Thanks for the reply. Your suggestion highlights the issue I have with my original analysis. Using your example code, I get an intercept of -135.67 and a fixed effect estimate for height of 3.77 (suggesting a regression equation of weight = 3.77*height - 135.67). However, when I reorder and re-run the same analysis using the following: data class; call streaminit(1); set sashelp.class; clinic = rand("Table", 0.3, 0.3, 0.4); run; data class2; set class;
if clinic = 1 then clinic2 = 'one';
else if clinic = 2 then clinic2 = 'two';
else if clinic = 3 then clinic2 = 'three';
proc mixed data = class2;
class clinic2;
model weight = height | clinic2 / solution;
random clinic2;
run; I get an intercept of -144.54 and a slope estimate of 3.91 (weight = 3.91*height - 144.54). The fixed effect estimates in the solution option (in PROC MIXED or PROC GLM) provides estimates for the reference subject only (which is the last one calculated), so simply re-ordering the data can vastly change the result. In this example, how do I determine the global effect of weight on height?
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