Hello SAS community,   Thanks in advance for your help / interest in my question.  I've searched around for this topic online and haven't found much guidance so I decided to post here.  I am having trouble with a multiple imputation model (which I run using PROC MI) which seemed to be running just fine a few days ago but now seems to be producing an error.  I am imputing values for four ordinal categorical (3 categories) variables which contain missing values.  I am imputing using logistic regression and fully conditional specification. Like I said, this code was running great until recently, now I cannot figure out what I did to the code that might have changed what used to run properly.  Please note I am running PROC MI within a macro call, hence the macro variables. Below this message, I've included the SAS code for my PROC MI run as well as the erroneous log output. Also note I'm using SAS Version 9.4.   When I run my PROC MI, my log contains an error reading: "ERROR: An exception has been encountered. Please contact technical support and provide them with the following traceback information: The SAS task name is [MI Segmentation Violation".  The more interpretable warning that accompanies this error states WARNING: An effect for variable a1c_ord_1yr is a linear combination of other effects. The coefficient of the effect will be set to zero in the imputation.   I could be missing something but as far as i can tell, none of the variables I'm imputing (i.e. a1c_ord_1yr, ldl_ord_1yr, sbp_ord_1yr, or dbp_ord_1yr) are linear combinations of other effects.  Note that I have also tried to resolve the log error by changing the nimpute (number of imputation), nbiter (number of burn-in iterations) and the seed.  None of these seemed to work.   If anyone has any intuition RE: what might cause the above errors please let me know.  I do have the ability to contact SAS technical support (as instructed in the log) via my institution but thought I'd check with the community first. Please let me know if there's anything that I can do to provide more information. Thank you for your help.    SAS CODE THAT PRODUCES ERROR (MACRO AND MACRO CALL)   /*STEP 1:MISSING data Pattern*/ /****************************/ %macro mi(status, lb, Strat, ordinal, bootstrap); *Number of bootstrap resamples*; %IF bootstrap=Y %THEN %LET NumSamples = 2; %ELSE %LET NumSamples = 1;; *Generate many bootstrap samples*; proc surveyselect data=cohort_&lb._lookback NOPRINT seed=1 out=cohort_&lb._lookback_BS(rename=(Replicate=SampleID)) method=urs /* resample with replacement */ samprate=1 /* each bootstrap sample has N observations */ /* OUTHITS option to suppress the frequency var */ reps=&NumSamples; /* generate NumSamples bootstrap resamples */ run; proc sort data=work.cohort_&lb._lookback_BS; %IF &Strat.=Gender %THEN %DO; BY SampleID GENDER; %END;; %IF &Strat.=Age %THEN %DO; BY SampleID age_bin; %END;; %IF &Strat.=GenderAge %THEN %DO; BY SampleID GENDER age_bin; %END;; run; proc mi nimpute=3 seed=1234 data=work.cohort_&lb._lookback_BS simple OUT=cohort_&lb._lookback_MI_BS; %IF &Strat.=Gender %THEN %DO; BY SampleID GENDER; %END;; %IF &Strat.=Age %THEN %DO; BY SampleID age_bin; %END;; %IF &Strat.=GenderAge %THEN %DO; BY SampleID GENDER age_bin; %END;; CLASS %IF ((&Strat.=Gender) or (&Strat.=)) %THEN AGECAT; %IF ((&Strat.=Age) or (&Strat.=)) %THEN GENDER; YEAR RACE OUTPTVISIT_&lb._cat SNF_&lb._cat HS_&lb._cat UniqueDrugs_&lb._cat %IF &ordinal=N %THEN a1c_&lb. ldl_&lb. sbp_&lb. dbp_&lb.; %ELSE a1c_ord_&lb. ldl_ord_&lb. sbp_ord_&lb. dbp_ord_&lb.;; VAR &status. StatinInitiator %IF &ordinal=N %THEN a1c_&lb. ldl_&lb. sbp_&lb. dbp_&lb.; %ELSE a1c_ord_&lb. ldl_ord_&lb. sbp_ord_&lb. dbp_ord_&lb.; /*Class variables*/ /*Need to omit the agecat variable whenever we stratify by age since the values will be non-overlapping within age strata*/ %IF ((&Strat.=Gender) or (&Strat.=)) %THEN AGECAT; YEAR RACE /*LS_&lb._cat*/ /*SS_&lb._cat*/ OUTPTVISIT_&lb._cat SNF_&lb._cat HS_&lb._cat UniqueDrugs_&lb._cat /*Continuous variables*/ AGEyrs Age_sq /*LS_&lb.*/ /*SS_&lb.*/ SNF_&lb. UniqueDrugs_&lb. HS_&lb. OUTPTVISIT_&lb. /*Binary variables*/ AFIB_&lb. AMBLIFESUPPORT_&lb. ANEMIA_&lb. ANGIOGRAPHY_&lb. ARB_&lb. ASTHMA_&lb. CANCERSCREEN_&lb. CKD_&lb. COLONOSCOPY_&lb. COPD_&lb. DEMENTIA_&lb. DIURETICS_&lb. ECHOCARDIOGRAPH_&lb. FECALOCCULT_&lb. %IF ((&Strat=Age) or (&strat=)) %THEN GENDER; HOMEOXYGEN_&lb. HSCRP_&lb. HYPERLIPIDEMIA_&lb. INCL_ENDARTERECTOMY INCL_STROKE INFLAMBOWEL_&lb. INSULIN_&lb. LIPIDPANEL_&lb. OBESITY_&lb. OSTEOARTHRITIS_&lb. PARALYSIS_&lb. PCD_&lb. PSYCHIATRIC_&lb. PVD_&lb. SEPSIS_&lb. SMOKING_&lb. STRESSTEST_&lb. SUBABUSE_&lb. SULFONYLUREA_&lb. THIAZIDE_&lb. VERTIGO_&lb. VTE_&lb. WEAKNESS_&lb. WHEELCHAIR_&lb.; FCS nbiter=10 /*Default is 10*/ logistic(%IF &ordinal=N %THEN a1c_&lb. ldl_&lb. sbp_&lb. dbp_&lb.; %ELSE a1c_ord_&lb. ldl_ord_&lb. sbp_ord_&lb. dbp_ord_&lb.; / details link=glogit likelihood=augment ORDER=FREQ); run; %MEND mi; %mi(status=STATUS_death, lb=1yr, Strat=GenderAge, ordinal=Y, bootstrap=N);     SAS LOG CONTAINING ERROR   MPRINT(MI): proc mi nimpute=3 seed=1234 data=work.cohort_1yr_lookback_BS simple OUT=cohort_1yr_lookback_MI_BS; MLOGIC(MI): %IF condition &Strat.=Gender is FALSE MPRINT(MI): ; MLOGIC(MI): %IF condition &Strat.=Age is FALSE MPRINT(MI): ; MLOGIC(MI): %IF condition &Strat.=GenderAge is TRUE MPRINT(MI): BY SampleID GENDER age_bin; MPRINT(MI): ; MLOGIC(MI): %IF condition ((&Strat.=Gender) or (&Strat.=)) is FALSE MLOGIC(MI): %IF condition ((&Strat.=Age) or (&Strat.=)) is FALSE MLOGIC(MI): %IF condition &ordinal=N is FALSE MPRINT(MI): CLASS YEAR RACE OUTPTVISIT_1yr_cat SNF_1yr_cat HS_1yr_cat UniqueDrugs_1yr_cat a1c_ord_1yr ldl_ord_1yr sbp_ord_1yr dbp_ord_1yr; MLOGIC(MI): %IF condition &ordinal=N is FALSE MLOGIC(MI): %IF condition ((&Strat.=Gender) or (&Strat.=)) is FALSE MLOGIC(MI): %IF condition ((&Strat=Age) or (&strat=)) is FALSE MPRINT(MI): VAR STATUS_death StatinInitiator a1c_ord_1yr ldl_ord_1yr sbp_ord_1yr dbp_ord_1yr YEAR RACE OUTPTVISIT_1yr_cat SNF_1yr_cat HS_1yr_cat UniqueDrugs_1yr_cat AGEyrs Age_sq SNF_1yr UniqueDrugs_1yr HS_1yr OUTPTVISIT_1yr AFIB_1yr AMBLIFESUPPORT_1yr ANEMIA_1yr ANGIOGRAPHY_1yr ARB_1yr ASTHMA_1yr CANCERSCREEN_1yr CKD_1yr COLONOSCOPY_1yr COPD_1yr DEMENTIA_1yr DIURETICS_1yr ECHOCARDIOGRAPH_1yr FECALOCCULT_1yr HOMEOXYGEN_1yr HSCRP_1yr HYPERLIPIDEMIA_1yr INCL_ENDARTERECTOMY INCL_STROKE INFLAMBOWEL_1yr INSULIN_1yr LIPIDPANEL_1yr OBESITY_1yr OSTEOARTHRITIS_1yr PARALYSIS_1yr PCD_1yr PSYCHIATRIC_1yr PVD_1yr SEPSIS_1yr SMOKING_1yr STRESSTEST_1yr SUBABUSE_1yr SULFONYLUREA_1yr THIAZIDE_1yr VERTIGO_1yr VTE_1yr WEAKNESS_1yr WHEELCHAIR_1yr; MLOGIC(MI): %IF condition &ordinal=N is FALSE MPRINT(MI): FCS nbiter=10 logistic( a1c_ord_1yr ldl_ord_1yr sbp_ord_1yr dbp_ord_1yr / details link=glogit likelihood=augment ORDER=FREQ); MPRINT(MI): run;   WARNING: An effect for variable a1c_ord_1yr is a linear combination of other effects. The coefficient of the effect will be set to zero in the imputation. WARNING: An effect for variable ldl_ord_1yr is a linear combination of other effects. The coefficient of the effect will be set to zero in the imputation.   ERROR: An exception has been encountered. Please contact technical support and provide them with the following traceback information:   The SAS task name is [MI ] Segmentation Violation   Traceback of the Exception:   /opt/sas/SASHome/SASFoundation/9.4/sasexe/sas(+0x15cfde) [0x5593446d5fde] /opt/sas/SASHome/SASFoundation/9.4/sasexe/sas(+0x4cb7b) [0x5593445c5b7b] /opt/sas/SASHome/SASFoundation/9.4/sasexe/tkmk.so(bkt_signal_handler+0x144) [0x7fa75e0d0404] /lib64/libpthread.so.0(+0xf5d0) [0x7fa75f3405d0] /opt/sas/SASHome/SASFoundation/9.4/sasexe/tkmk.so(skm_frontlink+0xf4) [0x7fa75e0e37b4] /opt/sas/SASHome/SASFoundation/9.4/sasexe/tkmk.so(skmMemRelease+0x310) [0x7fa75e0e1eb0] /opt/sas/SASHome/SASFoundation/9.4/sasexe/sasmi(+0x4a5a4) [0x7fa7059135a4] /opt/sas/SASHome/SASFoundation/9.4/sasexe/sasmi(+0x53816) [0x7fa70591c816] /opt/sas/SASHome/SASFoundation/9.4/sasexe/sasmi(mipfcs1+0x194) [0x7fa70590abf4] /opt/sas/SASHome/SASFoundation/9.4/sasexe/sasmi(+0x1923b) [0x7fa7058e223b] /opt/sas/SASHome/SASFoundation/9.4/sasexe/sas(vvtentr+0x13d) [0x5593445c571d] /lib64/libpthread.so.0(+0x7dd5) [0x7fa75f338dd5] /lib64/libc.so.6(clone+0x6d) [0x7fa75e924ead]   NOTE: The SAS System stopped processing this step because of errors. WARNING: The data set WORK.COHORT_1YR_LOOKBACK_MI_BS may be incomplete. When this step was stopped there were 139698 observations and 1490 variables. NOTE: The PROCEDURE MI printed pages 13-117. NOTE: PROCEDURE MI used (Total process time): real time 5:40.89 cpu time 5:39.59 ... View more

Hello,   I am trying to migrate a survival analysis out of PROC LIFETEST and into PROC PHREG and ran into some incongruencies that I want to make sure I understand. Specifically, I am confused by the difference between the confidence bands generated for my survival curves when I use PROC LIFETEST and PROC PHREG. Any feedback would be hugely appreciated (including links to technical documentation if that would help).   The options for survival confidence bands under PROC LIFETEST are Hall Wellner bands (cb=hw) or equal precision bands (cb=ep).  The options for survival confidence bands under PROC PHREG are log (clytpe=log), loglog (clytype=loglog), and normal (cltype=normal). It seems like when I specify cltype=log in PROC PHREG, my confidence intervals are close to the Hall Wellner bands I get from PROC LIFETEST.  However they are not exactly the same.  While I would love to have a conceptual understanding of the difference between these two procedures, the important question is this: Is this variation that I need to be concerned about or is it simply because the two procedures are using slightly different (but similarly valid) approaches to calculating confidence intervals?  Also a secondary question: Are there settings that I can apply in LIFETEST and PHREG that will generate identical survival functions and confidence bands?   Here are some things I think I understand to be true: Neither PHREG nor LIFETEST confidence bands will necessarily reflect the point-wise confidence intervals. All three PHREG options use some form of normal theory approximation.  The three different options allow transformations before assuming normality and calculating the corresponding confidence limits. Hall Wellner bands, estimated from PROC LIFETEST, also assume normality using the Miller and Siegmund equation (see lifetest reference below). I did at one point see a note in my log for PROC LIFETEST that said: "The LOGLOG transform is used to compute the confidence limits for the quartiles of the survivor distribution."    Two releveant references that I found informative:     Description of PHREG confidence limit types (see entry for "CLTYPE=method" on page 12): http://www.okstate.edu/sas/v8/saspdf/stat/chap49.pdf Description of equal precision and Hall Wellner confidence bands calculated in PROC LIFETEST: https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_lifetest_a0000000263.htm   ... View more

  Thanks in advance for your time and I apologize for how naieve my question might be! I am just looking for big-picture input - not expecting code or any specific solution. For the record I am using SAS 9.3 and do not currently have access to SAS 9.4.   I am generating a survival plot using PHREG.  I have generated survival plots using two different methods: 1) plots generated within PHREG using the PLOTS = option and 2) plots generated using SGPLOT and the dataset output by the PHREG BASELINE statement.   For either of these approaches, is it possible (in SAS 9.3) to generate a survival plot that contains the number of subjects at risk along the x-axis (time)?  I know how to do this in PROC LIFETEST (i.e. by using PROC TEMPLATE); however I can't figure out how to do this in PROC PHREG.  Can I use the same PROC TEMPLATE approach I use for PROC LIFETEST to revise the axis in SGPLOT?   I have reviewed the following resources (and drawn the corresponding conclusions): https://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_phreg_sect006.htm This reference seems to indicate that PHREG is capable of generating the ATRISK data.  However, I can't figure out how to incorporate it in the plot. Furthermore, I'm not sure how to output these data in the dataset generated by my BASELINE statement.  However, I assume I can figure out how to join the ATRISK table with my survival table. But this still doesn't solve the problem of how to get it into the plot. http://blogs.sas.com/content/graphicallyspeaking/2014/02/09/survival-plot/ This reference seems to indicate that SAS 9.4 incorporates is capable of doing what I want (i.e. plot ATRISK values using SGPLOT's XAXISTABLE statmenet. However, as far as I can tell, this reference only describes how SAS 9.3 can plot the ATRISK values using PROC LIFETEST.  ... View more

I am currently taking a very basic look at variable distributions in (publicily accessible) NHANES data - National Health and Nutrition Examination Survey.  Since my data contains survey weights, I'm using PROC SURVEYMEANS to generate estimates of means for continuous variables and the corresponding standard errors. Unfortunately I am getting nonsense values for estimates of the standard error (and standard deviation). In an online tutorial page, NHANES provides code and explanation for using PROC SURVEYMEANS to estimate means and standard errors in their weighted data (see step 2 of http://www.cdc.gov/nchs/tutorials/NHANES/NHANESAnalyses/DescriptiveStatistics/Task3b.htm ). As far as I can tell, I have correctly contructed my weight variable and have appropriately specified the STRATA and CLUSTER statements in PROC SURVEYMEANS.   I can't figure out why but I get really high values for standard deviation and really low values for standard error.  The means however look correct.  I have included my PROC SURVEYMEANS code below.  Since this is all publicly accessible data, I have also included my output below.   Thanks for any help.   CODE:   proc surveymeans data=NHANES_DataMerge NOBS MEAN STDERR STD; strata sdmvstra; cluster sdmvpsu; var LBDLDL RIDAGEYR BMXBMI; weight weight; ods output Statistics=printdata; run; PROC PRINT DATA=work.printdata; RUN;   OUTPUT:   Obs    VarName         VarLabel                                                       N            Mean                   StdErr            StdDev   1         LBDLDL          LDL-cholesterol (mg/dL)                            5245       127.594611      0.711109       1912657887 2         RIDAGEYR     Age at Screening Adjudicated - Recode    5245       52.729637        0.189132        779619697 3         BMXBMI         Body Mass Index (kg/m**2)                        5245       28.604494        0.128091        417023923     ... View more

I'm trying to generate two sets of 5,000 random numbers.  I have successfully generated the first set, which is a uniform distribution of integers from 0 to 120. For the second set, I would like to sample from a function with a linear (monotonic) increase in probability over that interval.  So, the probability of randomly generating the number 0 is near 0 and the probability of randomly generating the number 120 is greater than all other numbers. I apologize if this seems basic but I'm having a hard time phrasing my issue and so have been unable to find any technical support on this issue.  If anyone has any references for me to review, I'd greatly appreciate it. My current thinking is that I somehow need to transform the rand("uniform") distribution to increase linearly, but I can't seem to figure out. I'd prefer if I could adjust the slope of the line. I've pasted some code below that shows how I've been trying to code this. data Random_numbers; call streaminit(123); /* set random number seed */ do i = 1 to 5000;    uniform = int(rand("uniform") *120);    increasing = int((rand("uniform") * SOME TRANSFORMATION HERE? )*120);    output; end; PROC FREQ DATA = Random_numbers;   TABLES uniform increasing ; RUN; Thanks for your help! ... View more

Hello and thank you for helping.  I am trying to figure out a way to use the AVG aggregate function in PROC SQL to average across a subset of the data, without specifying this subset using WHERE or HAVING statements. This is because I would like to calculate multiple different averages across different subsets within the data, all within the same select statement, creating a different variable name for each different average.  I have included an illustrative example of what I am attempting to do below. Consider the following data set: DATA data;    INPUT id c b;    DATALINES; 1  1  1 17  0  1 33  0  0 49  0  0 65  1  0 81  1  1 2   1  0 18  0  1 34  0  0 50  1  0 20  1  1 39  0  0 ; PROC PRINT; RUN; Using only one PROC SQL step (i.e. a single select statement), I would like to create a data set which contains only one observation and two variables: AVG_B_C0 = the average value of B when c = 0 AVG_B_C1 = the average value of B when c = 1 So my output table should look like this: Obs Mean_B_C0 Mean_B_C1 1 .33333 .5 I was trying to use this code but it doesn't work.  The reason I was trying to use this code is that it works;  if I use the SUM aggregate function rather than the AVG aggregate function. PROC SQL;      CREATE TABLE summary AS      SELECT           AVG((c=0)*B) AS Mean_B_C0,           AVG((c=1)*B) AS Mean_B_C1 FROM data; QUIT; Can anyone help me?  This code is part of a program that runs iteratively and I have to calculate values in multiple different subsets. While I know how to do this using multiple PROC SQL steps, I would prefer not to since it slows down the speed of my program.  Just to note, I also tried using CASE WHEN statements, but it ends up returning two rows in my output table, with missing values for when the case statement was not satisfied. ... View more