say in the US you gain weight via Halloween, Thanksgiving and
Christmas celebrations that you never lose through the rest of the
year :)).
On 11/4/08, Richard Palmer-Jones <[email protected]> wrote:
> Thanks for all your contributions.
>
> I worked out the missing "^3" last night (and the x^3 = (_b[ht])^3 -
> good old Yule and Kendal) i.e.:
>
> nlcom ((_b[ht])^3 - 3*_b[ht2] * _b[ht] + 2*(_b[ht])^3)/(_b[ht2] -
> _b[ht] * _b[ht])^3/2
>
> but I am not convinced it gives sensible results - but then how to judge?
>
> In this dataset (NHANES3) using summarize with weights shows heights
> are not greatly skewed at any age; but weights are clearly negatively
> skewed up to the age of 5, and positively skewed thereafter (ditto for
> BMI). The nlcom calculation is quite close to the estimated skewness
> for height but for weight, although Pearson r = .5, the absolute
> sizes are not that close (skewness = 0.51 * nlcom - 0.079, r2 = .29,
> N=49, both coefs p< .000). The nlcom estimate seriously underestimates
> skewness after age 5 compared to the summarize estimate (with
> weights).
>
> ?
>
> I actually want to compare adult heights, weights, and BMIs in a
> situation where nutritional status has apparently been improving quite
> rapidly. Heights, weights & BMIs for 25 year olds are greater than
> those of 45 year olds (assuming no differntial mortalities, which I
> doubt). Most programs which compute anthopometry z-scores (zanthro, or
> WHO's Anthro macros) are for children or adolescents, so I wanted
> something like a zanthro for adults. One might set the standards using
> USA or UK heatlh surveys which give heights and weights of adults, but
> then one might want to compute skewness both to test for normality
> (they are not) and to use the LMS method (Cole et al. 2008) to develop
> the standards. Height at each age group for both sexes may not be
> normal, but as noted above weights are generally not (different tests
> give different results, but omninorm suggests weight is seriously not
> normal, and height slightly (p between 0.05 & 0.01) not).
>
> I suspect that pro temp I am better off using summarize, and smoothing
> the skewness estimates (and median and cv, but any further advice
> welcomed.
--
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.
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