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AVA_16
‎04-05-2019
  • 26 Posts
  • 6 Likes Given
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Re: 95% confidence intervals of Youden index

by Obsidian | Level 7 in Statistical Procedures
‎03-21-2019 10:17 AM
‎03-21-2019 10:17 AM
Thank you for your help. There was some issue with the dataset. Your help is highly appreciated. ... View more

Re: Proc glimmix/binary outcome/multilevel model

by Obsidian | Level 7 in Statistical Procedures
‎03-21-2019 09:30 AM
‎03-21-2019 09:30 AM
Thank you for your response:   I ran the model in the full data set.    proc glimmix data = final method=quad noclprint; class sex (ref='2') treatment (ref='0') ; model T2D_1(event='1') = X age sex bmi treatment / dist=binary link=logit; random intercept /subject=Family_id type=vc ; covtest/wald; output out=glmmout3 pred=xbeta pred(ilink)=predprob pred(ilink noblup)=fix_predprob; run;   I get this warning:   NOTE: Some observations are not used in the analysis because of: missing fixed effects (n=205). NOTE: The GLIMMIX procedure is modeling the probability that T2D_1='1'. WARNING: Obtaining minimum variance quadratic unbiased estimates as starting values for the covariance parameters failed. NOTE: The data set WORK.GLMMOUT3 has 3997 observations and 12 variables.   When I add parms in the above code as:   proc glimmix data = final method=quad noclprint; class sex (ref='2') treatment (ref='0') ; model T2D_1(event='1') = X age sex bmi treatment / dist=binary link=logit; random intercept /subject=Family_id type=vc ; parms (0.003); covtest/wald; output out=glmmout3 pred=xbeta pred(ilink)=predprob pred(ilink noblup)=fix_predprob; run;   The warning is:   OTE: Some observations are not used in the analysis because of: missing fixed effects (n=205). NOTE: The GLIMMIX procedure is modeling the probability that T2D_1='1'. NOTE: Convergence criterion (FCONV=2.220446E-16) satisfied. NOTE: At least one element of the gradient is greater than 1e-3. NOTE: The data set WORK.GLMMOUT3 has 3997 observations and 12 variables.   Based on the above warning I guess, the model is misspecified or a problematic model fitting problem. This is becasue when I try nloptions tech=   proc glimmix data = final method=quad noclprint; class sex (ref='2') treatment (ref='0') ; model T2D_1(event='1') = X age sex bmi treatment / dist=binary link=logit; random intercept /subject=Family_id type=vc ; covtest/wald; nloptions tech=nrridg; parms (0.003); output out=glmmout3 pred=xbeta pred(ilink)=predprob pred(ilink noblup)=fix_predprob; run;   NOTE: Some observations are not used in the analysis because of: missing fixed effects (n=205). NOTE: The GLIMMIX procedure is modeling the probability that T2D_1='1'. ERROR: NRRIDG Optimization cannot be completed. NOTE: ERROR: The function value of the objective function cannot be computed during the optimization process. NOTE: The data set WORK.GLMMOUT3 has 3997 observations and 12 variables. Any suggestions?   Regards,   Ava ... View more

Re: 95% confidence intervals of Youden index

by Obsidian | Level 7 in Statistical Procedures
‎03-21-2019 04:24 AM
‎03-21-2019 04:24 AM
The probability value associated with the unique optimum for the Youden index is the same I am using in the INVERSECL(PROB=). Even then the value of the cut-off for the variable X is coming out different. ... View more

Proc glimmix/binary outcome/multilevel model

by Obsidian | Level 7 in Statistical Procedures
‎03-20-2019 05:41 AM
‎03-20-2019 05:41 AM
  Dear SAS experts,   I am using proc glimmix to analyze two-level data. Level-1 patient and level-2 is family. I have 1353 families in total. The minimum number of patients in one family is one and maximum are 49. Most of the families have members from 2 to 4.   My research question is: What is the relationship between patient X variable and the likihood of geting diabetes while controlling for patient and family characteristics?   The following datastep shows a part of my data:   data have; input Family_id $ Patient_id T2D X age sex BMI treatment; datalines;   1 2 0 10.6 42.8 0 28.6 0 1 5 0 10.5 59.2 1 29.0 0 1 6 0 6.89 48.0 0 24.2 0 9 7 1 17.3 56.8 1 24.4 0 9 10 1 16.4 68.8 1 26.4 1 11 12 0 17.0 76.0 1 21.2 1 12 13 0 7.5 53.6 1 20.0 0 12 14 1 14.8 71.2 0 23.6 0 12 15 0 10.3 64.8 0 25.0 0 12 16 1 15.6 56.8 0 25.6 1 13 17 0 10.5 59.2 1 29.0 0 15 18 0 6.89 47.2 0 25.2 0 15 20 1 16.3 56.8 1 26.4 1 15 22 1 17.4 57.2 1 26.4 1 15 24 1 15.0 66.0 1 25.2 1 15 25 0 7.5 53.6 1 26.0 0 16 27 0 6.8 71.2 0 23.6 0 16 28 0 6.7 64.8 0 25.0 0 17 29 0 8.89 49.2 0 22.2 0 18 30 1 16.3 66.8 1 23.4 1 19 31 1 15.4 72.2 1 26.4 1 20 32 1 14.9 68.6 0 25.2 0 20 33 0 7.5 53.6 1 26.0 0 20 34 0 6.8 71.2 0 23.6 0 20 35 0 6.7 64.8 0 25.0 0 ; run;   I am using the following model to answer my question:   **************Proc glimmix fully adjusted model; proc glimmix data = have method=laplace noclprint; class family_id sex ; TITLE 'ROC curve of 1-hour OGTT'; model T2D(event='1') = X age sex BMI treatment /s cl dist=binary link=logit; random intercept /subject=family_id type=cs solution cl ; covtest/wald; output out=glmmout3 pred=xbeta pred(ilink)=predprob pred(ilink noblup)=fix_predprob; run;   I get the following as a warning:   NOTE: The GLIMMIX procedure is modeling the probability that T2D='1'. WARNING: Obtaining minimum variance quadratic unbiased estimates as starting values for the covariance parameters failed. NOTE: The data set WORK.GLMMOUT3 has 25 observations and 11 variables.     I am not sure what is wrong with the model? Any suggestions are appreciated. ... View more

Re: 95% confidence intervals of Youden index

by Obsidian | Level 7 in Statistical Procedures
‎03-20-2019 04:41 AM
‎03-20-2019 04:41 AM
Thank you. This sounds good. However, if I use %rocplot macro to obtain a cut-off for the variable X at the highest Youden index, it comes out to be 12.1 instead of 11.5 (obtained from proc probit). The senstivity, specificity, and the highest Youden index are the same using %rocplot macro and proc probit. Only the cut-off of variable x is different. Any idea, why is it so?   The sas code, I used for the %rocplot macro is as below:   ods graphics on; proc logistic data = CAT ; class sex (ref='0'); model Diabetes_120_(event='1') = X age sex BMI / lackfit rsquare outroc=rocdata2; output out=pred2 p=pred2; roc ; run; ods graphics off;   *I am only using variable X so as get a clear picture on the roc plot. However, the inroc data and predicted probabilities are from the adjusted model;   %inc "Path of the %rocplot macro"; %rocplot("9.4", inpred = pred2,inroc = rocdata2, p = pred2, id = X _OPTY_ _sens_ _spec_ ,optcrit= youden, optsymbolstyle = size=13 color=red weight=bold) ... View more

Re: 95% confidence intervals of Youden index

by Obsidian | Level 7 in Statistical Procedures
‎03-15-2019 03:47 AM
‎03-15-2019 03:47 AM
Hello, Thank you for your reply. Actually, both are the same variables. Is the difference in cut-off there because in the logistic regression model other than X, age, sex, and BMI.   *confidence intervals for youden index and cutoff;   proc probit data = CAT inversecl(prob= 0.097430); model Diabetes_120_(event='1') = X age sex VAR5 / d=logistic; run;    X          95% Fiducial Limits 11.5330 11.2543 11.7997     However, for calculating a cut-off using outroc data, I am only using intercept and slope and have not accounted for age, sex, and BMI. If so, how can I account these variable while calculting the cut-off?     *Calculate a rational cut-off point in ROC curve analyses; *using logit=intercept+slope(X), where X is cutoff or cutoff=(logit+intercept)/slope; *Here intercept is -13.5972 and slope is 0.8003; data CAT3(keep=cutoff prob Sensitivity Specificity Youden); set rocdata2; logit=log(_prob_/(1-_prob_));*calculate logit; cutoff=(logit+13.5972)/0.8003; *calculate cutoff; prob= _prob_; *calculate cutoff; Sensitivity = _SENSIT_; *calculate sensitivity; Specificity = 1-_1MSPEC_; *calculate specificity; Youden= _SENSIT_+ (1-_1MSPEC_)-1; *calculate Youden index; run;   ... View more

Re: Assigning dummy family ids to patients with no family ids

by Obsidian | Level 7 in SAS Programming
‎03-14-2019 06:12 AM
‎03-14-2019 06:12 AM
Thank you. Worked. ... View more

Re: 95% confidence intervals of Youden index

by Obsidian | Level 7 in Statistical Procedures
‎03-14-2019 04:11 AM
‎03-14-2019 04:11 AM
Hello,   Thank you. I do get a confidenc interval for the cut-off using the sas code above. However, there is some problem.   ************ROC curve, fully adjusted model for age sex and bmi; *obtain intercept and slope using the below command; ods graphics on; proc logistic data = CAT ; model Diabetes_120_(event='1') = X age sex BMI / lackfit rsquare outroc=rocdata2; output out=pred2 predicted=pred2; roc "X"; run; ods graphics off;   　 *Calculate a rational cut-off point in ROC curve analyses; *using logit=intercept+slope(X), where X is cutoff or cutoff=(logit+intercept)/slope; *Here intercept is -13.5972 and slope is 0.8003; data CAT3(keep=cutoff prob Sensitivity Specificity Youden); set rocdata2; logit=log(_prob_/(1-_prob_));*calculate logit; cutoff=(logit+13.5972)/0.8003; *calculate cutoff; prob= _prob_; *calculate cutoff; Sensitivity = _SENSIT_; *calculate sensitivity; Specificity = 1-_1MSPEC_; *calculate specificity; Youden= _SENSIT_+ (1-_1MSPEC_)-1; *calculate Youden index; run;   *sort data CAT3 by descending Youden index; Proc sort data=CAT3 ; by descending Youden ; run; Proc print data=CAT3 (firstobs= 1 obs= 10); TITLE 'First ten values of Youden index'; Run;   I get this as ouput from the above command    cutoff        PROB      Sensitivity Specificity Youden1 14.2085 0.097430 0.87550 0.88062 0.75612   When I use the command below:   *confidence intervals for youden index and cutoff; proc probit data = CAT inversecl(prob= 0.097430); model Diabetes_120_(event='1') = VAR8 age sex VAR5 / d=logistic; run;    X          95% Fiducial Limits 11.5330 11.2543 11.7997   The cut-off I am getting here is different than what I got above, i.e., 14.2085. Any idea as to why is it so? ... View more

Re: Meet your community managers

by Obsidian | Level 7 in Getting Started
‎03-14-2019 04:01 AM
‎03-14-2019 04:01 AM
Hi, I would like to change my user name. Can you please help? ... View more

Re: 95% confidence intervals of Youden index

by Obsidian | Level 7 in Statistical Procedures
‎03-13-2019 10:37 AM
‎03-13-2019 10:37 AM
Thank you. Is there any way it can be done in the SAS becasue I was not able to open to go through the example  as the link to the example does not open. ... View more

Assigning dummy family ids to patients with no family ids

by Obsidian | Level 7 in SAS Programming
‎03-13-2019 10:13 AM
‎03-13-2019 10:13 AM
I have a dataset with 4500 observations  with variables family_ids, patient_id, Diabetes (1/0), age, sex, BMI Some of the patients do not belong to any family and have not been assigned any family id. for e.g, the data is like:   Family_ids patient   1   3   4   6   9 1 20 1 21 1 90   There are 191 patients  with no family id. I would like to assign a family id to these patients other than the family ids already in the dataset. I would like to do so because the class identifier in my multilevel model is family_id. I would not like to delete these observations or assign a missing value to them.   How can it be done? Any help is appreciated. ... View more

Re: 95% confidence intervals of Youden index

by Obsidian | Level 7 in Statistical Procedures
‎03-13-2019 05:13 AM
‎03-13-2019 05:13 AM
Thank you for your reply. Here is the sas code I have used:   ************ROC curve, fully adjusted model for age sex and bmi; *btain intercept and slope using the below command;   ods graphics on; proc logistic data = CAT ; TITLE 'ROC curve of X'; model Diabetes_120_(event='1') = X age sex BMI / lackfit rsquare outroc=rocdata2; output out=pred predicted=pred; roc "X"; run; ods graphics off;   　 *Calculate a rational cut-off point in ROC curve analyses; *using logit=intercept+slope(X), where X is cutoff or cutoff=(logit+intercept)/slope; *Here intercept is -13.5972 and slope is 0.8003;   data CAT3(keep=cutoff prob Sensitivity Specificity Youden); set rocdata2; logit=log(_prob_/(1-_prob_));*calculate logit; cutoff=(logit+13.5972)/0.8003; *calculate cutoff; prob= _prob_; *calculate cutoff; Sensitivity = _SENSIT_; *calculate sensitivity; Specificity = 1-_1MSPEC_; *calculate specificity; Youden= _SENSIT_+ (1-_1MSPEC_)-1; *calculate Youden index; run;   *sort data CAT3 by descending Youden index;   Proc sort data=CAT3 ; by descending Youden ; run;   Proc print data=CAT3 (firstobs= 1 obs= 10); TITLE 'First ten values of Youden index'; Run;     ... View more

95% confidence intervals of Youden index

by Obsidian | Level 7 in Statistical Procedures
‎03-12-2019 06:10 AM
‎03-12-2019 06:10 AM
Hello,   I am looking at the accuracy of a new biomarker, say X (continouous) in diagnosing diabetes (Yes or no) using logistic regression. I am using age (continouous), sex (binary), treatment (binary), BMI (continouous) as covariates in the model I am using the highest Youden index to select a cut-off for this biomarker:       For e.g.,   Obs cutoff prob Sensitivity Specificity Youden 1 14.2085 0.097430 0.87550 0.88062 0.75612               Is there any way to estimate confidence intervals for the cutoff and the highest youden index? Any help is appreciated. ... View more

Re: From rows to columns

by Obsidian | Level 7 in SAS Programming
‎03-12-2019 05:13 AM
‎03-12-2019 05:13 AM
Great! It worked now. ... View more

Re: From rows to columns

by Obsidian | Level 7 in SAS Programming
‎03-12-2019 03:29 AM
‎03-12-2019 03:29 AM
Hello,   Thank you for your helpful reply. I tried this code. it works perfect. However, it seems I have some other issue in my dataset:   # Variable Type Len Format Informat  123 Family_ids Char 5 $5. $5.   members Num 8 BEST.     Patient_ids Char 337 $337. $337.   These are the variables in my dataset. Family ids and patient ids both are character variables.   After I use the below mentioned code, I get:     Obs Family_ids Patient_ids123456789101112         1 1 1   1   2 9 2   2   2   3 350 3   3   3   3                                     Only the first patient id is taken and rest of the rows in from a particular family id are empty. I guess, this happens becasue there patient ids in the original data have space after coma as shown below. Any suggestions are appreciated.   Obs Family_ids members Patient_ids12 1 3 1, 4, 8 2 4 9, 10, 100, 200 ... View more
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