Hello,   I study an interaction PRODUCT*VISIT with PRODUCT = 1 if active and 0 else, and VISIT=(V1, V2). According you, it's possible to have a meaningful interaction with PRODUCT*V1 but that the gobal interaction it's not  meaningful ?   Thanks, Clemence ... View more

Hello, I make multiple imputation with mixed model and I wish combine results for p-value of global interaction. I saw that there might be a macro, is it still relevant? how can I proceed ?   Here, my code : proc mixed data=v1_v2_im method=reml ; class prod (ref="Placebo") TV_VISIT_TMP(ref="V1") id_bis; model changeVn_V0 = prod TV_VISIT_TMP prod*TV_VISIT_TMP / s ddfm=kenwardroger; repeated TV_VISIT_TMP / subject=id_bis type=CS; lsmeans prod*TV_VISIT_TMP / slice=TV_VISIT_TMP cl; slice prod*TV_VISIT_TMP / sliceby(TV_VISIT_TMP="V1") cl pdiff=control("Placebo" "V1"); slice prod*TV_VISIT_TMP / sliceby(TV_VISIT_TMP="V2") cl pdiff=control("Placebo" "V2"); by simul _Imputation_; ods output SolutionF=mixparms ; ods output SliceDiffs=slicev1v2; run;   proc mianalyze parms(classvar=full)=mixparms ; class prod TV_VISIT_TMP; modeleffects Intercept prod TV_VISIT_TMP prod*TV_VISIT_TMP ; by simul; ods output ParameterEstimates=param_simul; run;   Thanks, Clemence ... View more

Hello, I make an multiple imputation from an uniform distribution. So, I create 5 dataset with each a different seed for the distribution uniforme to have different dataset. Then I concatenate the5 dataset in a single dataset "Mimpute".  I realize a mixted model on this dataset.  Then, I use PROC MIANALYZE to pooling results. But when I count how many I have p-value < 0.05, I obtain a number very small number, and this number does not match with the number for each imputation.  I make that for 1000 simulations, so the variable simul indicated the simulation number.   Here, my code :   proc mixed data=Mimpute method=reml ; class prod (ref="Placebo") TV_VISIT_TMP(ref="V1") id_bis; model changeVn_V0 = prod TV_VISIT_TMP prod*TV_VISIT_TMP / s ddfm=kenwardroger; repeated TV_VISIT_TMP / subject=id_bis type=CS; lsmeans prod*TV_VISIT_TMP / slice=TV_VISIT_TMP cl; slice prod*TV_VISIT_TMP / sliceby(TV_VISIT_TMP="V1") cl pdiff=control("Placebo" "V1"); slice prod*TV_VISIT_TMP / sliceby(TV_VISIT_TMP="V2") cl pdiff=control("Placebo" "V2"); by simul _Imputation_; ods output SolutionF=mixparms ; ods output SliceDiffs=slicev1v2; run;   proc mianalyze parms(classvar=full)=mixparms ; class prod TV_VISIT_TMP; modeleffects Intercept prod TV_VISIT_TMP prod*TV_VISIT_TMP ; by simul; ods output ParameterEstimates=param_simul; run;   To count the number of global p-value of interaction <0.05, I do :   data param_simul; set param_simul; where Parm="prod*TV_VISIT_TMP" AND prod="Actif" AND TV_VISIT_TMP="V2" ; run;   proc sql; select count(simul) from param_simul where Probt < 0.05; quit;   And I obtain 45/1000. But, in each imputation, I obtain numbers : 127/1000, 129/1000, 133/1000, 127/1000, 144/1000. For me, after PROC MIANALYZE, I should get a number arround 130/1000 because I combine the different imputations.    Could you help me please ? I think that it's the statement 'by' of proc mianalyze which is the problem....   Thanks; Clemence ... View more

Hello,   I use an multiple imputation and I realize two tests, so I adjusted the pvalue with the bonferonni method. But, how can I have the adjusted p-value with PROC MIANALYZE ?    Here my code :    proc mixed data=v1_v2_im method=reml; class PRDT (ref="Placebo") TV_VISIT_TMP(ref="V1") SUBJID; model changeVn_V0 = PRDT TV_VISIT_TMP PRDT*TV_VISIT_TMP / s ddfm=kenwardroger; repeated TV_VISIT_TMP / subject=SUBJID type=CS; lsmeans PRDT*TV_VISIT_TMP / slice=TV_VISIT_TMP cl diff adjust=bon; slice PRDT*TV_VISIT_TMP / sliceby(TV_VISIT_TMP="V1") cl pdiff=control("Placebo" "V1"); slice PRDT*TV_VISIT_TMP / sliceby(TV_VISIT_TMP="V2") cl pdiff=control("Placebo" "V2"); by _Imputation_; ods output SolutionF=mixparms; ods output SliceDiffs=slice; ods output Slices=slice2; ods output Diffs=difs; run;   It's in the table "Difs" that there are pvalues adjusted. But, I don't know how to use PROC MIANALYZE to pooling adjusted pvalues ?   Thanks for your answer. Clémence  ... View more

Hello,    I use PROC NLMIXED and I would like know if it's possible to have the p-value for fixe effect with the method of type3 in proc nlmixed ?   My code is :   proc nlmixed data=simul ; parms sigsq0=0.2 sigsqe=0.3 beta0=1 beta1=0.15 beta2=0.55 beta3=0.04 beta4=-0.8 beta5=0.2; bounds sigsq0 sigsqe >= 0; pi=2*arsin(1); mu=beta0+beta1*produit_bis+beta2*visite1+beta3*visite2+beta4*produit_bis*visite1+beta5*produit_bis*visite2 + a_i; if Censure_aureus=0 then ll=(1/(sqrt(2*pi*sigsqe)))*exp(-(LB_qSTAanum_LOG_cop_cm-mu)**2/(2*sigsqe)); if Censure_aureus=1 then ll=probnorm((LB_qSTAanum_LOG_cop_cm-mu)/sqrt(sigsqe)); L=log(ll); model LB_qSTAanum_LOG_cop_cm ~ general(L); random a_i ~ normal(0,sigsq0) subject=id_bis; by simul; ods output ParameterEstimates=param_mle ; run;   With this code, I obtain p-values for beta4 and beta5 which represent the product effect at visite1 and visite2 but, I would like obtain the global pvalue for the interaction. According you, it's possible with proc nlmixed ?   Thanks for your answer.   Clémence ... View more

Hello,   I use PROC NLMIXED to estimate a mean. And I wish to compare the result at the PROC MEANS.   My code is :   proc nlmixed data=lbmain_inclus_final; parms sigsq0=0.2 sigsqe=0.3 beta0=0.3851 beta1=0.1433 beta2=0.5633 beta3=0.01331 beta4=0.01 beta5=0.02 ; bounds sigsq0 sigsqe >= 0; pi=2*arsin(1); mu=beta0+beta1*produit+beta2*visite1+beta3*visite2+beta4*produit*visite1+beta5*produit*visite2+ a_i; ll=(1/(sqrt(2*pi*sigsqe)))*exp(-(LB_qSTAanum_LOG_cop_cm-mu)**2/(2*sigsqe)); L=log(ll); model LB_qSTAanum_LOG_cop_cm ~ general(L); random a_i ~ normal(0,sigsq0) subject=SUBJID;   /**Estimates**/ moy_v1_prod=beta0+beta1+beta2+beta4; estimate "moy_v1 produit" moy_v1_prod;   run;   With this code, I obtain the same mean that PROC MEANS but how obtain the standard deviation ? Because proc nlmixed give the standard error but when I calculate : sqrt(n)*standard_error, I don't obtain the same standard deviation that proc means. Is it normal ? Or, how obtain the good standard deviation with proc nlmixed ?   Thanks for your answer. Clémence   ... View more