Re: Main and interaction effect in Cox Prop Hazards

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Re: Main and interaction effect in Cox Prop Hazards

jaycamh
Hello,
I need help.

I am doing a Cox proportional hazards model in SPSS. I have Low SES and Heavy drinking as 2 main effects and LOW SES x Heavy drinking as interaction effect in the model.

We have stronger (and significant) effects for each main effect but the interaction term is coming protective which is against the theory. Not sure if I am doing it wrong ... see the SPSS result below.

I have, Low SES 0 is reference; 1=low SES

Heavy drinking 0 is reference; 1=heavy drinking.


in fact when I make interaction of alcohol and smoking it’s also coming protective...
Is my Interpretation wrong or is there any other way my syntax should have been formed.


Variables in the Equation

 

B

SE

Wald

df

Sig.

Exp(B)

95.0% CI for Exp(B)

 

Lower

Upper

AGE

.085

.000

3.719E4

1

.000

1.089

1.088

1.090

SEX - FEMALE

-.433

.013

1.179E3

1

.000

.649

.633

.665

BMI

.001

.001

6.844

1

.009

1.001

1.000

1.003

YEAR

-.006

.002

15.634

1

.000

.994

.990

.997

smoker_cfn

 

 

2.716E3

2

.000

 

 

 

smoker_cfn(1)

.827

.016

2.600E3

1

.000

2.285

2.214

2.359

smoker_cfn(2)

.223

.015

221.735

1

.000

1.250

1.214

1.287

heavy_drinker

.566

.110

26.427

1

.000

1.761

1.419

2.186

educ_lowses

.393

.016

608.737

1

.000

1.481

1.436

1.528

heavy_drinker*educ_lowses

-.094

.116

.656

1

.418

.911

.726

1.






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Re: Main and interaction effect in Cox Prop Hazards

Bruce Weaver
Administrator
In Nabble, at least, your table was unreadable.  But by coping it, pasting
into Word, and saving as a PDF, I was able to make a readable version.
There should be a link to it below.  (Members who do not use Nabble may need
to view this thread in Nabble to get access to it:
http://spssx-discussion.1045642.n5.nabble.com/Re-Main-and-interaction-effect-in-Cox-Prop-Hazards-td5738390.html.)

Variables_in_the_Equation.pdf
<http://spssx-discussion.1045642.n5.nabble.com/file/t7186/Variables_in_the_Equation.pdf>  


In a regression model, the terms that you are calling main effects are
really simple effects.  E.g., the coefficient for heavy_drinker shows the
effect of heavy drinking when educ_lowses is set to its reference category.
To get the effect of heavy drinking in the other category of educ_lowses,
add the coefficient for the interaction.  

Similarly, the coefficient for low_ses shows the effect of low_ses when
heavy_drinker is set to its reference category.  To get the low_ses effect
for the other category of heavy_drinker, add the coefficient for the
interaction.  

I worked out those effects in Excel.  Not sure how good the formatting will
be, but I'll paste the results here:

B Heavy Drinking
Low SES Yes No
Yes 0.299 0.393
No 0.566 0.472

If you exponentiate those numbers, you'll get the corresponding Exp(B)
values:

Exp(B) Heavy Drinking
Low SES Yes No
Yes 1.349 1.481
No 1.761 1.603

Finally, the test on the interaction is nowhere near significance (p =
.418).  So unless there is a good theory-based reason for keeping it in the
model, I would consider removing it.  

HTH.



jaycamh wrote

> Hello,
>> I need help.
>>
>> I am doing a Cox proportional hazards model in SPSS. I have Low SES and
>> Heavy drinking as 2 main effects and LOW SES x Heavy drinking as
>> interaction effect in the model.
>>
>> We have stronger (and significant) effects for each main effect but the
>> interaction term is coming protective which is against the theory. Not
>> sure if I am doing it wrong ... see the SPSS result below.
>>
>> I have, Low SES 0 is reference; 1=low SES
>>
>> Heavy drinking 0 is reference; 1=heavy drinking.
>>
>>
>> in fact when I make interaction of alcohol and smoking it’s also coming
>> protective...
>> Is my Interpretation wrong or is there any other way my syntax should
>> have been formed.
>>
>>
>
> Variables in the Equation
>  
> B
> SE
> Wald
> df
> Sig.
> Exp(B)
> 95.0% CI for Exp(B)
>  
> Lower
> Upper
> AGE
> .085
> .000
> 3.719E4
> 1
> .000
> 1.089
> 1.088
> 1.090
> SEX - FEMALE
> -.433
> .013
> 1.179E3
> 1
> .000
> .649
> .633
> .665
> BMI
> .001
> .001
> 6.844
> 1
> .009
> 1.001
> 1.000
> 1.003
> YEAR
> -.006
> .002
> 15.634
> 1
> .000
> .994
> .990
> .997
> smoker_cfn
>  
>  
> 2.716E3
> 2
> .000
>  
>  
>  
> smoker_cfn(1)
> .827
> .016
> 2.600E3
> 1
> .000
> 2.285
> 2.214
> 2.359
> smoker_cfn(2)
> .223
> .015
> 221.735
> 1
> .000
> 1.250
> 1.214
> 1.287
> heavy_drinker
> .566
> .110
> 26.427
> 1
> .000
> 1.761
> 1.419
> 2.186
> educ_lowses
> .393
> .016
> 608.737
> 1
> .000
> 1.481
> 1.436
> 1.528
> heavy_drinker*educ_lowses
> -.094
> .116
> .656
> 1
> .418
> .911
> .726
> 1.
>
>>
>>
>>
>>
>
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-----
--
Bruce Weaver
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http://sites.google.com/a/lakeheadu.ca/bweaver/

"When all else fails, RTFM."

NOTE: My Hotmail account is not monitored regularly.
To send me an e-mail, please use the address shown above.

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=====================
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Bruce Weaver
bweaver@lakeheadu.ca
http://sites.google.com/a/lakeheadu.ca/bweaver/

"When all else fails, RTFM."

NOTE: My Hotmail account is not monitored regularly.
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Re: Main and interaction effect in Cox Prop Hazards

Rich Ulrich
In reply to this post by jaycamh
(I'm reading the distribution from SPSSX-L directly, ,using Outlook, and the table is formatted fine for me.)

No - you do not have an effect worth mentioning for the interaction that you have coded.

In fact, it is somewhat impressive that the Wald chi-squared for it is less than 1; it is not
even picking up trivial, artifactual contributions from anywhere.

Do you know how to read those "Wald" numbers that end in E?  For Age, Wald is 3.719E4.
"3.719E4"  is a notation where "E4" stands for "10 to the 4th":  move the decimal over
by 4 places, so the actual number is approximately 37,190.  Apparently your sample size
is "hundreds of thousands" for it to generate a chi-squared type of test that is that large.

Just like with any ANOVA table, your emphasis should be on the larger tests values when
their sizes vary by orders of magnitude.  Why?  For example:  If your "age" is at all confounded with
BMI (which it probably is), then it is easy to imagine that the /observed/ BMI effect, Wald= 6.844,
will disappear if there were a more complete control for Age. It would not be unreasonable,
IMHO, to "look at" a re-run of the test where Age is entered as dozens of separate years, as dummy
categories, so that there are K-1  d.f.  for age variables (including "linear") where K is the number of
separate ages.   - Looking at the effects on other coefficients will give you /illustrations/ of how
the largest effects can create artifacts.

I'm not sure what Bruce was saying about coefficients. 

--
Rich Ulrich


From: SPSSX(r) Discussion <[hidden email]> on behalf of i Jay <[hidden email]>
Sent: Friday, September 20, 2019 12:53 PM
To: [hidden email] <[hidden email]>
Subject: Re: Main and interaction effect in Cox Prop Hazards
 
Hello,
I need help.

I am doing a Cox proportional hazards model in SPSS. I have Low SES and Heavy drinking as 2 main effects and LOW SES x Heavy drinking as interaction effect in the model.

We have stronger (and significant) effects for each main effect but the interaction term is coming protective which is against the theory. Not sure if I am doing it wrong ... see the SPSS result below.

I have, Low SES 0 is reference; 1=low SES

Heavy drinking 0 is reference; 1=heavy drinking.


in fact when I make interaction of alcohol and smoking it’s also coming protective...
Is my Interpretation wrong or is there any other way my syntax should have been formed.


Variables in the Equation

 

B

SE

Wald

df

Sig.

Exp(B)

95.0% CI for Exp(B)

 

Lower

Upper

AGE

.085

.000

3.719E4

1

.000

1.089

1.088

1.090

SEX - FEMALE

-.433

.013

1.179E3

1

.000

.649

.633

.665

BMI

.001

.001

6.844

1

.009

1.001

1.000

1.003

YEAR

-.006

.002

15.634

1

.000

.994

.990

.997

smoker_cfn

 

 

2.716E3

2

.000

 

 

 

smoker_cfn(1)

.827

.016

2.600E3

1

.000

2.285

2.214

2.359

smoker_cfn(2)

.223

.015

221.735

1

.000

1.250

1.214

1.287

heavy_drinker

.566

.110

26.427

1

.000

1.761

1.419

2.186

educ_lowses

.393

.016

608.737

1

.000

1.481

1.436

1.528

heavy_drinker*educ_lowses

-.094

.116

.656

1

.418

.911

.726

1.






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===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
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Re: Main and interaction effect in Cox Prop Hazards

Bruce Weaver
Administrator
Rich Ulrich wrote
> I'm not sure what Bruce was saying about coefficients.

I'll try again.  Here are the B values for the two variables of interest and
their interaction:

B(drink) = 0.566 = ln(HR) for drink when ses = 0 (i.e., reference category)
B(ses) = 0.393 = ln(HR) for ses when drink = 0 (i.e., reference category)

B(interaction) = -0.094

Therefore:
0.566 -0.094 = 0.472 = ln(HR) for drink when ses = 1
0.393 -0.094 = 0.299 = ln(HR) for ses when drink = 1

The interaction term tests the differences between 0.566 and 0.472 (for
drink) and between 0.393 and 0.299 (for ses).  

And of course, those ln(HR) values are adjusted for the other variables in
the model.  

Is this clearer?  




-----
--
Bruce Weaver
[hidden email]
http://sites.google.com/a/lakeheadu.ca/bweaver/

"When all else fails, RTFM."

NOTE: My Hotmail account is not monitored regularly.
To send me an e-mail, please use the address shown above.

--
Sent from: http://spssx-discussion.1045642.n5.nabble.com/

=====================
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[hidden email] (not to SPSSX-L), with no body text except the
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--
Bruce Weaver
bweaver@lakeheadu.ca
http://sites.google.com/a/lakeheadu.ca/bweaver/

"When all else fails, RTFM."

NOTE: My Hotmail account is not monitored regularly.
To send me an e-mail, please use the address shown above.
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Re: Main and interaction effect in Cox Prop Hazards

jaycamh
Thank you Bruce.

Sent from my iPhone

> On Sep 20, 2019, at 6:02 PM, Bruce Weaver <[hidden email]> wrote:
>
> Rich Ulrich wrote
>> I'm not sure what Bruce was saying about coefficients.
>
> I'll try again.  Here are the B values for the two variables of interest and
> their interaction:
>
> B(drink) = 0.566 = ln(HR) for drink when ses = 0 (i.e., reference category)
> B(ses) = 0.393 = ln(HR) for ses when drink = 0 (i.e., reference category)
>
> B(interaction) = -0.094
>
> Therefore:
> 0.566 -0.094 = 0.472 = ln(HR) for drink when ses = 1
> 0.393 -0.094 = 0.299 = ln(HR) for ses when drink = 1
>
> The interaction term tests the differences between 0.566 and 0.472 (for
> drink) and between 0.393 and 0.299 (for ses).  
>
> And of course, those ln(HR) values are adjusted for the other variables in
> the model.  
>
> Is this clearer?  
>
>
>
>
> -----
> --
> Bruce Weaver
> [hidden email]
> http://sites.google.com/a/lakeheadu.ca/bweaver/
>
> "When all else fails, RTFM."
>
> NOTE: My Hotmail account is not monitored regularly.
> To send me an e-mail, please use the address shown above.
>
> --
> Sent from: http://spssx-discussion.1045642.n5.nabble.com/
>
> =====================
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> [hidden email] (not to SPSSX-L), with no body text except the
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Re: Main and interaction effect in Cox Prop Hazards

Rich Ulrich
In reply to this post by Bruce Weaver
Bruce,
I hate it when interactions have to be interpreted, especially if it
is possible that the program generated them and I don't have control.
Also, telling a program that this is a "factor" and not a continuous
"variable" raises the problem that, for 0/1,  if the program takes "1" as
the reference group, it reverses the sign of the B obtained. PITA.

Further:  if two variables are coded as 0/1  and the interaction is their
simple product - instead of the "centered" product - then the interaction's
B is correlated with the main-effects' B's, and affects their magnitude in
the equation that has them all.

I recognize that this OP needs basic help with interpretation of the model,
which you provided.  I was skipping that, because the main thing relevant
to the question is that there /is/  no interaction effect worth worrying
about, according the Wald test.

--
Rich Ulrich


From: SPSSX(r) Discussion <[hidden email]> on behalf of Bruce Weaver <[hidden email]>
Sent: Friday, September 20, 2019 6:02 PM
To: [hidden email] <[hidden email]>
Subject: Re: Main and interaction effect in Cox Prop Hazards
 
Rich Ulrich wrote
> I'm not sure what Bruce was saying about coefficients.

I'll try again.  Here are the B values for the two variables of interest and
their interaction:

B(drink) = 0.566 = ln(HR) for drink when ses = 0 (i.e., reference category)
B(ses) = 0.393 = ln(HR) for ses when drink = 0 (i.e., reference category)

B(interaction) = -0.094

Therefore:
0.566 -0.094 = 0.472 = ln(HR) for drink when ses = 1
0.393 -0.094 = 0.299 = ln(HR) for ses when drink = 1

The interaction term tests the differences between 0.566 and 0.472 (for
drink) and between 0.393 and 0.299 (for ses). 

And of course, those ln(HR) values are adjusted for the other variables in
the model. 

Is this clearer? 




-----
--
Bruce Weaver
[hidden email]
http://sites.google.com/a/lakeheadu.ca/bweaver/

"When all else fails, RTFM."

NOTE: My Hotmail account is not monitored regularly.
To send me an e-mail, please use the address shown above.

--
Sent from: http://spssx-discussion.1045642.n5.nabble.com/

=====================
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command. To leave the list, send the command
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===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
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Re: Main and interaction effect in Cox Prop Hazards

Bruce Weaver
Administrator
Fair points, Rich.  It would be helpful if the OP posted the syntax.  

Also, if I'm using a command that allows factor variables, if it also has
EMMEANS, I like to generate the fitted values (and pairwise contrasts)
corresponding to the interaction term to make sure I've got things right.


Rich Ulrich wrote

> Bruce,
> I hate it when interactions have to be interpreted, especially if it
> is possible that the program generated them and I don't have control.
> Also, telling a program that this is a "factor" and not a continuous
> "variable" raises the problem that, for 0/1,  if the program takes "1" as
> the reference group, it reverses the sign of the B obtained. PITA.
>
> Further:  if two variables are coded as 0/1  and the interaction is their
> simple product - instead of the "centered" product - then the
> interaction's
> B is correlated with the main-effects' B's, and affects their magnitude in
> the equation that has them all.
>
> I recognize that this OP needs basic help with interpretation of the
> model,
> which you provided.  I was skipping that, because the main thing relevant
> to the question is that there /is/  no interaction effect worth worrying
> about, according the Wald test.
>
> --
> Rich Ulrich





-----
--
Bruce Weaver
[hidden email]
http://sites.google.com/a/lakeheadu.ca/bweaver/

"When all else fails, RTFM."

NOTE: My Hotmail account is not monitored regularly.
To send me an e-mail, please use the address shown above.

--
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--
Bruce Weaver
bweaver@lakeheadu.ca
http://sites.google.com/a/lakeheadu.ca/bweaver/

"When all else fails, RTFM."

NOTE: My Hotmail account is not monitored regularly.
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Re: Main and interaction effect in Cox Prop Hazards

jaycamh
Hi Bruce:

Here is the syntax.

COXREG event
   /STATUS=dead(1)
   /CONTRAST (sex)=Indicator(1)
   /CONTRAST (smoker_cfn)=Indicator
   /CONTRAST (HEAVY_DRINKER)=Indicator(1)
        /CONTRAST (educ_Lowses)=Indicator(1)
   /CONTRAST (HED)=Indicator(1)
/METHOD=ENTER age sex bmi year smoker_cfn  educ_lowses HEAVY_DRINKER HED HEAVY_DRINKER*educ_lowses
  /PRINT=CI(95)
   /CRITERIA=PIN(.05) POUT(.10) ITERATE(20).



Here is how the reference groups are set.


Categorical Variable Codingsb,c,d,e,f
Frequency (1) (2)
SEXa 1=Male 155307 0
2=Female 159312 1
smoker_cfna 1.00=Current 79914 1 0
2.00=Former 74646 0 1
3.00=Never 160059 0 0
heavy_drinkera .00=light/medium 308903 0
1.00=heavy/very heavy 5716 1
educ_lowsesa 0 94250 0
1 220369 1
HEDa 3.00=Current 291130 0
4.00=Heavy Episodic 23489 1
a. Indicator Parameter Coding
b. Category variable: SEX (Sex)
c. Category variable: smoker_cfn
d. Category variable: heavy_drinker
e. Category variable: educ_lowses
f. Category variable: HED



OUTPUT:
Variables in the Equation
B SE Wald df Sig. Exp(B) 95.0% CI for Exp(B)
Lower Upper
AGE 0.085579 0.000447 36726.27 1 0 1.089348 1.088395 1.090302
SEX -0.42074 0.01272 1094.055 1 6.5E-240 0.65656 0.640393 0.673134
BMI 0.001397 0.000552 6.410411 1 0.011345 1.001398 1.000316 1.002482
YEAR -0.00649 0.001627 15.92481 1 6.59E-05 0.993529 0.990367 0.996702
smoker_cfn 2640.209 2 0
smoker_cfn(1) 0.818795 0.016262 2535.139 1 0 2.267765 2.196624 2.34121
smoker_cfn(2) 0.223798 0.014984 223.0764 1 1.93E-50 1.250819 1.214618 1.288098
educ_lowses 0.385774 0.015961 584.1816 1 4.6E-129 1.470753 1.425455 1.517489
heavy_drinker 0.469307 0.110958 17.88943 1 2.34E-05 1.598886 1.286382 1.987308
HED 0.219843 0.029364 56.05265 1 7.06E-14 1.245881 1.176202 1.319688
heavy_drinker*educ_lowses -0.1332 0.115647 1.326699 1 0.249393 0.875286 0.697768 1.097966

On Sun, Sep 22, 2019 at 8:58 AM Bruce Weaver <[hidden email]> wrote:
Fair points, Rich.  It would be helpful if the OP posted the syntax. 

Also, if I'm using a command that allows factor variables, if it also has
EMMEANS, I like to generate the fitted values (and pairwise contrasts)
corresponding to the interaction term to make sure I've got things right.


Rich Ulrich wrote
> Bruce,
> I hate it when interactions have to be interpreted, especially if it
> is possible that the program generated them and I don't have control.
> Also, telling a program that this is a "factor" and not a continuous
> "variable" raises the problem that, for 0/1,  if the program takes "1" as
> the reference group, it reverses the sign of the B obtained. PITA.
>
> Further:  if two variables are coded as 0/1  and the interaction is their
> simple product - instead of the "centered" product - then the
> interaction's
> B is correlated with the main-effects' B's, and affects their magnitude in
> the equation that has them all.
>
> I recognize that this OP needs basic help with interpretation of the
> model,
> which you provided.  I was skipping that, because the main thing relevant
> to the question is that there /is/  no interaction effect worth worrying
> about, according the Wald test.
>
> --
> Rich Ulrich





-----
--
Bruce Weaver
[hidden email]
http://sites.google.com/a/lakeheadu.ca/bweaver/

"When all else fails, RTFM."

NOTE: My Hotmail account is not monitored regularly.
To send me an e-mail, please use the address shown above.

--
Sent from: http://spssx-discussion.1045642.n5.nabble.com/

=====================
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===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
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Re: Main and interaction effect in Cox Prop Hazards

Rich Ulrich
In reply to this post by jaycamh
By the way - I would like to add - do not present the Age effect, when you get that far,
as the B coefficient = 1.089.  That is the increase in the OR for risk for a /single/ year.  As a
"risk", it is not nearly commensurate with the values of the risks you produce for your
dichotomous items.

There are a couple of approaches for handling this.  One that does not depend on the
range of ages in the sample is run the run with an alternate version of age, like,  AGE10 = AGE/10 .
The test-statistics remain exactly the same, but the new "effect" is the risk per decade of age,
and will be 2.346 (if I've picked the right formula - raise 1.089 to the power of the number
of years, which is 10). 

The approach which gives values that are even more commensurate to the dichotomies is
to compare the risk at the range of plus-and-minus one standard deviation of the age range. 
How many years?  - raise 1.089 to that power.  That one would do more to emphasize that
Age is by far the strongest effect according to the Wald tests.

--
Rich Ulrich

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Re: Main and interaction effect in Cox Prop Hazards

Martin Holt-3
In reply to this post by Bruce Weaver
Bruce

"RTFM"

The Manual says that the best way forward is to use the Google Group "MedStats".

(I'm biased....I'm the Founder :))

Kind Regards

Martin 

Freelance Medical Statistician

If you can't explain it simply, you don't understand it well enough.....Einstein


Concise

Encyclopedia

of Biostatistics for

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Martin P. Holt

https://www.crcpress.com/Concise-Encyclopedia-of-Biostatistics-for-Medical-Professionals/Indrayan-Holt/9781482243871


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On Saturday, 21 September 2019, 00:15:41 BST, Bruce Weaver <[hidden email]> wrote:


Rich Ulrich wrote
> I'm not sure what Bruce was saying about coefficients.

I'll try again.  Here are the B values for the two variables of interest and
their interaction:

B(drink) = 0.566 = ln(HR) for drink when ses = 0 (i.e., reference category)
B(ses) = 0.393 = ln(HR) for ses when drink = 0 (i.e., reference category)

B(interaction) = -0.094

Therefore:
0.566 -0.094 = 0.472 = ln(HR) for drink when ses = 1
0.393 -0.094 = 0.299 = ln(HR) for ses when drink = 1

The interaction term tests the differences between 0.566 and 0.472 (for
drink) and between 0.393 and 0.299 (for ses). 

And of course, those ln(HR) values are adjusted for the other variables in
the model. 

Is this clearer? 




-----
--
Bruce Weaver
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"When all else fails, RTFM."

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