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Terminology and Jargon Demystified Meg Franklin, PharmD, PhD Franklin Pharmaceutical Consulting March 27, 2015 1

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Health Econ 101 Why are we here? Intro table of contents Study Design Types of studies Metrics to report 01 Cost Analyses Usual suspects Trends 02 Decision Analysis The life of a tree Calculations 03 Economic Models Definitions Trends 04 Conclusions Lessons learned Looking ahead 05

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3 Joke of the Day How many pharmacoeconomists does it take to change a light bulb? Four 1 to estimate the cost of the new light bulb 1 to estimate the life expectancy of this new light bulb 1 to estimate the QOL associated with the light from the new light bulb 1 to package the information so that it convinces the healthcare decision- maker to take out the old light bulb and put in a new one.

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4 Why are we here? Improve writing Communicate effectively Share ideas and experiences Develop strategies for publications Introduction Calvin and Hobbes, by Bill Watterson Introduction Focus on: 1.Nomenclature 2.What to report 3.Common pitfalls

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Types of Studies Metrics to Report Study Design

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6 Observational study designs Study Design Adapted from Figure 2-5 in Basic & Clinical Biostatistics (4 th Ed).

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7 Observational Studies Basic & Clinical Biostatistics (4 th Ed). Study Design DefinitionCommon uses Case seriesReports characteristics of a small groupHypothesis generation Cross-sectionalReports data on a group of subjects at one time rather than over a period of time Describes what is happening right now; hypothesis generating Case-controlBegin with the absence or presence of an outcome and then look backward in time to try to detect possible causes or risk factors What happened? CohortBegins with the exposure and looks forward longitudinally for the outcome What will happen? Observational study designs Study Design

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8 Observational Studies Basic & Clinical Biostatistics (4 th Ed). Study Design Observational Study Designs Study Design

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9 Commonly reported metrics P values and CIs If possible, report both Confidence intervals tell you everything you need to know Significance Idea of the range RR and ORs When are they alike? Risk vs Odds Significance NNT/NNH Resonate with clinicians Dichotomous data required Clinical significance vs statistical significance Study Design

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10 Quick reference for formulas Basic & Clinical Biostatistics (4 th Ed). Study Design Experimental event rate (EER) =A / (A + B) Control event rate (CER) = C/(C + D) Absolute risk reduction (ARR) =|EER – CER| Number needed to treat (NNT) =1/ARR Relative risk reduction (RRR) =|EER-CER|= ARR CER CER Relative risk (RR) =EER= [A/(A+B)] CER [C/(C+D)] Odds ratio (OR) =(A/(A+C)/[C/(A+C)] =A/C =AD [B/(B+D)]/[D/(B+D)] B/D BC Study Design

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11 Common pitfalls Terminology Efficacy vs effectiveness Significance based on CIs Crossing 0 or 1 (depending on measurement) When metrics can be calculated Type of data Significance Study Design

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Types of Analyses Trends Cost Analyses

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13 Comparison of methodologies Cost Analyses

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14 A word on cost-effectiveness Nomenclature Often times articles will contain cost- effectiveness in the title (or text), when in fact it is really another type of cost analyses. Reporting Determining the cost-effectiveness threshold is still an issue Issues with the cost-effectiveness plane Cost Analyses Drummond et al (1987). Cost Analyses

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15 What should be reported Alternatives to the CE plane Net Health Benefit (NHB) NHB = QALYs – (Cost/ WTP) Net Monetary Benefit (NMB) NMB = QALYs*WTP – Cost Cost-effectiveness acceptability curve (CEAC) CEACs Allow for the comparison of multiple treatment strategies WTP is unknown, and foreign concept to many health professionals Cost Analyses http://www.jmir.org/article/viewFile/2059/1/21640

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16 Common pitfalls Terminology Type of study Discounting Time periods beyond 1 year should be discounted Deterministic vs Probabilistic Trend towards probabilistic analyses Cost Analyses Study Design

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The life of a tree Calculations Decision Analysis

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18 Anatomy of a tree Decision Analysis Choice Node Chance Node Terminal Node Decision Analysis

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19 Example Decision Analysis Example Scenario: Given the cost of an antibiotic, the probability of success, the probability of an adverse event, and the cost of treating the adverse event, we can construct a decision tree. Example Scenario: Given the cost of an antibiotic, the probability of success, the probability of an adverse event, and the cost of treating the adverse event, we can construct a decision tree.

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20 Example Decision Analysis Rascati KL. Essentials of Pharmacoeconomics. Philadelphia, PA: Lippencott Williams & Wilkins. 2009. Decision Analysis

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21 Example Decision Analysis 21 Rascati KL. Essentials of Pharmacoeconomics. Philadelphia, PA: Lippencott Williams & Wilkins. 2009.

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22 What to report Ideally, a picture of the decision tree is included Probabilities and Costs should be transparent Assumptions and sources should be relevant and accessible Decision Analysis

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23 Common pitfalls Transparency Assumptions Inputs Sources Calculations Decision Analysis

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Definitions Trends Economic Models

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25 Evolution of models Deterministic All data is known beforehand Once you start the process, you know exactly what is going to happen Example: Predicting the amount of money in a bank account. If you know the initial deposit and the interest rate, then you can determine the amount of the account after one year Deterministic All data is known beforehand Once you start the process, you know exactly what is going to happen Example: Predicting the amount of money in a bank account. If you know the initial deposit and the interest rate, then you can determine the amount of the account after one year Probabilistic Element of chance is involved You know the likelihood that something will happen, but you don’t know when it will happen. Example: Roll a die until it comes up ‘5’. In each roll, the probability that it comes up ‘5’ is 1/6 Don’t know exactly when it will be ‘5’, but we can predict this fairly well.. Probabilistic Element of chance is involved You know the likelihood that something will happen, but you don’t know when it will happen. Example: Roll a die until it comes up ‘5’. In each roll, the probability that it comes up ‘5’ is 1/6 Don’t know exactly when it will be ‘5’, but we can predict this fairly well.. Economic Models http://people.qc.cuny.edu/faculty/christopher.hanusa/courses/245sp11/Documents/245ch5-3.pdf Economic Models

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26 Markov models Approximately 50- 60% of economic models now are Markov models. When should you use a Markov model? A decision tree becomes too complex The timeframe for the analysis is lengthy Transitions between health states are possible (e.g. recurrent events) Modeling a complex disease Probabilities change over time Economic Model Economic Models Decision models embed Markov processes. Monte Carlo simulations are often used to solve Markov models.

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27 What to report At a minimum, the write-up should include: Type of model Assumptions Inputs Results Limitations Economic Models http://www.spandidos-publications.com/article_images/mco/1/1/MCO-01-01-0175-g00.jpg Economic Models

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28 Common pitfalls Transparency Assumptions Inputs Sources Calculations Terminology Rate vs probability Economic Models

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Lessons learned Looking ahead Conclusions

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30 Lessons learned Conclusions Helpful resources http://www.pharmacy.arizona.edu/ centers/hope/training-programs

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31 Looking ahead Conclusions

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