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

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Presentation on theme: "Terminology and Jargon Demystified Meg Franklin, PharmD, PhD Franklin Pharmaceutical Consulting March 27, 2015 1."— Presentation transcript:

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

2 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

3 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.

4 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

5 ​ Types of Studies ​ Metrics to Report Study Design

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

7 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

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

9 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

10 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

11 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

12 ​ Types of Analyses ​ Trends Cost Analyses

13 13 Comparison of methodologies Cost Analyses

14 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

15 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

16 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

17 ​ The life of a tree Calculations Decision Analysis

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

19 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.

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

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

22 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

23 23 Common pitfalls ​ Transparency  Assumptions  Inputs  Sources  Calculations Decision Analysis

24 ​ Definitions ​ Trends Economic Models

25 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

26 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.

27 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

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

29 ​ Lessons learned ​ Looking ahead Conclusions

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

31 31 Looking ahead Conclusions


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