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Comparative effectiveness analysis and quality of life
 

Comparative effectiveness analysis and quality of life

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    Comparative effectiveness analysis and quality of life Comparative effectiveness analysis and quality of life Presentation Transcript

    • Patricia Cerrito, University of Louisville John Cerrito, Kroger Pharmacy [email_address]
      • The purpose of comparative effectiveness analysis is ordinarily defined as a means to compare the benefits of drug A versus drug B.
      • However, particularly in relationship to cancer drugs, there is only drug A.
      • We demonstrate how data mining can be used to investigate patient quality of life using actual complaints
      • We also look at how comparative effectiveness analysis can be enhanced through text analysis
    •  
      • Suppose a cancer drug for patients with liver cancer allows a patient to live an average of 18 months compared to not using the drug
      • The analyst decides that the quality of life is only 40% of perfect health (giving a weight of 0.4)
      • Then the drug gives 1.5*0.4=0.6 QALYs to the patient (quality adjusted life years)
      • Suppose that at the initial introduction of this drug, it costs $1000/month, or about $18,000 for the anticipated additional life of the patient.
      • Then the cost per QALY is equal to 18,000
      • Suppose the quality of life is 60% of perfect health.
      • Then the drug gives 1.5*0.6=0.9 QALYs to the patient at a cost of $12,000/0.9=$13,000 per year of life saved
      • Suppose the quality of life is 50% of perfect health.
      • Then the drug gives 1.5*0.5=0.75 QALYs to the patient at a cost of $16,000
      • If a person is otherwise young and healthy and a drug costs $10,000 per year, then the QALY is $10,000.
      • If a patient is older and has a chronic condition, then that patient’s utility may be defined as exactly half that of a young and healthy person. In that case, the QALY is $20,000 for the same drug.
      • If the patient is old and has two or more chronic conditions, then the patient’s utility could be defined as 25% that of a young and healthy person. In that case, the QALY IS $40,000 per year of life saved
      • Non-treatment will always be cheaper than treatment
      • If enough costs are added into the model and the quality of life sufficiently lowered, the most cost effective option will always be non-treatment
      • When there is only one payer for health services, treatment can be denied because of cost using abstract models without having to deal with real patients with real concerns
      • In Britain, cost effectiveness is used for all new drugs
      • 25% of all cancer patients are denied effective chemotherapy drugs because they are not cost effective
      • Britain now has the lowest cancer survival rates compared to the rest of the western world.
      • When patients are denied life-prolonging treatments, it stands to reason that the survival time will be lower.
      • Lower quality of life
      • Use the minimal potential benefit
      • Example of Avastin
        • Assumption of palliative care only instead of the possibility of remission and cure
        • Assumption of just 5 months of added survival with this drug
        • Assumption of quality of life equal to between 40% and 60%
      • Claim that sensitivity analysis will take care of any error in assumptions.
      • Clinical trials generally test single medications
      • Treatment practice on the margins provides medical advances by using drug combinations
        • Treatment for AIDS
        • Lowering gestational age of treatment of neonates starting now at 22 weeks
        • Treatment of cancer
      • By rationing drugs based upon comparative effectiveness analysis, these medical advances will no longer be possible.
      • Drug costs include the cost of research and development
      • Costs are also a consequence of the size of the market
        • Rare diseases are more likely to be rationed since a small market entails a higher cost that is more likely to be above the designated threshold value.
        • Not everyone is lucky enough to have a more common disease that is less likely to be rationed.
      • “ NICE has been proclaimed for representing the closest anyone has yet come to fulfilling the economist’s dream of how priority setting in healthcare should be conducted”
      • “ It is not uncommon for an economists dream-come-true to be seen as a nightmare by everyone else”
      • Alan Williams
      • The implications of continually denying effective treatment to patients based upon quality adjusted cost remain unknown
      • What are the general impacts upon the population (other than a reduction in longevity)?
      • The FDA has met to de-list the chemotherapy drug, Avastin (decision postponed from September to December)
        • Previously approved for advanced breast cancer
        • Will no longer have FDA approval
      • It was approved because it demonstrated significant disease-free survival but not overall survival
      • The lack of overall survival is the reason for withdrawing approval
      • The drug, Provenge has been approved by the FDA for metastatic prostate cancer
        • It improves overall and disease free survival
        • It has fewer side effects compared to current treatments
      • Medicare has not approved it for payment although all regional Medicare units are providing for the drug.
      • Medicare will spend the next year studying the drug under the criteria of “necessary and reasonable”
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      • To a person without a hand, is a hand transplant a benefit?
      • To the surgeons, is a patient transplant of benefit to the patient?
        • A recent study suggests that the two perspectives are extremely different
      • The patients with metastatic breast cancer will virtually all prefer disease-free survival as a benefit to the use of Avastin.
      • Reliability means that similar responses are given for similar questions, and that similar responses are given over time
      • Validity means that the questions are in fact related to the desired information
      • How do we know that the concept of quality of life is valid and reliable?
      •   F13.1(F17.1.3)How alone do you feel in your life?
      • F15.2 (F3.1.2) How well are your sexual needs fulfilled?
      • F15.4 (F3.2.3) Are you bothered by any difficulties in your sex life?
      • F14.1(F18.1.2)Do you get the kind of support from others that you need?
      • F14.2(F18.1.5)To what extent can you count on your friends when you need them?
      • F13.3(F17.2.3)How satisfied are you with your personal relationships?
      • F15.3(F3.2.1) How satisfied are you with your sex life?
      • F14.3(F18.2.2)How satisfied are you with the support you get from your family?
      • F14.4(F18.2.5)How satisfied are you with the support you get from your friends?
      • F13.4(F19.2.1)How satisfied are you with your ability to provide for or support others?
      • F13.2(F17.2.1)Do you feel happy about your relationship with your family members?
      • G1(G1.1) How would you rate your quality of life?
      • F15.1(F3.1.1) How would you rate your sex life?
      • Perfect health
      • Quality of life
      • Disability
      • Utility
      • Extraordinary medical care
      • Palliative care
      • Comfort care
      • What is the best way to determine the patient’s perspective on these terms?
      • To find out how individuals define these terms, why not use short response, open-ended questions?
      • We need to discover the differing perspectives
      • Use text analysis to examine the results (SAS Text Miner)
      • Perfect health is hard, if not impossible, to define.
      • Some argue that there are health states worse than death, and that therefore there should be negative values possible on the health spectrum
      • Determining the level of health depends on measures that some argue place disproportionate importance on physical pain or disability over mental health.
      • The effects of a patient's health on the quality of life of others (e.g. caregivers or family) do not figure into these calculations.
      • How do you define it?
      • Can we really measure something we cannot define?
      • Time Trade Off (TTO): Respondents are asked to choose between remaining in a state of ill health for a period of time, or being restored to perfect health but having a shorter life expectancy.
      • This trade off can be considered the presentation of a "happy pill" guaranteed to give you perfect health for a period of time after which you drop dead while you can have twice as long to live but you have to put up with a chronic illness.
      • Standard gamble (SG): Respondents are asked to choose between remaining in a state of ill health for a period of time, or choosing a medical intervention, which has a chance of either restoring them to perfect health, or killing them.
      • Very few patients, when faced with this actual choice, decide to avoid the treatment. However, comparative effectiveness analysis often disallows a choice by rationing the treatment for older age groups, or those in need of expensive medical interventions.
      • Visual Analogue Scale(VAS): Respondents are asked to rate a state of ill health on a scale from 0 to 100, with 0 representing death and 100 representing perfect health.
      • Patients can suffer from untreated depression that will reduce their definition on the scale. Those who are healthy can define artificially low values because of a lack of understanding of a disease state.
      • Very few patients refuse treatment because it is not worth a diminished quality of life
      • Most patients want the treatment
      • Hypothetical measurements cannot substitute for actual patient choices
      • My spouse helps me to cope with my disease and treatment by....
      • My children help me with my disease and treatment by....
      • My friends help me with my disease and treatment by....
      • My faith helps me with my disease and treatment by....
      • We can then examine the concepts that are related to or interpreted as “perfect health”
      • We can see how these terms are defined by individuals and to see if patients define them differently compared to healthy individuals
      • We can also investigate the terms from different perspectives-provider, insurer, patient, economist, etc.
      • We can look at real situations rather than hypothetical scenarios.
      • 85 nursing students were surveyed using open-ended questions. They were asked to define the concepts of “perfect health, “health”, and “quality of life”.
      • The short responses were analyzed using text mining. The responses are grouped based upon the content, using natural language processing.
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      • Since Avastin is given in combination with other drugs (specifically 5FU, Leucovorin, and oxiliplatin for colon cancer), it is difficult to investigate adverse events of just Avastin alone separate from the adverse events of the other drugs.
      • The market basket analysis demonstrates this inter-connectedness through a link graph showing how individual reports provide multiple drugs as possible causes.
      • The Adverse Event Report System (AERS) sponsored by the CDC (Centers for Disease Control) is a means of providing voluntary reports of adverse events after a drug is approved for use. It is located at http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/default.htm
      • For the year 2009, there are over 5000 reports related to the first-line treatment for metastatic colon cancer.
    •  
      • There are 4 discernable patterns for complaints concerning the drugs used for first-line treatment of metastatic colon cancer. There are a handful of reports that do not fit one of these four patterns.
      • The four patterns center at
        • Abnormal blood tests and throat tightness
        • Stent placement and infection
        • Cell counts and sepsis
        • Jaundice
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      • Abnormal blood tests are very typical of chemotherapy generally and it would be difficult to attribute it to any one drug.
    •  
      • Stents are very typical in medical treatment generally and cannot be attributed specifically to chemotherapy, or to any one drug for chemotherapy.
    •  
      • This time, the complaints are for white cells; very typical in chemotherapy generally.
      • Sepsis is a very serious infection and could be attributed to chemotherapy and could be related to the low cell counts. It is an important hypothesis that should be investigated in more detail.
    • Jaundice is a result of disease progression rather than of chemotherapy, especially for metastatic colon cancer.
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      • This is a serious problem and could be the result of chemotherapy or the result of previously undiscovered heart disease
      • It should also be considered a generated hypothesis that needs to be investigated in more detail.
    • Cell counts are a problem of chemotherapy; liver abscess is a sign of disease progression. Neither can be attributed to a specific drug.
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      • Severe reactions are known for Oxaliplatin.
      • Medications such as Decadron and Benadryl are used to prevent these severe reactions
      • They can be directly attributed to Oxaliplatin
    •  
      • Infection is common because of low white cell counts
      • This is very common with chemotherapy generally and can be a cumulative result of multiple medications
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      • There are a few complaints related to bleeding, which is a known side effect of Avastin
      • The remaining complaints are quite similar to those for 5FU and Oxiliplatin, such as cell counts.
      • However low cell counts are more likely from the 5FU and the Oxiliplatin rather than the Avastin.
    •  
      • Because the AERS database is voluntary, the results of this analysis cannot be considered definitive.
      • However, the analysis can generate hypotheses that can be verified through the evaluation of the electronic medical records of patients treated for metastatic colon cancer.
      • Comparative effectiveness analysis is best when it compares drug A to drug B when A is both cheaper and more effective. Yet this rarely happens in reality.
      • To be valid, all aspects of treatment should be considered.
      • When comparing treatment to no treatment, analysis involves vague terminology that can mean different things to different people
      • Text analysis can help to determine just what the different terminologies mean to different people
      • Patricia Cerrito, PhD, Professor of Mathematics at the University of Louisville, has spent over 20 years investigating health outcomes using data mining tools. She has published a number of books on the general topic including
        • Cases on Health Outcomes and Clinical Data Mining: Studies and Frameworks
        • Text Mining Techniques for Healthcare Provider Quality Determination: Methods for Rank Comparisons
          • Clinical Data Mining for Physician Decision Making and Investigating Health Outcomes: Methods for Prediction and Analysis
      • She has been recognized as one of America’s Elite Educators for her work in training students in health outcomes research
      • For more information, go to www.drpatriciacerrito.com
      • John C. Cerrito, PharmD, Kroger Pharmacy, has practiced pharmacy for over 30 years along with ten years of experience in investigating adverse events and health outcomes research.