10-26-2020
Malthy
Fluorite | Level 6
Member since
09-23-2020
- 10 Posts
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Latest posts by Malthy
Subject Views Posted 1039 10-26-2020 01:13 PM 1121 10-25-2020 08:48 AM 2672 09-28-2020 09:31 AM 2700 09-28-2020 03:28 AM 2704 09-28-2020 03:26 AM 2742 09-25-2020 08:39 AM 2840 09-23-2020 10:17 AM 2854 09-23-2020 09:57 AM 2880 09-23-2020 08:20 AM 2951 09-23-2020 04:02 AM -
Activity Feed for Malthy
- Posted Re: Multilevel survival analysis using proc glimmix on Statistical Procedures. 10-26-2020 01:13 PM
- Posted Multilevel survival analysis using proc glimmix on Statistical Procedures. 10-25-2020 08:48 AM
- Posted Re: Multilevel survival analysis on Statistical Procedures. 09-28-2020 09:31 AM
- Liked Re: Multilevel survival analysis for SteveDenham. 09-28-2020 09:31 AM
- Posted Re: Multilevel survival analysis on Statistical Procedures. 09-28-2020 03:28 AM
- Posted Re: Multilevel survival analysis on Statistical Procedures. 09-28-2020 03:26 AM
- Liked Re: Multilevel survival analysis for SteveDenham. 09-25-2020 09:44 AM
- Posted Re: Multilevel survival analysis on Statistical Procedures. 09-25-2020 08:39 AM
- Posted Re: Multilevel survival analysis on Statistical Procedures. 09-23-2020 10:17 AM
- Liked Re: Multilevel survival analysis for SteveDenham. 09-23-2020 10:16 AM
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- Posted Re: Multilevel survival analysis on Statistical Procedures. 09-23-2020 09:57 AM
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- Posted Re: Multilevel survival analysis on Statistical Procedures. 09-23-2020 08:20 AM
- Posted Multilevel survival analysis on Statistical Procedures. 09-23-2020 04:02 AM
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Posts I Liked
Subject Likes Author Latest Post 1 1 1 1 1
10-26-2020
01:13 PM
Thanks again for your response 🙂 when i write the nloptions maxiter=1000; I get this error "the sas system stopped processing this step because of insufficient memory" I have approximately 900 000 individuals in my cohort which means that my fam_id has about 400 000 strata and some strata only include 1 individual I just tried proc genmod and included the repeated subject=fam_id and I have the same problem... convergence problems and insufficient memory...is it because I have too many strata with too few individuals? Best, Malene
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10-25-2020
08:48 AM
Hi, in a longitudinal cohort study I am investigating individuals being exposed to environmental toxins in early childhood and their risk of developing ADHD. The data has a multilevel structure (subjects within families (family_id) within different regions, below the variable f_region) and I used Proc Glimmix and ran the syntax below. (lnpyrs are person years) Proc glimmix data=temp method=quad;
class toxin yeargrp SES f_region family_id;
model case=toxin yeargrp SES/ dist=poisson link=log offset=lnpyrs covb cl solution;
random intercept / subject=f_region;
random intercept / subject=family_id(f_region);
run; However, I have a problem when I include families (family_id) in the multilevel model because there are too few individuals in each strata or maybe to many strata...anyhow the model does not converge. Does anyone have a suggestion how to handle this problem? I need to keep the individual level and I cannot exclude siblings. I have been told that some take clustering into account by estimating clustered standard errors e.g. by the repeated statement in proc genmod but is there a similar option in proc glimmix ? I hope to hear from some of you 🙂 Best regards, Malene
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09-28-2020
09:31 AM
Ah of course 🙂 Thanks again for your help, I really appreciate it!
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09-28-2020
03:26 AM
Hi Steve, Thank you again for your helpful answers! So far i have treated the variable toxin as a categorical variable (divided into deciles) and I found the effect of the toxin on my outcome in each decile, However I would like to investigate the effect on the outcome per 1 unit increase in the toxin treating the toxin variable as continous and I used the same syntax as before. Proc glimmix data=temp method=quad; class toxin_con yeargrp SES f_region; model case=toxin_con yeargrp SES / dist=poisson link=log offset=lnpyrs covb cl solution; random intercept /subject=f_region; run; However, SAS seems to think that the variable is categorical with 1189 levels and obviously this does not work. So my question is how do i "explain" SAS that this variable should be treated as continous? Best, Malene
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09-25-2020
08:39 AM
Hi again, I have one last question (I hope :-)). Some of the individuals in the cohort have missing values in the SES (Socioeconomic status) variable, do you know how missing values are handled? I know that STATA drops all observations that have a missing value for any one of the variables used in the model is it the same in SAS? Proc glimmix data=temp method=quad;
class toxin yeargrp SES f_region family_id;
model case=toxin yeargrp SES/ dist=poisson link=log offset=lnpyrs covb cl solution;
random intercept / subject=f_region;
random intercept / subject=family_id(f_region);
run; Best, Malene
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09-23-2020
09:57 AM
Thanks again! Yes you are right there will probably be to few observations within each family_id. I am not sure what you mean by "roll up" the number of cases? Best, Malene
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09-23-2020
08:20 AM
Thank you very much for your reply! I have another question also 🙂 Some of the individuals in my cohort might be siblings so I think that family should be added as another level to the multilevel model because children from the same family are not "independent" observations so I created a variable family_id in which members of the same family have the same family_id and i tried to add this to the syntax as random intercept / subject=family_id (see below) but I get an error...so how can i incorporate it as another level? Proc glimmix data=temp method=quad; class toxin yeargrp SES f_region; model case=toxin yeargrp SES f_region/ dist=poisson link=log offset=lnpyrs covb cl solution; random intercept / subject=f_region; random intercept / subject=family_id; run; Best regards, Malene
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09-23-2020
04:02 AM
Hi, i am investigating individuals being exposed to environmental toxins in early childhood and the risk of development of ADHD. It is a population based cohort study and I did a survival analysis using Poisson regression in proc genmod. Something like this: proc genmod data=temp order=internal; class toxin yeargrp SES; model case=toxin yeargrp SES / dist=poisson link=log offset=lnpyrs type3 covb lrci; run; (lnpyrs are person years) However, I have been made aware that the data have a multi-level structure (subjects within different regions, below the variable f_region), and the analyses should take account of this. I found that piecewise exponential survival models partition the duration of follow-up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. This is equivalent to a Poisson regression model, so I think I should be able to use Proc glimmix and i ran this syntax. Proc glimmix data=temp method=quad; class toxin yeargrp SES f_region; model case=toxin yeargrp SES f_region/ dist=poisson link=log offset=lnpyrs covb cl solution; random intercept / subject=f_region; run; But I am really not sure if this is right?, does the subject=f_region mean that that I actually take the region into account? I hope to hear from some of you 🙂 Best regards, Malene
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