About baseballyanks1

baseballyanks1 · ‎08-17-2020

Fair suggestion. I've plotted log_X for the binary outcome (disease/no disease) as well as against age but nothing seems obviously out of the ordinary, unless I'm missing something.

baseballyanks1 · ‎08-15-2020

Just ran proc logistic data = WORK.disease; model strata1(event='1') = log_X / firth; run; proc logistic data = WORK.disease; model strata1(event='1') = log_X; run; Still getting the ">999.99" issue. I can include a unit statement, but the Firth model (unless my code is wrong) didn't seem to do much.

baseballyanks1 · ‎08-15-2020

Yeah, it seemed odd to me, I've never seen this issue with a continuous variable like this with a sample size at a decent size of this dataset. Thanks so much for that suggestion, I'll take a look now and will try to run it!

baseballyanks1 · ‎08-15-2020

Thanks for the heads up! In the case presented on that link, there were a severely limited number of data points (3 observations). But for the continuous variable log_X there are no missing data points (n=236) without any clear outliers. Despite having a similar profile, Log_Y doesn't yield such an extreme OR.

baseballyanks1 · ‎08-15-2020

I tried KSharp's suggestion of including a Units statement, which yields actual values (79.685, 95% CI 24.050-318.803). As you said, do you think it's so high due to the spread of the data for log_X (I.e. only encompassing a 1-unit change) as opposed to log_Y or could something else be going on to drive it?

baseballyanks1 · ‎08-15-2020

Thanks for the suggestion, when I include a units statements I do get actual values for log_x OR: 79.685 with 95% CI 24.050-318.803

baseballyanks1 · ‎08-15-2020

Sorry for the late reply. The estimate for log_X is 10.3647 with a 95% CI of 7.3216-13.4078 Included more output below

baseballyanks1 · ‎08-14-2020

Thanks for the welcome message! 😄 Long time visitor, first time poster. That's what I was thinking too, about an outlier, but the data range for log_X is 3.390 to 4.421 (compared to log_Y, 1.853 to 3.346). Happy to provide any other information needed!

baseballyanks1 · ‎08-13-2020

Trying to figure out why I'm getting an absurd OR for a continuous variable log_X that does not have any missing values. Any ideas? proc logistic data=WORK.DISEASE_MM; class Gender1 (ref="0") / param=glm; model Strata1 (event='1')= gender1 log_X log_Y age/ link=logit clparm=both clodds=both alpha=0.05 technique=fisher scale=none aggregate lackfit; run; Odds Ratio Estimates and Profile-Likelihood Confidence Interverals Effect Estimate 95% CI gender1 1 vs 0 7.321 3.514 16.152 log_Y 0.617 0.075 5.004 log_X >999.999 >999.999 >999.999 Age 0.926 0.894 0.955

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Re: re: ">999.999" odds ratio in logistic regression model

Re: re: ">999.999" odds ratio in logistic regression model

Re: re: ">999.999" odds ratio in logistic regression model

Re: re: ">999.999" odds ratio in logistic regression model

Re: re: ">999.999" odds ratio in logistic regression model

Re: re: ">999.999" odds ratio in logistic regression model

Re: re: ">999.999" odds ratio in logistic regression model

Re: re: ">999.999" odds ratio in logistic regression model

re: ">999.999" odds ratio in logistic regression model

Re: re: ">999.999" odds ratio in logistic regression model

Re: re: ">999.999" odds ratio in logistic regression model

Re: re: ">999.999" odds ratio in logistic regression model

Re: re: ">999.999" odds ratio in logistic regression model

Re: re: ">999.999" odds ratio in logistic regression model

Re: re: ">999.999" odds ratio in logistic regression model

Re: re: ">999.999" odds ratio in logistic regression model

Re: re: ">999.999" odds ratio in logistic regression model

re: ">999.999" odds ratio in logistic regression model