Programming the statistical procedures from SAS

Absurd value for odds ratio in logisitc regression output.

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Absurd value for odds ratio in logisitc regression output.

Hi there,

I have run a multivariate logistic regression model and one of the odds ratio values I have got it is

<0.001   (<0.001- >999.999)

I am not sure what this means and why I have got such a value. Does this mean there is something wrong with my  model. Is this a sample size issue? The original sample size is 914 and after weighting it comes to be 4500

The model is as below. Greatly appreciate any help or advice.

With such absurd value, can I still use the results?

proc logistic data =library.nismipostcath2 descending ;

 

class died  race1(ref=first)  ZIPINC_QRTL(ref=first) dm_all (ref=first) cm_htn_c (ref=first)  morbidobesity (ref=first) hyperlipidemia (ref=first) cm_perivasc (ref=first) chf (ref=first) afib (ref=first) liverdisease(ref=first) ckd(ref=first)/param=ref;

model  died=  age  race1 ZIPINC_QRTL dm_all cm_htn_c morbidobesity hyperlipidemia cm_perivasc chf afib liverdisease ckd ;

  

where pci_postcath=1 and postcathcompli1=1 and female=1;

weight discwt;

title 'Logi Reg in-hosp mortality in POST-PCI-  MI women  with post cath complications ';

  run;

  quit;

Some of the odds ratio values are as below.

Diabetes
  Mellitus
1.36 (1.05 - 1.77)0.02
Hypertension1.09 (0.83 - 1.42)0.5364
Morbid
  Obesity
<0.001 (<0.001- >999.9)0.9764
Hyperlipidemia0.43 (0.33 - 0.55)<0.0001
Perivascular disorders1.81 (1.41- 2.34)<0.0001
Chronic
  Heart Failure
4.11 (1.43 - 11.85)0.0088
Atrial
  Fibrillation
1.61 (1.21 - 2.15)0.001
Liver
  Disease
1.57 (0.63 - 3.94)0.3335
Chronic
  Kidney Disease
0.98 (0.72 - 1.35)0.9226

Thanks much!

Ashwini


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‎02-20-2013 03:59 PM
Super User
Posts: 18,583

Re: Absurd value for odds ratio in logisitc regression output.

From what I saw yesterday it seems like if you're using weights for sampling then you need to use proc surveylogistic not proc logistic.

Additionally you do have estimates of negative infinity to infinity when you have a  very small numbers in that particular variable (check your table of characteristics or table 1 or run a proc freq to verify).

Usually if it's another reason SAS will issue a warning in the log or the output.

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Solution
‎02-20-2013 03:59 PM
Super User
Posts: 18,583

Re: Absurd value for odds ratio in logisitc regression output.

From what I saw yesterday it seems like if you're using weights for sampling then you need to use proc surveylogistic not proc logistic.

Additionally you do have estimates of negative infinity to infinity when you have a  very small numbers in that particular variable (check your table of characteristics or table 1 or run a proc freq to verify).

Usually if it's another reason SAS will issue a warning in the log or the output.

Regular Contributor
Posts: 173

Re: Absurd value for odds ratio in logisitc regression output.

Hi there

Thanks  for our response!

It seems that I cannot increase the sample size or change the restrive criteria.

What I wonder is if it is still alright to use these results, that I posted in the original post. Morbid obesity is one of the independent variables in the model, my interest variable is diabetese and hypertension. I hope there is no harm using these results.

Appreciate your response.

Super User
Posts: 18,583

Re: Absurd value for odds ratio in logisitc regression output.

Take out morbid obesity and see if the results change.

Respected Advisor
Posts: 2,655

Re: Absurd value for odds ratio in logisitc regression output.

I think you are missing a critical point.  When there are sampling weights attached to the design, PROC LOGISTIC does not give the correct results.  You MUST switch to PROC SURVEYLOGISTIC to get meaningful results.  See many of @Reeza's responses to your question.

Steve Denham

Regular Contributor
Posts: 173

Re: Absurd value for odds ratio in logisitc regression output.

Thanks for your response Reeza and for suggesting me using Proc surveylogistic!. I did switch to proc surveylogistic; good thing- my results haven't changed at all.

**I am curious to know why surveylogistic is more meaningful than logistic where we are able to add the weight statement and get the SAME results as suyveylogistic? What and how does proc surveylogistic do differentlythat logisitc while analysing the weights?

** I read on the sasupport that PROC SURVEYLOGISTIC incorporates complex survey sample designs, including designs with stratification, clustering, and unequal weighting. Would you please explain what unequal weighting refers to?

** So does it finally mean that whenever we are analysing any unweighted database, proc surveylogisitc is a better choice over proc logistic?

Appreciate your response

Thanks

Ashwini

Super User
Posts: 18,583

Re: Absurd value for odds ratio in logisitc regression output.

You shouldn't get the same results. Check the standard error and the confidence intervals of your odds ratio, though the parameter estimates are probably fine. 

The SAS Docs specify, in the weight statement for proc logistic, that it does not calculate the variance properly, to use surveylogistic instead. 

I don't know what unequal weighting means.

Regular Contributor
Posts: 152

Re: Absurd value for odds ratio in logisitc regression output.

The odds ratio of zero for the variable, MORBIDOBESITY, implies that none of the study subjects meeting the WHERE condition of

     PCI_POSTCATH=1 and POSTCATHCOMPLI1=1 and FEMALE=1.

died.  So, this is a sample size issue.

You can include the MODEL statement option, FIRTH, following a forward slash ("/") after the independent variables to see one approach for dealing with this issue.  Another approach is to increase your sample size.  A third approach is to make your WHERE condition less restrictive.

I agree with Reeza that you should use PROC SURVEYLOGISTIC instead of PROC LOGISTIC when your sample respondents have weights.

Regular Contributor
Posts: 173

Re: Absurd value for odds ratio in logisitc regression output.

Hi there

Thanks  for our response!

It seems that I cannot increase the sample size or change the restrive criteria.

What I wonder is if it is still alright to use these results, that I posted in the original post. Morbid obesity is one of the independent variables in the model, my interest variable is diabetese and hypertension. I hope there is no harm using these results.

Appreciate your response.

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