Dear all, How are you? I'm writing to ask for help about the large standard errors from my GEE. Is it correct to report the result given the very large standard errors? Your insight is greatly appreciated. Kind regards, KC proc genmod data=long; class ID year scale01; model y=year scale01 scale01*year /type3; repeated subject=ID / type=unstr covb corrw; lsmean scale01*year / cl; run; Year*scale01 Least Squares Means Year scale01 Estimate Standard Error z Value Pr > |z| Alpha Lower Upper 2013 1=Disagree a lot 3151.32 524.47 6.01 <.0001 0.05 2123.4 4179.3 2013 2=Disagree a little 3830.37 518.49 7.39 <.0001 0.05 2814.2 4846.6 2013 3=Neither agree nor disagree 3987.09 645.05 6.18 <.0001 0.05 2722.8 5251.4 2013 4=Agree a little 4791.8 416.07 11.52 <.0001 0.05 3976.3 5607.3 2013 5=Agree a lot 10020 1706.07 5.87 <.0001 0.05 6676 13364 2014 1=Disagree a lot 2750.11 481.45 5.71 <.0001 0.05 1806.5 3693.7 2014 2=Disagree a little 3147.5 469.63 6.7 <.0001 0.05 2227.1 4068 2014 3=Neither agree nor disagree 3515.44 816.01 4.31 <.0001 0.05 1916.1 5114.8 2014 4=Agree a little 4166.6 418.75 9.95 <.0001 0.05 3345.9 4987.3 2014 5=Agree a lot 5754.81 782.82 7.35 <.0001 0.05 4220.5 7289.1 ... View more

Dear all, How are you? I'm very puzzled because I have this SAS data that has a few numeric variables containing alphabets. I have checked that there is no formats attaching to them and no 'option missing='x' being specified in the SAS program. What could possibly go wrong here? Your insight is greatly appreciated. ... View more

Dear all, How are you? Can I please ask what do the parameter estimates of the covariates mean from a generalized estimating equation model with a square root transformed continuous response variable? I have square root transformed my continuous response variable(2 measurements for each individuals from two timepoints)due to non-normality and fitted a GEE with a few categorical demographic covariates,  the "time" and 'group' variable and the interaction term for 'time' and 'group'. But I don't understand my SAS GEE output (Section of 'Analysis of GEE Parameter Estimates'). Your insight is greatly appreciated. Happy Easter. ... View more

Dear all, How are you? I am analyzing my data with only 2 continuous outcome repeated measures from each individuals(total=1320) with a GEE model with unstructured correlation structure. I am not familiar with GEE but as far as I understand, QIC is for comparing the appropriateness of correlation structure and QICu is for variable selection, which the smallest the value the better. Does it make it a significant predictor if the p-value of an effect <0.05 from the 'Score Statistics For Type 3 GEE Analysis' output? In my case, with or without corresponding p-value<0.05 effect doesn't impact the correlation too much. But including the corresponding effect increases the QIC and QICu though not too much. Hence I'm confused about whether to include or exclude such effect. Your insight is greatly appreciated. ... View more

Dear all, How are you? I use the polychoric matrix produced from the polychoric macro(25010 - Create a polychoric correlation or distance matrix) as an input data for proc factor. But I don't understand the first note. Can anyone please enlighten me on how to address this? Thank you very much. %polychor(data=tmp, var=r1 r2 r3 r4 r5 r6 r7 r8, out=chor,type=corr); proc factor data = chor  method = ml rotate = promax outstat = FAstats n=3 heywood residuals msa score; var r1--r8; run; NOTE: N not equal across variables in data set WORK.CHOR. This may not be appropriate. The smallest value will be used. NOTE: 3 factors will be retained by the NFACTOR criterion. NOTE: Convergence criterion satisfied. NOTE: The data set WORK.FASTATS has 56 observations and 10 variables. NOTE: At least one W.D format was too small for the number to be printed. The decimal may be shifted by the "BEST" format. ... View more

Dear all, How are you? I was reading this Example 79.1 Factor Scoring Coefficients from The Score Procedure. How would one interpret the Factor1 and Factor2 in relation to the dependent variable if he/she fits a simple linear regression with Factor1 and Factor2 as independent variables and y as continuous dependent variable for example. As a simple regression interpretation, it means a unit increase in Factor1 will increase by x amount of Factor1 beta coefficient. Do beta coefficients for Factor1 and Factor2 have any unit of measurement? Thank you very much. ... View more