HERC Economics - 9 -Department of Veteran Affairs

August 8, 2012

# Department of Veterans Affairs

**HERC Econometrics with Observational Data**

**Instrumental Variables Models**

**Presenter: Patsi Sinnott, PT, PhD**

**August 8, 2012**

Patsi Sinnott:  Well, good morning, afternoon, wherever you are.  We're going to talk about instrumental variable models today.  And this is essentially a continuation of — so we're going to do a brief review of causation and then talk about the IV approach and give you some examples.

This is essentially a continuation of Todd's lecture on causation.  We're going to show a little bit about testing the instruments and the limitations of the methods.  Just as background, remember that causation, randomized trial provides a structure for understanding causation.  But, of course, we are confronted all the time with reports in the news about dark chocolate affecting your health and the physical therapy following hip fracture reduces the risk of death.  The question is really, have we a control for everything that might be affecting the outcome or influencing the outcome.

In a randomized clinical trial we start by recruiting participants.  They're randomized.  One group gets treatment; one group does not.  And then we assess the outcomes in each group.  We assume that the observed and unobserved characteristics are evenly distributed between the groups so that when you infer results you are inferring them due to the treatment.  Now sometimes, just by chance alone, your groups might not be balanced so that certain differences might be present in your comparison group or your treatment group.  But generally in a randomized clinical trial you can use checks and balances to preserve the randomization.

So then in your interpretation in OLS you'll have the x's explaining the variation in y, the random error and then the randomization assumes that there's a high probability that the treatments are similar.  But in some cases randomized trials can be not practical.  It might be that the standard care is generally believed to be effective or efficacious and so to withhold treatment might be considered unethical.  The study population may not be representative of the general population.  And therefore, your results might not be generalizable and the trial that you propose is just impractical, your number of patients to recruit is just impossible or that doing the test is not scientifically justified.

One of the alternatives is observational studies.  And these are increasingly becoming useful and used because of the assumption that this is a natural experiment, there's a lot of data out there, and many observable characteristics can be included but that your non-randomized groups differ in both observed and unobserved characteristics.

And so, for example, you might have co-morbid disease illness severity, patient and provider preferences which change the way people get access to care and unknown factors in patient health status and history that are going to confound your results.  These unobserved characteristics might skew the data.  They can lead to violations of the assumptions of OLS.  They can lead to bias in the results and faulty inference of causality.

You use IV or instrumental variable approach when the randomization does not produce even distribution of characteristics.  And just to note, for those of you who listened to Todd's presentation last week about propensity scores, propensity scores are used, as I understand it, when you have observable characteristics and instrumental variables when you have unobserved characteristics that you want to control for.  

So we're going to use as an example this classic paper by McClellan, McNeil and Newhouse in JAMA, 1994.  The question is does — is mortality after acute MI a function of cardiac catheterization and other variables.  In this population of patients, they are elderly patients.  They are using Medicare claims data for the analysis.  And they're looking primarily at survival at four years after the AMI.  In this case the more intensive treatment is defined as having a cardiac cath or other cardiac procedures including percutaneous transluminal coronary angioplasty or CABG within 90 days of the acute MI.

So when you start up you look at your distribution of the observed characteristics.  And in this case what you see, these are unadjusted results, your population with no cath is more likely female, slightly older, more likely have cancer, have actually — are more likely to have all of these medical conditions including cardiovascular disease. 

Additionally, their outcomes are different.  You see that 40% of the population who have no catheterization are admitted to a cath revascularization hospital, in other words a hospital that has that capacity.  While those who have a catheterization within 90 days, 62% of them are admitted to that kind of hospital.  But significantly, what you have is one day mortality difference in the no-cath group and a four-year mortality difference of 37 percentage points difference between the two groups.  And the question is really is this due to the catheterization or when you look at the distribution of the observable characteristics are they in fact — is there something else that's happening here.  

And so then in this particular study they started with adjustments first, just a summary of the outcomes with unadjusted, then adjusted for the demographics characteristics alone, and then adjusted for demographic and co-morbidity differences.  And you see that adjusting for these observable characteristics does not substantially reduce mortality differences.  At four years you still have a 28 percentage point difference between groups and a huge variation.  This suggests that there's still undefined differences between the groups or selection bias and that you need an instrumental variable that will capture the variation in catheterization rates but not have an effect on outcome.  So just the highlight of this 28 percentage point difference in 4-year mortality between groups.

So again, the IV approach is when randomization, you use it when it doesn't produce even distribution of characteristics and when unmeasured and unobserved characteristics potentially skew the results.  

So really what you want to ask yourself is are there reasons why some people receive care and some people don't.  Are the sicker patients getting treatment?  Does distance from the hospital determine who gets treatment?  Do certain kinds of physicians or specialties prefer specialty treatment?  And do these occur both at onset of care and return for care?  So you might be looking at return visits following an incidence diagnosis.  And you gain, when you're using observational data, you want to be asking yourself are there reasons why people don't return for care after the first visit.  Are there financial issues?  Are there distance and transportation issues?  And is there an issue of other insurance?  

So having assumed that you want and need an instrumental variable and that's going to make a difference in your results, when you choose the instrumental variable you want to make sure that it has face validity, it's exogenous, it's a strong predictor of your treatment variable and is just identified, meaning that when you use instrument variables you need to make sure that there are not more IVs than exogenous variables in the model.  

Face validity really is the irrefutable relationship to the treatment.  You need to be able to argue that this is a valid instrument.  Exogeneity, you have to be able to confirm that there is no direct or indirect effect on the outcome directly; it has to be through the treatment.  So you're looking for variation in access to treatment that's affected by something that does not directly or indirectly affect the outcome.  It has to cause substantial variation in the variables interest and it needs to be just identified.  

So we're going to go back to same examples here, again, with the mortality as a function of catheterization, really the question is what's missing.  And what I like to do is use the question frame and see if people can suggest what things might affect access to this kind of treatment that's not currently in the model.  Who gets catheterization; who does not.  And I wonder if you can put those comments into the question frame.  Any comments?  

Moderator:  Okay, so the first one is people who live closer to big medical centers get catheters.  Our local supply of specialists trained in the procedure.  Distance to the teaching hospital or cardiovascular center.  Hospital types - academic, et cetera.  Distance from hospital and insured.

Patsi Sinnott:  Great.  So really what you've identified here is some of the key issues, one being distance, one being specialty services availability and then also physician training or physician specialty.  One more that I might add is that patients who are in better health might disproportionately be sent to a catheterization hospital.  So what we see is the setup for the instrument variable.  Again, we want to make sure that the instrument doesn't affect mortality directly but affect access to cardiac catheterization.  And in this particular study what they used was the differential distance to the nearest catheterization hospital.  And that was calculated as the distance from home to the cardiac cath hospital less the distance to the nearest regular medical center.  

So in terms of evaluating this as an instrument, the differential distance between the nearest  hospital and the nearest catheterization hospital, you assume that the patients with AMI would go to the nearest hospital but how close those two hospitals are would be independently predicative of catheterization for similar patients.  And that the differential distance only affects the outcomes only through the likelihood of receiving treatment passes the test of reasonability.  

Then you look at whether it's a strong predictor.  And here we have the unadjusted results compared by differential distance.  They created two classes of the population, one with a differential distance of less than 2.5 miles, less than or equal to, and one greater than 2.5 miles.  Your population is much more evenly distributed.  Your patient characteristics are much more balanced, but you see also that this differential distance is a strong predictor of whether or not you get a catheterization within 90 days.  

So what we're interested in is the results with the differential distance instrumental variable and you find that the instrument causes variation in the treatment is satisfied and based on the previous table you saw that the patients who are closer had a 6.75 percentage point greater chance of getting a cath.  

So then the next test is looking at the multiple regression results.  They included in this model patient characteristics and three IVs - the hospital volume to control for difference in the hospital size, whether the patient lived in rural versus urban location, and the differential distance IV.  Andwhat you see in these results that there's a 5 point difference that exist at day one in the one-day mortality and that that 5 point difference is fairly consistent or closely consistent throughout the outcome category.  You also see that patients who were admitted to a high volume hospital had somewhat better results in terms of mortality and those in a rural residence had a somewhat worse outcome.  So what's very interesting here is this one day mortality.  This suggests that these results are different but not due to catheterization, because the catheterization in this model occurs within 90 days, not within one day necessarily.

So the summary of the result of this particular study, unadjusted you have a 37 percentage point difference effect on four-year mortality adjusted without the IV, a 28 percentage point difference, with the differential distance IV, a 6.9% effect,.  And with the three instrumental variables, the high volume hospital, the rural hospital and the differential distance, there's approximately a five percentage point difference.

And so the interpretation is that there is a five percentage beneficial effect on mortality which occurs at day one before the procedure could have had any beneficial effect and that this is likely due to something other than the catheterization but occurs in those patients who received cath.  And this may be due to other medical care providers at the same hospital, by the staff, by some other operation, but not due to the catheterization.

So we're going to go forward and look at some other instrumental variables that are the classics in the econometrics for these kinds of models.  These two particular examples have been used extensively in describing instrumental variables - is wage a function of years of education only or are there other things involved, and is school performance a function of class size.  So in this first model we're going to look at wage as a function of years of education.  So what else is possibly influencing years of education but not directly affecting wage?  And again, could we use the question panel and see if people have suggestions about what an instrument might be that influences access to education but not — and which would affect access to education.  

Moderator:  The first one is parents' education.

Patsi Sinnott:  Right, and I guess the question is how would you measure that.  Additionally?

Moderator:  And so far that's it.

Patsi Sinnott:  Okay.

Moderator:  Oh, there's a few more.  Urban/rural neighborhood in childhood.  Socioeconomics.  Proximity to employment opportunities.  Someone else said parents' education.  Veteran with GI Bill rights.  Scholarship availability.  Willingness to relocate.  And that's it for now.

Patsi Sinnott:  Right.  And so in this case we are interested in how years of education are influencing wage.  These other factors are potentially affecting wages but are there things that affect the years of education alone?  And so, we're in this particular model, what we're looking at again is what is this model that's going to give us additional years of education of affect years of education but not the wage directly.  And in this particular case they used the distance to the nearest college which would have - the assumption is, of course, that the closer you are the more likely you are to go to college and that in itself will not affect your wages, the distance, but will have an effect on how much education you get.  

And here's another — the next one, the school performance one, that school performance is a function of class size.  Again, you're looking for an instrument that's going to affect class size directly but not school performance and because you're interested in clearly the effect of class size on school performance.  And so you might think that, again, parental involvement in the school, parents choosing the size of the school, parents working at home with the school.  All of that would directly affect school performance but will not contribute to differences in the class size such that you could study the effect of class size on school performance.  

So in this case, again, you're looking for this framework of an influence on the class size but not school performance directly.  And in this case they had a natural experiment where class sizes were changed because of growth in the school and classes were split and they could use a dummy variable for a split class in here as the instrument to identify the — to interpret the effect of class size on school performance.

So just some highlights on the inference for an instrumental variable is an RCT provides the average effects for the population eligible for the study.  The instrumental variable gives a wide angle view of the effects of treatment.  It is a more generalized or generalizable population than an RCT.  It has external validity and, again, back to testing the instruments, you want to look at face validity to make sure that it tells a good story.  Does the instrument have the expected sign and is it significant.  Can you compare it to alternative instruments if they're available.  You need to be able to defend this assumption that it's not an explanatory variable, that it doesn't have a direct effect on the outcome and you need to be able to explain why the instrument is not correlated with the omitted variable.  And for testing exogenating you want to confirm that the IV is not correlated with the regressors, with the Houseman test.  I'm sorry, you want to make sure that the instrument is uncorrelated with the error with Sargan and that if the errors are correlated with regressors with the Houseman test you want to confirm that there's a strong predictor and that the F Statistic is greater than 10 and also to test this predictor factor with the Staiger Stock.  

Again, remember that you want to make sure that it's just identified, meaning that there are not more IVs than exogenous variables in the model.  And again, a conclusion that instrumental variables mimic randomization.  They're hard to find.  It's an estimate of the marginal influence on the outcome.  You want to copy and steal everything, meaning there are not a lot of good instruments out there, so you should look at what's available but make sure that the IV from one study is going to work for your study.  

So I wanted to give you a couple of examples of IVs that are used in VA.  Slade, McCarthy, Pracht, Bass, Kim and Eisenberg all use variations on a distance to the nearest VA for treatment.  In their study the distance to the nearest VA hospital minus the distance to the nearest non-VA hospital (Helmer, Sambamoorthi); a racial mix by the enrollees and utilizers; and visit intensity for enrollees of a class.  This was to describe or use an instrument to describe local practice and how that is influencing whether or not a patient got a particular treatment.  

So here's a horrible slide that you'll be able to read more carefully with some references that we used in this presentation.  Also, remember that randomization is an IV method and two-stage least-squares use and are an IV method.  And also I would suggest that you watch for a technical report on the use of instrumental variables in VA research from Todd and Elizabeth Cowgill coming this year.  So that's the end of my presentation.  I'm wondering if there are questions.

Moderator: So the first one is what is the difference between average treatment effect, ATE, and average treatment effect on the treated, ATT.

Patsi Sinnott:  I don't know.  But we'll try to — if you'll send us that question, I don't know if there is anybody else on the line who can answer that or write the answer.  

Moderator:  Okay, I've got a second one.

Patsi Sinnott:  I'm sorry, that was ATE versus —

Moderator:  And average treatment effect on the treated ATT.  So the second question is please expand on your definition of face validity, slide 20.

Patsi Sinnott:  So face validity means that you need to be able to argue that this is a valid instrument, that it makes sense in the anecdote and in the best argument you can make.  For  example, the argument that people who live closer to hospitals that can provide cardiac cath are more likely to get cath.  That has face validity to it.  

Moderator:  Next question is I would like clarification of the just identified slide.

Patsi Sinnott:  So the explanation for just identified means that there are no more IVs in the model than there are — in other words — so you don't want to have a creative model that's over-identified.  In other words, if there are too many variables in your model — and I'm going to explain this in the most statistical way, but over-identification means that you have essentially too many variables in the model.  In this particular case with instrumental variables you want to make sure that your instruments — there are not more instruments than there are independent variables in your model.  I hope that helps.

Moderator:  Next question is how does one confirm that a variable only affects the treatment but not the outcome directly?  And do you use correlation analysis, et cetera?

Patsi Sinnott:  Yes.  That's one of the ways.  And the statistical tests that are in the handout.  So is it a strong predictor and that's the F test in the Staiger Stock.  If the errors are correlated with the regressors, the Houseman test.  If the instrument is correlated with the error, the Sargan test.  As well — I have some other recommendations that will be in the guide that is coming out shortly on use of instrumental variables from some additional recommendations.  The Wald estimator to estimate the measured effect of the treatment on the outcome.  And using two-stage least-squares system of equations to find beta, the measured effect of treatment on outcome.  I hope that's helpful.

Moderator:  And then there was a comment that said, male patients are more likely than female patients to be referred sooner by providers to catheterization.

Patsi Sinnott:  Right.  Because men are more likely to have heart problems than women.

Moderator:  And then the next question, from a public healthy perspective isn't it possible that the characteristics of the neighborhoods of hospitals would have some effect on mortality?  Would this be a problem for this IV?

Patsi Sinnott:  Well, could you create an IV is the question for the — or control for effects of the neighborhood or the zip code or the HAS.  And the question is really is that a control variable, an exogenous variable, or is that something that's affecting treatment.  Is it affecting outcome, is it affecting the probability of getting treatment, the probability of getting some kind of treatment compared to others.  And that will help you differentiate whether you're using an instrumental — or need to use an instrumental variable or that it's an exogenous variable.  If it's independently predicative of the outcome then you need to put it in the model.

Moderator:  And this regarding slide 26.  How did you choose 2.5 miles?

Patsi Sinnott:  I didn't.  McClellan, Newhouse and McNeil did.  And what they did in this particular paper which is listed in the references is they actually looked at one mile, two and  a half miles.  I think they did five miles and ten miles to find the appropriate distance that differentiated — that was the better differentiation for them.

Female;  And then there was a comment that said people go away to college.  Not sure that distance to the nearest college is that predicative of years of education.  And then next question can you discuss further the test used to insure that instrument is uncorrelated with the errors and the other statistical tests you mentioned on the same slide.

Patsi Sinnott:  No, and I'd refer you to the guide book or the technical report that's coming out I think next month.  Sorry about that.

Moderator:  Next question is how is an IV entered into regression models?  Is it different from other co-variables?

Patsi Sinnott:  In the sense that it's included in the model?  No, it's not different.  I'm not sure I understand the question.  So if we go back to it's about the fourth slide when I included the OLS model, you have beta 1x beta 2x2.  It might be that but I'm sorry, I can't explain it better than that in this structure without a blackboard.

Moderator:  And then the next question is I am curious if hierarchical models or any kind of models are used because of the correlation between the IV and the independent variable?

Patsi Sinnott:  I can't answer that question, I'm sorry.

Moderator:  Next question.  What do you do when the results of the instrumental variables are different from the results from propensity?  Method.

Patsi Sinnott:  You decide which one is — remember, propensity is on an observed and an IV is for an unobserved effect.  You have to make a decision or report all the results and then come to a conclusion after that.  And I would refer you to there's a paper, Sleimen, that's not necessarily about VA veterans but basically they use several Cox proportional hazards models with and without instrumental variables.  And you have to use several methods to determine whether or not you're capturing all the effects, whether you're controlling for all of the effects and then which model best fits your data and which interpretation.  But you need to report all of the results.

Moderator:  Next question is please clarify the concept of no direct or indirect effects on outcome from slide 20.  It appears the instrumental variable has an indirect effect on outcome through the treatment.

Patsi Sinnott:  Well, I'm not sure I can clarify any more.  The instrumental variable affects whether or not you get the treatment but has no direct influence on the outcome.  What you're talking about that the indirect effect is only through the effect on treatment.  I'm not sure I'm helping much with that one.

Moderator:  Can you describe difference between 2SLS and IV method?  Have seen literature in which 2SLS is a way to implement IV.

Patsi Sinnott:  Correct.  That is — basically you're creating your instrumental variable with the first stage and then putting it in a second stage regression.

Moderator:  Is there a statistical association between IV and outcome?

Patsi Sinnott:  No.  There should be none.

Moderator:  Regarding face validity, cyclemetrically it is simply that the instrument appears to measure what it's (inaudible) measure.  A measure of suppression has items with writing that cancel problems with systems related suppression.  I guess that was a comment.

Patsi Sinnott:  That's a further explanation, correct.  Yes, and thank you for that.

Moderator: Another one is it is not an LLS regression model, it requires a different estimation 2SLS or two-step estimator.  I think that was a comment also.  Then another comment is structural equation model software allows for IV regression equations but it requires a specific estimator.  If you enter in "exogenous" and "endogenous" co-variants in the SUM, the typical estimator results in something consistent with mediation.  And then in parentheses, indirect — slash indirect effect.  And then this is the answer to a previous question on this issue.  And then the last question is how do I test the validity of IV for a logistics model?

Patsi Sinnott:  Well, the same way.  In other words, your model is going to be the same — is a logistic regression but your testing of the IV is going to be the same.  Does it have face validity, is it a strong predictor, is it uncorrelated.  And you will test it the same way.

Moderator:  There's actually one last question.  Can you explain how IV analysis tells you about the marginal case?  I've heard a speaker, John Brooks, explain that this is the use of IV analysis.

Patsi Sinnott:  Would you read that again?

Moderator:  Sure.  It says, can you explain how IV analysis tells you about the marginal case.  And then I've heard a speaker, John Brooks, explain that this is the use of IV analysis.

Patsi Sinnott:  That's correct.  What you're looking at is the marginal effect on the patients who are treated in using an instrumental variable rather than the general effect on the entire population.  

Moderator:  That's all the question we have.

Patsi Sinnott:  Thank you very much.  And again, I just want to emphasize that we will have a more in-depth technical manual available by the end of this fiscal year, I believe, or the end of the year.  And I would encourage people to look for that.  Heidi, are you there?

Moderator:  I am still here, yes.

Patsi Sinnott:  I think we're done.

Moderator:  Fantastic, I was just looking.  This is the last session of the course, isn't it?

Patsi Sinnott:  Yes.

Moderator:   Fantastic.  Well, for our audience, thank you very much for joining us for the HERC Econometrics course. We are glad that you joined us for this.  I think most of you received an email yesterday for the upcoming HERC course, the cost effective analysis course.  Feel free to register for that.  We hope you find as much value in that one as you have in this one.  

Patsy, thank you so much for taking the time to prepare and present today.  We really appreciate it.  Angela, thank you so much for pinch hitting for Todd or Karen or whoever was supposed to be here today.  .We  appreciate you being able to step in for that.  Thank you, everyone, for joining us today and we look forward to seeing you at a future HSR&D cyber seminar.  Thank you.  

**[End of Recording]**