https://communities.sas.com/t5/SAS-Programming/Getting-coefficient-of-variation-and-the-power-in-the-Box-Cox/m-p/461703#M284823 <P>Well, I think I found the answer to my own question:</P><P>&nbsp;</P><P><SPAN>LMS values (measures of skew, the median, and the standard deviation)&nbsp;were computed from the interpolated cubic splines at weekly intervals. Cole’s procedures&nbsp;and an iterative least squares method were used to derive the LMS parameters (L = Box-Cox power, M = median, S = coefficient of variation) from the multicentre meta-analyses for weight, head circumference and length. The LMS splines were smoothed slightly while maintaining data integrity as noted above.</SPAN></P><P>&nbsp;</P><P><SPAN>Cole TJ, Green PJ: Smoothing reference centile curves: the LMS method and penalized likelihood. Stat Med. 1992, 11: 1305-1319. 10.1002/sim.4780111005.&nbsp;</SPAN></P><P>&nbsp;</P><P>&nbsp;</P> Fri, 11 May 2018 20:46:20 GMT rogersaj 2018-05-11T20:46:20Z https://communities.sas.com/t5/SAS-Programming/Getting-coefficient-of-variation-and-the-power-in-the-Box-Cox/m-p/461636#M284822 <P>This is slightly more in the realm of math questions than SAS, but maybe someone can help me figure out how to use SAS to compute it:</P><P>&nbsp;</P><P>The CDC publishes US infant growth charts for weight and length -- complete with percentile, z-scores, and "<SPAN>LMS parameters: the median (M), the generalized coefficient of variation (S), and the power in the Box-Cox transformation (L)" to allow us to calculate our own percentiles from weight and length. The chart starts at birth.</SPAN></P><P><SPAN><span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="CDC.PNG" style="width: 600px;"><img src="https://communities.sas.com/t5/image/serverpage/image-id/20469i0D0586A9F1F9EB3C/image-size/large?v=v2&amp;px=999" role="button" title="CDC.PNG" alt="CDC.PNG" /></span></SPAN></P><P>&nbsp;</P><P><SPAN>However, I want these percentiles from BEFORE birth. The WHO just published these values for expected fetal weight at different gestational ages.&nbsp;</SPAN></P><P><span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="WHO_EFW_female.PNG" style="width: 532px;"><img src="https://communities.sas.com/t5/image/serverpage/image-id/20468iDEDF994A1080ED39/image-size/large?v=v2&amp;px=999" role="button" title="WHO_EFW_female.PNG" alt="WHO_EFW_female.PNG" /></span></P><P><BR />These percentiles (5th, 10th, 25th, etc.) aren't good enough for me. I need a much more granular percentile -- i.e. I need the L, M, and S values so that I can calculate my own percentile from birthweight data.</P><P><BR />Any idea how to get those LMS values from the above table? The authors don't publish them. Do I need to contact them for raw data? That might take forever...</P><P>&nbsp;</P><P>Thanks SO much!</P> Fri, 11 May 2018 17:45:07 GMT https://communities.sas.com/t5/SAS-Programming/Getting-coefficient-of-variation-and-the-power-in-the-Box-Cox/m-p/461636#M284822 rogersaj 2018-05-11T17:45:07Z https://communities.sas.com/t5/SAS-Programming/Getting-coefficient-of-variation-and-the-power-in-the-Box-Cox/m-p/461703#M284823 <P>Well, I think I found the answer to my own question:</P><P>&nbsp;</P><P><SPAN>LMS values (measures of skew, the median, and the standard deviation)&nbsp;were computed from the interpolated cubic splines at weekly intervals. Cole’s procedures&nbsp;and an iterative least squares method were used to derive the LMS parameters (L = Box-Cox power, M = median, S = coefficient of variation) from the multicentre meta-analyses for weight, head circumference and length. The LMS splines were smoothed slightly while maintaining data integrity as noted above.</SPAN></P><P>&nbsp;</P><P><SPAN>Cole TJ, Green PJ: Smoothing reference centile curves: the LMS method and penalized likelihood. Stat Med. 1992, 11: 1305-1319. 10.1002/sim.4780111005.&nbsp;</SPAN></P><P>&nbsp;</P><P>&nbsp;</P> Fri, 11 May 2018 20:46:20 GMT https://communities.sas.com/t5/SAS-Programming/Getting-coefficient-of-variation-and-the-power-in-the-Box-Cox/m-p/461703#M284823 rogersaj 2018-05-11T20:46:20Z