Hello, I am confused about PROC POWER logistic. For example, I am examine the association between smoking and cardiovascular disease. I have the following data. Smoking Cardiovascular disease Total No Yes No 80 50 130 Yes 20 100 120 Total 100 150 250 I would like to use below the syntax: PROC POWER;       LOGISTIC       vardist("smoking") = binomial (p,n)       testpredictor = "smoking"       responseprob = 0.60                      testoddsratio = 1.5       ntotal = 250       power = .; run; In SAS support, it says " BINOMIAL (p, n) is a binomial distribution with probability of success and number of independent Bernoulli trials . The value of must be greater than 0 and less than 1, and must be an integer greater than 0. " What should the p and n be in this example? Thanks! ... View more

Hi, I am doing a secondary data analysis. I am trying to use PROC POWER to calculate detectable effect size based on extant data. But it seems this SAS procedure can only compute sample size or power rather than detectable effect size. Is that correct? In addition, SAS can only compute sample size for survival analysis with two exposure groups. Could anyone suggest how to calculate sample size, power or detectable effect size in a survival analysis in which there are 3 exposure groups? Thanks, ... View more

Hello, I am building model using PROC GLMSELECT with SELECT = ADJRSQ. Could anyone tell me how to set up a threshold for adjusted R-sqaure to decide addition a variable or not? For example, only when addition of a variable results in an increase of 0.01 in adjusted R-square, this variable will be retained in the model. Thank you very much for your help! ... View more

Hello, I am building model using PROC GLMSELECT with SELECT = ADJRSQ. Could anyone tell me how to set up a threshold for adjusted R-sqaure to decide addition a variable or not? For example, only when addition of a variable results in an increase of 0.01 in adjusted R-square, this variable will be retained in the model. Thank you very much for your help! ... View more

Hi, In a dataset with repeated measurements from each subject, I matched the exposure group to those who are unexposed to a certain factor. I have the data structure as follows: Obs  id     Y      X1      X2    matching 1      1     80    100      25       1           2      1     70     90       20       2  3      2     100   120     25        3 4      2     125    80      40        1 5      3     108    86      92        2 6      3     110    90      84        3 ... PROC MIXED DATA = aa METHOD = ML;      CLASS id;      MODEL Y = X1  X2;      REPEATED INT / TYPE = cs SUBJECT = id; RUN; In a matched study, the matching pairs (i.e., variable "matching" here) should be adjusted for to obtain unbias estimates. How can I adjust for matching? Treat matching as a binary variable and put it after X2, or in the REPEATED statement or allow it a RANDOM effect? Thanks a lot? ... View more

Hi I am running the following PROC MIXED procedure to compare the difference in Y between group 1 and 2 to the difference in Y between group 3 and 4. PROC MIXED;      CLASS group;      MODEL Y = group  X1  X2;      CONTRAST 'Difference of differences 1 - 2 - (3 - 4)' group 1 -1 -1 1; RUN; The levels of another confounder (e.g., X3) in group 1 and group 3 is physiological higher than the levels in group 2 and group 4. So I cannot include X3 as an independent variable in the model since putting x3 in the model will allow X3 equal in the four groups, which is not true. To make the comparison of differences in Y between groups (1-2) - (3-4) reasonable, I have to allow X3 comparable between group 1 and group3. So do between group 2 and group 4. How can I achieve it? Thanks, ... View more

Hello, I have a stacked data set mydata with below the structure: 1. some are repeatedly measured and others are not; 2. sbp_change = sbp_v2 - sbp_v1; id  group sbp_v1 sbp_v2 sbp_change 1     1     113   116      3 1     1     110   119      9 2     1     124   115     -9 3     2     125   126      1 3     2     126   134      8 ... %macro mean (a, b, c, d ,e, f, g, h); PROC MIXED DATA = mydata METHOD=ML ABSOLUTE CONVF = 0.00001 NOCLPRINT;    CLASS ID group;    MODEL &a =  group/NOINT;    LSMEANS group /CL;    REPEATED INT / TYPE = CS SUBJECT = ID; RUN; %mend; %mean (sbp_v1) %mean (sbp_v2) %mean (sbp_change) Theoretically, the lsmean sbp_change should be equal to the difference between lsmean sbp_v2 and lsmean sbp_v1. But my analyis results showed they are not equall. What is the reason? Thanks, ... View more