HERC Health Economics Seminar - 14 -Department of Veteran s Affairs

August 15, 2012

**Department of Veterans Affairs**

**HERC Health Economics Seminar**

**Cost-Effectiveness Using Decision-Analytic Modeling**

**Kee Chan, PhD**

**August 15, 2012**

Moderator:  Just go ahead and introduce Kee Chan.  So Kee is a graduate from Yale University.  She’s a former research fellow at NIH.  She’s currently a research investigator at the VA Center for Health Quality, Outcomes and Economic Research in Bedford, Massachusetts.  And she’s also an assistant professor of health sciences epidemiology.  So we’re pleased to have her present today.   

Kee Chan:  Thank you to the audience and thank you to the HERC organizer for inviting me to give a presentation on cost-effective analysis using decision-analytical modeling and to share our research in progress. In today’s presentation I will discuss the decision-making process and the fundamental models used in cost-effectiveness analysis. Then I will present our research in progress on hepatitis C treatments 

If you’re interested in learning more about decision analysis, I will share with you my favorite resources, links and references. If you have questions or would like to have me consult with you on cost-effectiveness analysis in your studies, please feel free to contact me.  At the end of this presentation you will learn the concepts behind the decision-making process, the framework, the structure, your featured questions and the decision-analytical model, and the application in cost-effectiveness analysis in your studies. 

This is today’s presentation outline. I have found that using the concept of proactive modeling very helpful in study design. Then I will discuss the structure of the decision analysis tree and the components of cost-effectiveness analysis. 

I will share with you our model view from our studies. In general modeling has some limitations. However, the strength in modeling lies in this finding to help guide policymaking and to develop new guidelines.  Last I will share resources, references and links that I’ve found very helpful. 

“A good decision is a logical decision, one based on uncertainties, values, and preferences of a decision‐maker.” This is quoted by Ronald Howard, who is a professor in management science and engineering at Stanford University. He is known as the person who pioneered the field decision analysis. 

How do we assess the best use of health care resources? How do we control health care costs? There are many ways to control health care costs. You can either eliminate inefficient health care services or interventions. Many hospital services are now focusing on outpatient services to offset the costs while still providing good quality care. 

Investing in resources and preventive services could also save on future costs of chronic illnesses.  To justify these alternatives decision analysis is an objective and systematic approach to test out all of these options.  

Think of the word proactive.  And let’s focus on the first three letters, P-R-O in the word proactive.  In steps one in decision making it is to define the problem. P represents P in problem. What would happen to the situation if I took no action?  What are the outcomes you want to avoid or to achieve in your study?  

R represents reframe.  Look at the problem from different perspectives.  Are there different stakeholders?  Is this study from a society perspective, from a health care system or from a health insurance company perspective?  

O represents the objectives.  Are you interested in preventing or treating?  Are there short of long-term goals in your study?  

In step two let’s consider the endpoints.  Focus on the next three letters, A-C-T in the word proactive.  What are the alternatives, the consequences and the trade-offs?

A stands for alternatives.  Sometimes you will have a list of different alternatives.  It is then helpful to categorize these alternatives in one of the following categories, either to wait and see, which is the do-nothing policy, or to initiate treatment intervention to a new program of interest, which is considered the action step, or to obtain more information before deciding. 

As you list all your alternatives think of all the consequences of each alternative.  Then last identify and estimate the value trail.  In step three the goal is to determine the best option.  After completing step one and two let’s integrate the evidence.  

Use the model to optimize the expected value.  In most models where their assumptions need to project the cost and health benefits it is then important to evaluate uncertainties of these interpretations.  Conducting a series of sensitivity analysis can then test the robustness of these values.  

So let’s put all these letters together, proactive.  And it is a very helpful way to think of your problem, your study design using a proactive model and concept.  And it helps you to visualize and map out your model on paper while you’re thinking about the question of how cost effective this particular program, or screening program or your new treatment for your particular population. 

So that concludes a brief discussion of the concept of proactive modeling. Next a decision tree is a visual display of all possible options and the consequences that follow each option.  

So let’s apply the concept of proactive into the decision analytical tree.  In step one define the problem and objective from the perspective of interest.  In this example let’s say that there are three decisions that we want to consider, either invest in a new treatment A, or to invest in treatment B or to have no treatment.  

In step two remember the ACT in the word proactive. We now want to define the alternatives, consequences and the trade-offs. 

A decision tree consists of three types of nodes, a decision node which is commonly represented by a square.  Chance nodes are represented by circles.  And end nodes are represented by triangles.  

In step three use this decision analytical model to evaluate and integrate the evidence and to evaluate uncertainty in your model.  This is the fundamental structure of the decision analytical model. 

Now we will now focus on the components of cost-effectiveness analysis with the understanding of the concept of proactive modeling and the decision analysis tree.  We can use these models to consider the costs, economic costs of health care.  

We can then ask the research question, what is the most efficient use of this health care resource given the alternative uses in terms of time, resources and costs?  Decision analysis can also be used to assess time efficiency.  For example, an hour of a physician’s time spent with one patient is then unavailable for another patient.  

Resource effectiveness is another type of analysis that could be done using decision analysis.  Resources used for one program cannot be spent to increase the program use of another or invest in a new program, resources to be described in monetary value such as cost, or non-monetary value such as personnel or maybe volunteer helpers. 

This graph illustrates this health intervention in terms of health care costs on the x axis and their health effect on the y axis.  On the upper left quadrant the interventions here are can improve health and save money at the same time.  Therefore no further analysis is necessary. 

At the lower right quadrant interventions here decreases health and costs money.  Therefore these interventions could be considered to be discontinued.  

The decision-making process then becomes more challenging in the intervention lines in the other two quadrants in the upper right and in the lower left.  You can either improve health, yet cost money, which on the upper right, or have interventions that save money but at some loss of health outcomes. 

Health benefits and health resources costs must each be expressed in terms of unit of measurement.  Healthy resources can be measure in terms of monetary terms or non-monetary terms. Health effectiveness or health benefits can be expressed in terms of the units of output, such as the cost, such as the case of disease prevented, lives saved, years of life saved or quality adjusted life years. 

Keep in mind that there is a large range of decision makers.  What is the perspective of the study?  Who is the decision maker?  Who is the targeted audience?  Who is the study—what is the study examined using a society perspective, a patient perspective, a provider perspective or an organizational perspective?

Consider different types of costs.  I find it helpful to have a list of different categories to organize the data collection of costs in terms of health care resources and non health care resources. 

For example, health care resources cost in a clinical visit typically would include inpatient admissions, the pharmacy, the medical equipment and medical tests for the patient.  And example of a non health care resource in a health care service would be the cost of a patient’s transportation.  For example, if your study includes a bus voucher then you may want to discuss the cost of transportation from the patient’s perspective, but include the cost of transportation as a part of your intervention costs. 

I find it helpful to list all the health and non health cost resources first when you’re designing your study.  With this list you can prioritize what is most relevant, most important and when it’s available and unavailable.  

Now with the list of different types of costs lay out the costs sequentially.  Organize the sequence of events according to the initial costs, the induced costs and averted costs.  

Initial cost refers to the cost of setting up intervention.  In the present of the intervention are there other additional costs induced by the intervention?  And as a consequence of the intervention being in place are there averted costs due to the presence of intervention such as of cost reduction in a particular service, or maybe a less work produced of a particular service.  And finally also consider the short or long-term resources costs. 

Probability is the chance of an event.  Probability of zero represents an event is impossible.  Probability of one represents an event that’s certain to happen and the probability of 0.5 is that the event is equally as likely to occur as not to occur. 

Preference-based measures reflect the value an individual has for a particular health state or the relative desirability of a health outcome.  Effectiveness includes the health benefits which can be described as a single measure or a combined measure.  What I mean by that is a single measure of health effect could be represented as the number of cases prevented by vaccinations, number of cases of cancer detected, number of hospital days reduced.  It really depends on what’s your end goal in your particular study. 

A combined measure of health outcome could consider both the effect of quality of life and the length of life.  And this could be represented as adjusted life years.  

An intervention can only be cost effective as compared to another use of resources or compared to some standard.  So it’s actually more accurate to state that program X had incremental cost effectiveness of $50,000 per quality-adjusted life years say as compared to program Y.  Therefore assessing the incremental cost-effectiveness ratio, which we often abbreviate as ICER, also call it icers, is important.  

So in this example let’s say we have an intervention A which costs $400,000 and produces the effectiveness of ten life years.  That’s intervention A which is in blue.  Unless we’re comparing it to intervention B which costs $100,000 per eight life years gained. 

So the question is, is the extra health benefit worth the extra cost?  So in this example I’m using the formula you would subtract the cost of intervention A which is $400,000 minus the cost of intervention B which is $100,000, which equals to $300,000, divide it by ten life years minus eight life years, which equals to two life years.  This then equals to an ICER of $150,000 per life year. 

So the answer to the question is the extra health benefit worth the extra cost?  If intervention A is chosen the additional investment of $150,000 results in one additional life year relative to intervention B.  

So let’s say that there’s a probability associated with dying with each treatment option.  Would this change our analysis?  In this example let’s say intervention A is treatment A and treatment A is chosen.  There’s a ninety percent—and there’s a ninety percent of living and ten percent of dying.  

And if treatment B is chosen there’s eighty percent of living and twenty percent of dying.  It is also important to consider the effect of dying in the analysis in assessing your and assessing the incremental cost effectiveness ratio.  

We will now grow back the analysis, meaning we will average out the endpoints to determine the optimal options.  So for example here $400,000 is multiplied by 0.9, which equals to $360,000.  $200,000 is multiplied by 0.1, which equals to $20,000.  $360,000 plus $20,000 equals to $390,000.  

Complete the same calculation for effectiveness, which will give you nine life years which you represented here in red.  Complete the same analysis for treatment A and you will see that it is $90,000 per 6.4 life years.  

So to compute the ICER for this analysis it is $390,000 minus $90,000.  Divide by nine life years minus 6.4 life years will give you an incremental cost effectiveness ratio of $111,000, $538,000 per life year.  

So in addition to determining the ICER of your analysis handling uncertainty in your model is crucial to determine the robustness of your analysis.  Parameters and model structures uncertainty can be addressed used sensitivity analysis.  

You could evaluate one of the values over a range, which is termed as one-way sensitivity analysis.  If you then examine two parameters over a range it is considered a two-way sensitivity.  You link them in two or more parameters in your analysis and it’s considered a multi-way sensitivity analysis.  If you’re examining your parameters using a distribution it is considered a probabilistic sensitivity analysis.  

In summary, cost-effective analysis using decision-analytical modeling can help summarize large amount and improve staff information.  It can help clarify the decision-making process and compare the different scenarios in a complex system.  

So in the second half of this presentation I will present our cost-effectiveness analysis using decision-analytical models.  Because the work is in progress the following slides are not included in your handouts.  

First I would like to give special thanks to my co-authors listed here, from San Diego, Eric and Dr. Sam Ho from Los Angeles, Palo Alto, Dr. Steve Asch, and the two from Boston.  And I would like to also give special thanks to our research assistant, May, who has been extremely helpful in our analysis, and to Dr. Sam Ho who has been a great mentor to me on this project. 

So the presentation I would present on our research is examining the cost-effectiveness analysis of new direct-acting antiviral known as DAA therapy for untreated chronic hepatitis C genotype 1 infections.  Four million Americans are infected with hepatitis C. Among them 130,000 are veterans.  

Hepatitis C infection leads to fibrosis which causes liver failure and liver cancer.  Severe cases require liver transplant.  

Hepatitis is caused by a viral infection that leads to inflammation and scarring of the liver.  When the liver cells die they are replaced by scar tissue and this process is called fibrosis.  The amount of scarring is usually determined at the different stages of fibrosis.  

From stage one and two little holes have started to develop in the liver, but there’s no significant effect on liver function yet.  At stage three and four scarring in the tissue has begun to siphon normal function of the liver.  This scarring can lead to liver failure and liver cancer. 

Only a liver transplantation can save a patient’s life at that point.  Therefore, it is important to treat hepatitis early on. 

Therapy is currently available.  Dual therapy is taking pegylated interferon with ribavirin. 

For the rest of this talk I will refer to dual therapy as PR.  Hepatitis can be cured which is known as the sustained biological response, SBR.  

High cure rate has been difficult to achieve in current therapy because of poor adherence and long duration of the treatment.  However, new therapies are currently available which is known as a triple therapy.  

New drugs, Boceprevir and Telaprevir, work in combination with PR and are known to be effective and have shorter duration.  Two studies have already shown the cost effectiveness of using the new triple therapy in general populations, and one within the general population and in the second study was a group in Italy who conducted the analysis.  

Using a society perspective we then asked the question what is the cost effectiveness analysis of new triple therapy in our veteran patients.  Using a simulated population we aim to estimate the long-term clinical outcome benefits and to calculate total costs and effectiveness.  

The objective of our proposed research is to use the decision-analytical mark of model to investigate, estimate the costs and effectiveness of new triple therapy in the VA health care system.  The target population for this analysis is the current cohort of untreated chronic hepatitis C genotype 1 patients in the VA health care system. 

The model uses natural history data and progress rates to estimate the current distribution of fibrosis among VA patients with hepatitis C.  Sensitivity analysis was also used to examine the impact of variation in drug costs, treatment efficacy, overall treatment rates, transition probability and analyzing these costs on the results of the model.  The results presented will help assist policymakers, the stakeholders, clinicians and patients at our VA to determine the best use of resources and the investment of new triple therapy drugs. 

Four strategies were compared in our model.  We looked at standard dual therapy, PR, triple therapy with Boceprevir plus PR, triple therapy with Telaprevir plus PR and compared to no treatments.  In our model patients received PR, which is the dual therapy, for an average of 38 weeks, Telaprevir for twelve weeks with the dual therapy for about twenty-weeks, and Boceprevir for an average thirty weeks with PR for an average of thirty-five weeks. 

This table describes our population in our study. Here based on the date of registry in 2008 about twenty-one percent of veterans received the treatment had hepatitis therapy. 

In our decision and our decision-analytical model we obtained transition probability from published literature.  We then estimate SVR rates based on the VA registry data for hepatitis genotype, hepatitis C genotype 1 from 2001 to 2007 and obtained information based from the Phase III clinical trial, which are from the published literature. 

The annual cost of care for caring for a patient with hepatitis C included the following, which is illustrated on this table.  Their care during the fibrosis stage cost about $195.  The hospitalization for liver cancer care cost about $49,000 and if match is available liver transplantation costs on average $134,000, but in some cases the cost of liver transplantation can cost up to $500,000. 

We used the triple therapy costs were based on the VA for—the triple therapy treatment cost was based on the VA pricing data that was available, publicly available and FDA-approved response guided therapy protocol to estimate the duration of the treatment.  I would like to give special thanks to our co-authors, Dr. Allen Gifford and Amresh Hanchate for sharing their data on the VA hepatitis C utilization cost for our study. 

Therefore to compare the costs and the benefits between triple therapy we examined these scenarios.  We compared the triple therapy versus no treatment. And then we compared the triple therapy with dual treatment. 

We examined the projected benefit using the therapy to reduce the rates of liver cancer, liver transplant and liver death.  And we also examined the projected costs for hepatitis C care. 

Our model shows that new therapy can have significant health benefits if we can treat twenty-one percent of the veteran patients who are eligible for our therapies.  Here in this graph it takes the number of liver deaths. 

Without treatment liver deaths from untreated hepatitis are about 29,000 deaths annually. On the other hand, if patients are treated with dual therapy liver death is reduced by five percent. More interestingly, with Boceprevir PR triple therapy liver death is reduced by 8.7 percent.  With Telaprevir PR therapy we can achieve similar effectiveness by reducing liver deaths by 8.5 percent. 

To compare the cost and benefits between the two therapies we examined these scenarios. The cost of triple therapies are different.  Based on our stimulation we are able to project the cost and effectiveness across the different strategies.  

Dual therapy will cost on average about $36,000 with a gain of 8.7 quality-adjusted life years.  And Boceprevir triple therapy will cost about $64,000 per 8.8 quality-adjusted years.  And Telaprevir PR triple therapy will cost about $64,000 per 8.8 quality-adjusted life years. 

So here examined the incremental cost-effectiveness ratio, which is important to compare the triple therapy versus no treatment and comparing triple therapy with dual treatment.  In this graph we’re able to show the cost per quality-adjusted life years across the different scenarios.  

Treatment below $50,000 per quality-adjusted life years is considered somewhat cost effective.  Here—well the x axis are missing here.  Okay so here on the far left, well so this bar represents well some of the year PR versus no treatment.  And it’s about $13,000. 

Here Telaprevir with dual therapy versus no treatment is about $26,000.  And on this side you see on the far right side this bar here represents Boceprevir compared to dual therapy is about $31,000.  And Telaprevir PR compared to dual therapy is about $47,000 per quality-adjusted life years.  And keep in mind this is a graph representing the incremental cost effectiveness ratio.  

Last we projected the annual cost of care for patients with hepatitis C from a VA health care perspective.  We are able—we showed that the cost expenditure is about $3.8 billion, which includes the costs related to liver transplantation for a projected 5,155 cases. This is under no treatment. 

Under the current standard of care with the dual therapy PR we can reduce the number of liver transplants by 600 cases.  With triple therapy we can further reduce the number of liver transplantations to almost about 1,000 cases. 

So here this graph shows that although there are upfront costs of investing in the triple therapy, there is great benefit for our veterans.  Triple therapy can reduce the number of patients with severe fibrosis and this reduction of cases would then reduce the need of a liver transplant, which would reduce cost and expenditure of the latest liver related illness.

Due to the estimates and assumptions made in the model, there are some limitations.  Here we cited background mortalities for our hepatitis-affected veteran patients in the male population in the U.S. life table. 

Second, specific VA clinical trial data was not available for new triple therapy.  Despite the limitations, the findings from the model provide significant values to stakeholders who are considering the investment of new therapies for veterans.  

In conclusion, Boceprevir PR and Telaprevir PR can reduce liver deaths up to twenty percent.  If twenty-one percent of the VA patients are treated with triple therapy it will cost about a $1 billion investment. 

Our findings from our model suggest that it is cost effective for the VA health care system to invest in the new triple therapy to help veterans live free from hepatitis so that they can have a productive—so they can be a productive citizen in society and live a healthy life.  And that is priceless. 

So that concludes the portion which I shared on the research and progress part of my talk.  Now I will share with you some of the resources that I found very helpful in conducting a cost-effectiveness analysis in a decision-analytical model.  

So with that research and question in mind it is helpful to evaluate how your research study would fit in the scope of other literature.  Here at Tufts University they host a registry of all the cost-effectiveness studies that they evaluate and summarize on this website.  On this site you can search for studies that have been reviewed for their quality and summary statements. 

I provided a link for here, but since you’re not able to click on the link we’re just going to spend a couple of minutes going through, navigating through the site, which I found to be very helpful when you’re designing your studies.  So this is the website.  This is the cost-effectiveness analysis registry.  

And once actually once your study on cost-effectiveness is completed the members of the team review your study and hosts will then send you notification that your study has been reviewed and is on this registry.  For example, my study on using markup models to analyze the cost-effectiveness screening for severe combined immunodeficiency SCID was just recently reviewed.  And you can just see here that how it will look.  

It gives you the full reference and tells you what the in progression type is, whether the study considered prevention stages, where the data came from, whether this is private or government funded, whether the time horizon was provided in the study, the perspective of the study.  Remember I mentioned that it is very important to consider perspective of your study when you’re designing your research question, whether discounting rates were considered and what were the sensitivity studies, what were the sensitivity analysis studies conducted in this in perspective, and whether the cost-effective analysis acceptability curve was used in the turnaround in this particular study. 

And also they also rate this.  The reviewers on this registry also rate the quality for on your study.  between a scale of 1 to 7.  

So this is an example of a work that I completed.  And if you also click on the site it goes straight to Pubmed.  And you can also log in and pick up the full article if you choose to. 

So now let’s just go back here.  So in the example of hepatitis let’s type the word hepatitis.  

Question:  Hey, Kee.  This is Todd.  Can you hear me?

Kee Chan:  Yes.  

Question:  Can you tell me?  This is a great resource.  And I didn’t realize they rated your studies and it sort of a little daunting to see your rating score that everybody would hope we’d get a seven.  Do you have a sense on why you don’t get a seven and what factors they sort of downgraded you on?

Kee Chan:  I actually at first because I actually received the notice that my study was reviewed just two days ago.  So that’s why I included on the slide on this presentation.  

So I think what they do is they actually on the website you can view your—on the website you can click on to how to become a reviewer.  And on that link you can then see what are some of the criteria that the reviewers use to assess your study. 

I haven’t actually looked into details of the comments, but what I did do is I looked at other studies that were published in The New England Journal of Medicine and their cost-effective analysis.  And some of them were scored 5.5.  So for me I wanted to see relatively where my score fits compared to studies that were published in higher impact journals.

So I don’t know if seven is a score that not many papers receive or if it’s what are the criteria that a paper would need to receive a score of seven, but that’s something we actually follow up actually after this presentation.  So that’s why I put a note to the audience that you will actually receive an email letting you know that they actually reviewed your paper.  

Question:  Very cool.  Thanks. 

Kee Chan:  Yeah.  So what I found to be very helpful is I when I was designing my study for hepatitis and also my other projects is I go to this website.  And I look at what all the different studies that are currently available on cost-effective now.  

And you can also do the same search on pubmed.  However, on this particular site since they actually have gone through and did quite or a summary statement for you, you can then isolate on which of the papers that were looking at similar questions or looking at similar perspectives that you’re interested in. 

And so for example this hepatitis this is all the previous papers that are published.  There is a one-year lag time.  So papers that were recently published a couple months ago, for example the two papers on cost-effective analysis, they’re not going to be available on this registry.  So I do find it helpful to use pubmed or medline and this particular registry to complement each other. All right, so you can see here and hopefully you’ll find this registry helpful.   

So what else?  It is important to keep in mind some of the limitations of models.  And there is a lack of for example there might be a lack of available data.  The model may suffer from too many assumptions of entered parameters.  And how much does the model accurately reflect real world experiments?

Despite limitations there are many benefits to modeling.  And the strength lies in its ability to illustrate complex, decision-making process in visual way.  Decision analysis can help formulate objectives, evaluate complex systems, inform policy, and develop guidelines and guide new direction for research.  

So in addition to my research on hepatitis C treatments that I presented here today, my other current research in progress includes developing models to evaluate cost analysis of using new interventions such as a clinical reminder to improve HIV testing rates.  This project on clinical reminders to improve HIV testing rates is directed by Dr. Matt Goetz from Los Angeles and Steve Asch from Palo Alto.  I’m currently working with the team on developing a budget impact analysis. 

And another project that I’m currently examining—another project that I’m working on examining the impact of a touch screen computer-assisted self-interview tool that can identify issues related to poor adherence to HIV medication.  And that project is directed by Dr. Allen Gifford from Bedford VA.  And I’m developing a model to assess the cost and time efficiency of using these new touch screen tools during the waiting room and how that might increase patient adherence behavior while also making the clinics work much more efficiently.  

Trained as a microbiologist and geneticist who became interested in health policy, I teach courses on public health genomics.  And so I’m very interested in examining cost-effectiveness and the use of personalized genomic information in the health care system.  

So in these various projects I’m also expanding the use of decision analysis to incorporate the individual level and the system level effect on the cost of care.  So I’m exploring the use of agent-based modeling and system dynamics in decision analysis.  

Here is a list of a few of my favorite references that are on my bookshelf.  And this list is included in your handout.  The software that I use for my analysis is listed here and Treeage. 

And last is the HERC website is a great resource for tutorials, references, as well as contact information of other researchers with similar research interests.  If you’re interested on learning more about decision analysis here are a few research societies to explore, the Society for Medical Decision Making.  I will be presenting at the Society of Medical Decision Making in October at their annual meeting, ISPOR, International Society of Pharmacoeconomics and Outcome Research, the Decision Science Institution.  

DSI really focuses on more of the philosophy and methodology, not so much on disease models, on the disease content, but if you’re interested in learning more of the theories this is a good society to join, and also informs, which Institute for Operational Research and Management Science.  If you’re interested in looking at how to apply decision analysis and management situations, informs is another great resource and society to become a part of.  

So in summary from today’s presentation we discussed the use of proactive modeling in your design.  We constructed a design analysis tree, used cost-effective analysis, compared research studies, understand the limitation and strengths and I also shared with you the resources and references that you might find helpful in the design of your studies.  

The books I listed and also the websites.  And some of the methods I shared today in terms of how to think about your problem using the proactive model, like think of P which is problem; R refers to reframe the perspective; O represents the objective; ACT represents the alternatives, the consequences and the trade-offs in your study; I now all the information that’s integrated evidence; V what is the value, what is the endpoint value; and E is to finally evaluate your information and to assess the uncertainty in your interpretation.  

So if you have any questions or would like to collaborate, please feel free to contact me.  My information is listed here.  I thank you again the HERC organizer for this invitation to give this presentation, and the audience for their interest in today’s topic.  Thank you. 

Question:  Thanks so much, Kee.  This was a great talk.  And I apologize for being a little bit late.  Jean, you gave the introduction.  Have you been keeping track of the questions? 

Moderator:  Yes.  So there’s one in here right now that says, was asking about the Tufts registry and whether or not there’s help for approvals.  

Kee Chan:  You mean help in developing a proposal?  

Moderator:  Yes.  

Kee Chan:  Let’s look at the website here.  Let me see.  This registry—well in terms of developing your proposal and in terms of the basic development and decision analysis, they do provide some information of how to interpret the registry.  

In terms of proposal sites you may want to actually contact the content designer of this website to learn more of a proposal design.  So that I’m not too familiar, but I do know that this website is focused on more of a—is focused on providing a resource and it’s actually a lot of it’s volunteer work to provide a list of studies so that you can compare. 

However, it is also to be mindful that many of the studies that are not considered cost effectiveness are not going to be in this registry.  So for example if you are interested in looking at a cost minimalization study it won’t be listed here, but a study on cost minimalization study, or looking at cost analysis or looking at a budget impact analysis to help inform your study design, those types of analyses won’t be in this registry, but you will find those types of studies on pubmed or medline. 

So in terms of proposal design I’m not too family, but the group at Tufts has been—I do work with some of the researchers at Tufts University at the medical center.  And they’re extremely helpful in terms of providing guidance and decision making. 

Question:  Thanks, Jean, for asking that.  I was actually going to ask a question about the model that you presented on the hepatitis C.  And it’s a very interesting model.  I was curious why do you find that the treatments aren’t having a bigger effect?  And you mentioned that the liver transplants go down by about 1,000, but I was expecting a bigger effect.  Can you describe why you don’t see a bigger effect on lung cancer and lung and, sorry, liver cancer and liver transplants?

Kee Chan:  Yes.  So one of the factors that affects the number of cases reduction is the treatment rate.  So if you had the VA which I showed at the very beginning of the talk, is that based on our 2008 data, only about twenty, about only twenty-one percent of patients with hepatitis are treated with treatment. 

So our starting population is small.  So that’s why the effects of numbers of liver transplantation is not as much as the reduction of liver transplantation is not as great as we hoped.   

So one of the factors we also did do in our sensitivity analysis which I did not present here, is to estimate what would be the effect of increasing treatment rate to fifty percent.  And when we increased the treatment rate to fifty percent we can see a much more greater reduction in liver death, liver transplantation and liver cancer.  

Question:  And I take it that there’s a trade-off between the treatment costs, especially these new treatments that are expensive and the savings that have to do with liver cancer and transplantation.  Are there any strategies?  And I know that the dual therapy is not widely liked by patients.  Are there any strategies by which you start them on that, and if they can’t continue then you move them to these more complicated, newer regimens that are more expensive?

Kee Chan:  That’s a really great question.  And actually in our model we were—the current model that we examined only took into account of the fourth strategy, which is no treatment, dual therapy and triple therapy.  

One of our follow-up questions to this model is to examine what would happen if we had a fifth strategy which is looking at treating patients with dual therapy and only patients who either were not successful with dual therapies, then move them on into triple therapy.  And that we haven’t explored yet, but that’s something that we could also examine using our model.

But that is something that might actually produce, might be a good medium between the cost effectiveness of since the medicines are quite expensive.  But given that the duration of the treatment is much shorter that might propose that there is a greater likelihood of patients completing this regimen, which then has a higher adherence, then giving a much higher SVR rate. 

Moderator:  There are a couple of more questions that have come in.  So one asks this seems very medically oriented.  Can you talk a little bit about what might change when doing a cost-effectiveness analysis of a mental health intervention?

Kee Chan:  In terms of the decision analysis of how to—is the question is how to use a decision analysis to evaluate a mental health intervention?  Is that the question?

Moderator:  Right.  Yes that’s the question. 

Kee Chan:  So for example if we—I’ll just—so that’s why in the very beginning I showed the fundamental design of a cost-effectiveness and a decision [in the hospice] tree, so that way that audience who could use this information to apply to their own studies.  So that’s why I’m in the middle of the second part of this topic focused on the work that I’m working on, but decision analysis is the basic concept that you could use to apply for any research design.  

So for example, here the three steps that I mentioned, the proactive.  And this is actually a concept that’s been widely used in the decision analysis field.  So it’s not something I made up. 

So first we define the problem, what’s the problem, reframing the perspective and objective.  So what you could do is treatment A, B and C here I look at treatments, which is for example the hepatitis C treatment. 

Well you can also look at mental health programs like I don’t know the actual objective of the audience question, but for example if you have a mental health program which is examining either treatment with a behavioral treatment, versus group therapy, versus maybe a yoga treatment therapy, a physical therapy, so you could them based on your mental health is that you have this mental health intervention that’s related to working with the doctor and having pharmaceutical treatment, maybe treatment B which would be intervention B comparing to a group counseling session, compared to no therapy.  Or you can also compare it with a third strategy, which I gave here, which is a physical like a yoga type of mental health intervention. 

And once you have those strategies that you’re interested in examining, you would then look at the chance node, which is what are the events that could happen.  So here looking at using treatment I’m comparing Y, then here someone lives or dies.  And this is very small, but so the point of this exercise was to just to give the audience the basic structure.  

So here if you’re looking at intervention where a physician is working with the patient and prescribing medicine, maybe the chance node is having a positive effect or a negative effect from the treatment, positive or negative effect and a positive or negative effect.  And the investigator would then define what a positive effect would be.  Would that be that they have a lower blood pressure or other types of measurements that would have a more quantitative measure that you could apply here, and the endpoint which is necessary as its cost per health benefit.   

So maybe it could be cost per days of less depression, cost per days plus inpatient care for medical mental health issue care.  So it depends on what the problem is, how you’re reframing the question and what your objective is.  

And this basic model here you can basically look at whether all the events that you are interested in and then make and as you list your events also then define what the probability of a particular patient experience in that event.  Hopefully that answers your question.  And again I’ll be happy to speak with the audience on any of the questions.  And feel free to contact me directly.  My contact information is listed on the handout and I’ll be happy to collaborate or work as a consultant on your projects.  

Question:  Hey, Kee, I just wanted to stress the idea that it really depends on your decision.  And with the hepatitis model that you presented it’s a very straightforward decision.   And so it’s a great, simple sort of structure.  And you take something like mental health treatments it’s a very tough thing to get a hand, your arms around.

Kee Chan:  Mm-hm. 

Question:  And so you’re very right to sort of start with a proactive approach.  

Kee Chan:  Yeah.  

Question:  And I think people will want to be able to boil down this approach to something that’s useful, the decision makers.  

Kee Chan:  Yeah. 

Question:  And you might recognize is that not all decision makers have the same objectives.  And so....

Kee Chan:  Yes exactly, mm-hm. 

Question:  Thank you. 

Kee Chan:  Yes, mm-hm.  So that’s why I gave those three examples, which is if you’re interested on pharmaceutical drugs for mental, for more of a behavior intervention or a physical intervention it’s going to really depend on your objective.  But it’s possible to use decision analysis in mental health in the mental health research field.  Any questions?

Moderator:  One last question from the following slide.  I guess it’s slide twenty-two.  It says how does one determine the probabilities of an event?  Is there a general rule?  Like I think you have that probability of living versus dying on that slide.  

Kee Chan:  Over here?  The question was how to determine the probability?  

Moderator:  Right.  Do you take it from the literature?  

Kee Chan:  Yes, mm-hm.  So again many, much of the design of the tree really depends on availability of the data.  So that’s why in the last slide I presented some of the limitations of modeling is that it really depends on availability of data. 

So the probability so here I’m assuming that I have the there was—here in the example I’m assuming that the combo trial has shown that treatment A results in a ninety percent survival rate compared to and then that ten percent of the individuals die.  So that information is already provided. 

If information is not provided in terms of the probability then you might need to make assumptions.  And to make those assumptions most realistic is when you might want to consult an expert opinion on which could be physicians, or psychiatrists or psychologists, but depending upon the research field that you’re interested in.  Any other questions?

Moderator:  No.  That’s it.  

Question:  Well thank you much, Kee.  This was a wonderful talk.  I really appreciate it and I again apologize for being late.  And thanks to Jean for introducing you.  

Kee Chan:  Yeah.  Thank you, everyone.  And again this is my contact information for anyone who has follow-up questions or would like to ask any questions individually or directly to me feel free to contact me.   And I’m available on my BU email address, keechan@bu.edu.  

Question:  Perfect.  Thanks everyone. 

Kee Chan:  Thank you. 

Question:  Thanks, Kee.  

Kee Chan:  Thanks. 

Question:  Have a good day.  

Kee Chan:  Oh you too.  

Question:  Thanks, Heidi.  

Moderator:  Thank you, everyone, for joining us.  And I just want to mention if anyone is interested in more information on cost-effective analysis we are starting a HERC cost-effective analysis course the beginning of September.  

I’m assuming that all of you are already registered for it because the numbers are astronomical, but if you have not had a chance to register yes that will be starting on September 5th.  And we will be sending registration information out to everyone on that shortly.  Thank you for joining us today and we will see you at a future HERC cyberseminar.  Thank you. 

Question:  Thank you. 

**[End of Recording]**