Hi,   I would like to study association between few outcomes (binary) and types of feed- bolus versus continuous in a group of patients.   I used multivariate logistic regression to do the analysis and we found significant results at 0.05 level.   However, some are making comments that our continuous fed group is sicker and very different from bolus group. However , i have all the confounders adjusted for  in my logistic regression.   I am wondering how to addresss that concern.   Thanks     ... View more

Hi,   I am wondering if i should do Proc GLM (linear regression) vs Proc GENMOD (poisson regression) for my outcome below.   Days to goal feed is my outcome which is positive integers (no decimals). My exposure is type of feed which is bolus vs continuous.   Thanks,     ... View more

Hi,   I am working with longitudinal data for the first time. I need a little help with analysis.   I have lab values preop, 6 months, 12 months, 24 months postop for creatinine for a group of patients. I want to compare the trajectory between obese and non obese group.   part of my dataset is: DMRN obesity time creatinine 7300031 nonob 0 0.8 7321580 nonob 0 0.93 7281606 nonob 0 0.73 7225922 nonob 0 0.68 7300031 nonob 6 1.79 7321580 nonob 6   7281606 nonob 6   7225922 nonob 6 1.21 7300031 nonob 12 1.6 7321580 nonob 12   7281606 nonob 12   7225922 nonob 12 1.12 7300031 nonob 24 1.53 7321580 nonob 24 1.2 7281606 nonob 24 1.4 7225922 nonob 24 1.06  i have used the below code.   proc mixed data=red.new method=ml; class DMRN obesity ; model creatinine = obesity time obesity*time / s; repeated / type=ar(1) subject=DMRN group=obesity; run;   I am pasting my output Model Information Data Set RED.NEW Dependent Variable creatinine Covariance Structure Autoregressive Subject Effect DMRN Group Effect obesity Estimation Method ML Residual Variance Method None Fixed Effects SE Method Model-Based Degrees of Freedom Method Between-Within Class Level Information Class Levels Values DMRN 74 2290228 2480811 2825809 3143067 3145614 3430846 3597837 6021584 6305161 7157755 7163265 7225922 7244425 7265190 7281606 7286918 7290563 7300031 7312662 7320783 7321580 7321866 7325162 7330013 7332704 7332787 7336014 7340994 7341213 7341980 7342354 7345560 7349764 7361060 7363079 7364229 7369669 7372753 7375796 7376577 7377069 7377576 7382679 7382681 7391466 7395193 7395311 7399264 7410841 7410870 7412002 7412512 7412822 7412865 7412900 7419006 7434224 7446572 7448861 7455344 7478866 7483328 7483731 7490221 7502069 7502324 7524993 7526872 7609175 7633993 7675067 7676515 7682442 7718968 obesity 2 nonob obese Dimensions Covariance Parameters 4 Columns in X 6 Columns in Z 0 Subjects 74 Max Obs per Subject 4 Number of Observations Number of Observations Read 298 Number of Observations Used 228 Number of Observations Not Used 70 Iteration History Iteration Evaluations -2 Log Like Criterion 0 1 -6.21039560 1 2 -33.31404693 0.03275555 2 1 -41.35741962 0.00452043 3 1 -42.53522587 0.00029572 4 1 -42.60745297 0.00000269 5 1 -42.60807686 0.00000000 Convergence criteria met. Covariance Parameter Estimates Cov Parm Subject Group Estimate Variance DMRN obesity nonob 0.04987 AR(1) DMRN obesity nonob 0.3509 Variance DMRN obesity obese 0.06532 AR(1) DMRN obesity obese 0.5371 Fit Statistics -2 Log Likelihood -42.6 AIC (Smaller is Better) -26.6 AICC (Smaller is Better) -26.0 BIC (Smaller is Better) -8.2 Null Model Likelihood Ratio Test DF Chi-Square Pr > ChiSq 3 36.40 <.0001 Solution for Fixed Effects Effect obesity Estimate Standard Error DF t Value Pr > |t| Intercept 1.1036 0.04566 72 24.17 <.0001 obesity nonob -0.09035 0.05476 72 -1.65 0.1033 obesity obese 0 . . . . time 0.006546 0.002878 152 2.27 0.0243 time*obesity nonob 0.002397 0.003590 152 0.67 0.5054 time*obesity obese 0 . . . . Type 3 Tests of Fixed Effects Effect Num DF Den DF F Value Pr > F obesity 1 72 2.72 0.1033 time 1 152 18.61 <.0001 time*obesity 1 152 0.45 0.5054 Please help me interpret. Is the trend of creatinine not significantly different among obese and non obese (p-value- 0.1033)?   Thanks. ... View more