topic Re: Help needed with Proc Logistic in Statistical Procedures

Help needed with Proc Logistic

MatSof — Fri, 19 Dec 2014 15:07:22 GMT

Dear all.

I hope someone can help me with a quit basic question with regards to the PROC LOGISTIC procedure:

I want to calculate an adjusted Odds Ratio. The variables that I want to use for the calculation are 1) Cardiovascular disease: yes /no 2) smoking: yes/no and 3) age 16 years-100 years+

I want to calculate the OR of being a smoker, when having a cardiovascular disease and I want to adjust this Odds Ratio estimate for age.

I have tried the following approach:

Proc logistic data=cardiosmoke;

Model cardiovaskulardisease=smoking age;

Run;

But I am not sure whether this code is the correct one? I would be very grateful if anyone could help me with an example of how to correctly use the PROC LOGISTIC to do this calculation??

Thank you for your time

Re: Help needed with Proc Logistic

mohamed_zaki — Fri, 19 Dec 2014 16:47:08 GMT

Check example two in

How can I get "adjusted" predicted values from a logistic model in SAS?

Re: Help needed with Proc Logistic

StatDave — Fri, 19 Dec 2014 20:13:08 GMT

If you want to compare the odds of being a smoker in the cardiovascular=yes group to the odds in the cardiovascular=no group, then SMOKING is the response variable. Assuming variables SMOKING and CVD with values "yes" and "no":

proc logistic;

class cvd(ref="no") / param=ref;

model smoking(event="yes") = cvd age;

run;

Note the use of the EVENT= option so that you are modeling the probability of SMOKING=yes rather than no. This assures that the odds ratio will be using the odds of SMOKING=yes rather than no. Also the use of the REF= option so that the CVD=no group is in the denominator of the odds ratio. The Odds Ratio Estimates table will then label the odds ratio for CVD as "CVD yes vs no" and the note under the Response Profile table will show "Probability modeled is SMOKING=yes". It is important to use these options so that you don't use the odds of the wrong level or put the wrong group in the denominator of the odds ratio.

Re: Help needed with Proc Logistic

MatSof — Sat, 20 Dec 2014 08:47:42 GMT

Dear Dave,

Thank you very much for your response - it has been of great help. I have one last question concerning the output. The following is the results I get when running the code:

proc logistic data=sasuser.cvdsmoking;

class CVD(ref="1") /param=ref;

Model Smoking(event ="2")= CVD age;

RUN;

1= No, 2 = Yes

The LOGISTIC Procedure

Model Information

Data Set SASUSER.cvdsmoking

Response Variable smoking

Number of Response Levels 2

Model binary logit

Optimization Technique Fisher's scoring

Number of Observations Read 78702

Number of Observations Used 69113

Response Profile

Ordered Total

Value smoking Frequency

1 1 54002

2 2 15111

Probability modeled is smoking=2.

NOTE: 9589 observations were deleted due to missing values for the response or explanatory

variables.

Class Level Information

Design

Class Value Variables

Cvd 1 0

2 1

Model Convergence Status

Convergence criterion (GCONV=1E-8) satisfied.

Model Fit Statistics

Intercept

Intercept and

Criterion Only Covariates

AIC 72596.045 65900.426

SC 72605.189 65927.856

-2 Log L 72594.045 65894.426

The LOGISTIC Procedure

Testing Global Null Hypothesis: BETA=0

Test Chi-Square DF Pr > ChiSq

Likelihood Ratio 6699.6191 2 <.0001

Score 6494.7233 2 <.0001

Wald 5916.4069 2 <.0001

Type 3 Analysis of Effects

Wald

Effect DF Chi-Square Pr > ChiSq

Cvd 1 15.4882 <.0001

Age 1 5742.7223 <.0001

Analysis of Maximum Likelihood Estimates

Standard Wald

Parameter DF Estimate Error Chi-Square Pr > ChiSq

Intercept 1 0.7702 0.0266 839.6578 <.0001

Cvd 2 1 -0.2357 0.0599 15.4882 <.0001

Age 1 -0.0446 0.000589 5742.7223 <.0001

Odds Ratio Estimates

Point 95% Wald

Effect Estimate Confidence Limits

Cvd 2 vs 1 0.790 0.703 0.888

Age 0.956 0.955 0.957

Association of Predicted Probabilities and Observed Responses

Percent Concordant 69.4 Somers' D 0.401

Percent Discordant 29.3 Gamma 0.407

Percent Tied 1.3 Tau-a 0.137

Pairs 816024222 c 0.701

I just want to make sure I am using the correct output. I would think that the OR adjusted for age is the highlighted numbers: OR=0.95695%CI:0.955;0.957 p.-value <.0001

Would that be a correct assumption? Or is it the result above the highlighted called ¨Cvd 2 vs 1¨ with an OR of 0.79?

Best Regards Mathilde

Re: Help needed with Proc Logistic

psj2 — Sat, 20 Dec 2014 19:11:53 GMT

Hi Mathilde, it's the latter idea: The OR for CVD (yes vs no) of being a smoker adjusted for age can be read in the first line: It's 0.790 (95% CI 0.703 - 0.888). The highlighted part shows the corresponding numbers for an increase in 1 unit of age (adjusted for CVD) (probably its a numeric variable with unit "years", so it's the OR for an increase of 1 year of age adjusted for CVD).

Re: Help needed with Proc Logistic

Reeza — Sat, 20 Dec 2014 19:52:47 GMT

Adding on to what StatDave has mentioned, this is the unadjusted rate, without age

proc logistic;

class cvd(ref="no") / param=ref;

model smoking(event="yes") = cvd;

run;

And this is the adjusted rate, by including age:

proc logistic;

class cvd(ref="no") / param=ref;

model smoking(event="yes") = cvd age;

run;

The odds ratio you want is for CVD, and then you should compare the unadjusted to adjusted to see the impact but mostly that's to be aware of the issue, not for any statistical purpose.

Re: Help needed with Proc Logistic

MatSof — Mon, 22 Dec 2014 10:25:45 GMT

Thank you very much for your reply - it has been very helpful.