They were simulated so that they have weak correlations among them. ... View more

Hi Paige,   Thank you for your reply.   They are imaginary variables that are normally distributed. Please imagine that P1_3 and P2_3 are office systolic and diastolic blood pressure, and SP1_3 and SP2_3 are home systolic and diastolic blood pressure.    Thanks,   Yasu   Yasu   ... View more

Sorry, there is a typo in the description of repeated statement. I specified the repeated statement as follows:    repeated bp*visit / type=un subject=id R; ... View more

Dear Frankgreen, Thank you for your reply. I almost agree with you, but I don't think that the graph is exact because it is an approximate value. Please submit the example code below. Comparing results, you can see that the estimated value on the graph can be affected by the number of values for the parameter, that is, the precision can differ depending on the option. /*example1*/ proc power;   pairedmeans test=equiv_ratio dist=lognormal   meanratio = 1.10   alpha     = 0.05   cv        = 0.15   corr      = 0   lower     = 0.8   upper     = 1.25   npairs    = 2 to 100 by 1   power     =. ;   plot  min=2 max=100 yopts=(ref=0.8, 0.9 crossref=yes) ; run; /*example2*/ proc power;   pairedmeans test=equiv_ratio dist=lognormal   meanratio = 1.10   alpha     = 0.05   cv        = 0.15   corr      = 0   lower     = 0.8   upper     = 1.25   npairs    = 2 to 100 by 1   power     =. ;   plot  min=2 max=100 yopts=(ref=0.8, 0.9 crossref=yes) npts=500; run; regard, yasu ... View more

Dear Steve, I really appreciate your continued support ! I'll check with tech support about this. Once agan, thank you  for your kind reply. Yasu ... View more

Dear PG, Thank you for your usuful information. I'll contact with him by e-mail. Yasu ... View more

Dear Steve, I really appreciate your continued support. Looking at the log, it seems to occur at the initial iteration. And I looked into the data once again, but there are no such a record. In addition, I have another question. If there are some duplicate record, why does not the infinite likelihood occur with type=UN specificstions? Thanks, Yasu ... View more

Than you for your continued help. Yes, I know the conventional Hosmer-Lomeshow test for binary data can be conducted with lackfit specification in Proc Logistic. But I want to conduct the modified one for survival data. D'Agostino and Nam introduced it and a lot of authers have been refering to this article. So I think SAS macro code can be available or published. Thanks, Yasu  ... View more

Thank you for your reply. I'm sorry for trouble you. Could you refer to the following article? http://www.sciencedirect.com/science/article/pii/S0169716103230017 ... View more

The time points are equally spaced for each subject, so I tried type=AR(1) and type=sp(pow)(time1). But the "infinite likelihood" error message occured with both type specification. I'n not sure what is causing the infinite liklihood error yet... ... View more

Dear Steve, Thank you for your continued sopport. the covariance estimates under the TYPE=UN specification as follows: Estimated R Matrix for SUBJID 0001-001                                                       Row        Col1        Col2        Col3        Col4        Col5                                                         1      331.08      301.34      278.17      269.69      260.96                                                         2      301.34      330.16      302.41      285.32      270.93                                                         3      278.17      302.41      419.88      356.43      283.12                                                         4      269.69      285.32      356.43      473.49      320.51                                                         5      260.96      270.93      283.12      320.51      400.03   Covariance Parameter Estimates                                                                                                                                    Cov Parm    Subject    Estimate                                                                      UN(1,1)     SUBJID       331.08                                                                      UN(2,1)     SUBJID       301.34                                                                      UN(2,2)     SUBJID       330.16                                                                      UN(3,1)     SUBJID       278.17                                                                      UN(3,2)     SUBJID       302.41                                                                      UN(3,3)     SUBJID       419.88                                                                      UN(4,1)     SUBJID       269.69                                                                      UN(4,2)     SUBJID       285.32                                                                      UN(4,3)     SUBJID       356.43                                                                      UN(4,4)     SUBJID       473.49                                                                      UN(5,1)     SUBJID       260.96                                                                      UN(5,2)     SUBJID       270.93                                                                      UN(5,3)     SUBJID       283.12                                                                      UN(5,4)     SUBJID       320.51                                                                      UN(5,5)     SUBJID       400.03                                                                      UN(6,1)     SUBJID       255.04                                                                      UN(6,2)     SUBJID       264.57                                                                      UN(6,3)     SUBJID       276.21                                                                      UN(6,4)     SUBJID       294.30                                                                      UN(6,5)     SUBJID       323.57                                                                      UN(6,6)     SUBJID       373.06                                                                      UN(7,1)     SUBJID       248.73                                                                      UN(7,2)     SUBJID       256.85                                                                      UN(7,3)     SUBJID       274.29                                                                      UN(7,4)     SUBJID       284.68                                                                      UN(7,5)     SUBJID       295.96                                                                      UN(7,6)     SUBJID       310.99                                                                      UN(7,7)     SUBJID       351.47                                                                      UN(8,1)     SUBJID       249.61                                                                      UN(8,2)     SUBJID       257.29                                                                      UN(8,3)     SUBJID       275.29                                                                      UN(8,4)     SUBJID       285.32                                                                      UN(8,5)     SUBJID       296.04                                                                      UN(8,6)     SUBJID       298.96                                                                      UN(8,7)     SUBJID       329.12                                                                      UN(8,8)     SUBJID       360.03 Looking at the covariance estimates under the TYPE=UN specification, the assumption of compound symmetry is no longer valid. I' d really appreciate it if you would give me your  inputs. Thanks, Yasu ... View more

Dear ballardw, Thank you for your reply and I apologize my explanation was not enough. The H-L statistic stands for the Hosmer-Lemeshow statistic and you can view the reference article at the following link (though it might be the draft version): http://books.google.co.jp/books?hl=ja&lr=&id=oP4ZJxBE1csC&oi=fnd&pg=PA1&dq=Evaluation+of+the+Performance+of+Survival+Analysis+Models:+Discrimination+and+Calibration+Measures&ots=NGZ2ZHObiP&sig=r5ErSjPz9JdfSFsR63F7hR4CSUk#v=onepage&q=Evaluation%20of%20the%20Performance%20of%20Survival%20Analysis%20Models%3A%20Discrimination%20and%20Calibration%20Measures&f=false Your continued support will be greatly appreciated. Yasu ... View more