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Terminology and Jargon Demystified

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

2 03 01 04 02 05 Intro table of contents
Health Econ Why are we here? 03 Decision Analysis The life of a tree Calculations 01 Study Design Types of studies Metrics to report 04 Economic Models Definitions Trends 02 Cost Analyses Usual suspects Trends 05 Conclusions Lessons learned Looking ahead

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

5 Study Design Types of Studies Metrics to Report

6 Observational study designs
Cross-sectional Case-Control Cohort Today Historical Control Study Design Adapted from Figure 2-5 in Basic & Clinical Biostatistics (4th Ed).

7 Observational Studies
Study Design Observational study designs Observational Studies Study Design Definition Common uses Case series Reports characteristics of a small group Hypothesis generation Cross-sectional Reports 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-control Begin 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? Cohort Begins with the exposure and looks forward longitudinally for the outcome What will happen? Study Design Basic & Clinical Biostatistics (4th Ed).

8 Observational Studies
Study Design Observational Study Designs Observational Studies Study Design Population size Longitudinal Direction of observation Comparison Group Case series Small Yes, but short Prospective No Cross-sectional Big One point in time Case-control Yes Retrospective Cohort Usually Study Design Basic & Clinical Biostatistics (4th Ed).

9 Commonly reported metrics
Study Design 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 Quick reference for formulas
Study Design Quick reference for formulas Disease No Disease Risk factor present A B A + B Risk factor absent C D C + D A + C B + D 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 Basic & Clinical Biostatistics (4th Ed).

11 Common pitfalls Study Design Terminology Significance based on CIs
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 Cost Analyses Types of Analyses Trends

13 Comparison of methodologies
Cost Analyses Comparison of methodologies Methodology Cost Outcome CMA Dollars Clinical measure CEA CBA CUA QALYs CCA Multiple (any of the above) Cost Analyses

14 A word on cost-effectiveness
Cost Analyses 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).

15 What should be reported
Cost Analyses 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

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

17 Decision Analysis The life of a tree Calculations

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

19 Example Example Scenario:
Decision Analysis Example 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. Specify Possible Costs, Outcomes, and Probabilities For each option, info should be obtained for the probability of occurrence and the consequences of the occurrence Probabilities are assigned for each branch of the chance nodes, and the sum must add up to 1 Consequences are reported as monetary outcomes, health-related outcomes, or sometimes both Articles should include a listing of inputs used in the analysis as well as where or how the estimates were obtained Billomycin Megacillin Probability of Clinical Success 90% 80% Cost of Antibiotic per Course of Therapy $600 $500 Probability of Adverse Events 10% 15% Cost of Treating Adverse Events $1,000 Decision Analysis

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

21 Example Decision Analysis Cost Probability Cost * Probability
Billomycin Success with no adverse events $600 0.81 $486 Success with adverse events $1,600 0.09 $144 Failure with no adverse events $54 Failure with adverse events 0.01 $16 Total for Billomycin 1 $700 Megacillin $500 0.68 $340 $1,500 0.12 $180 0.17 $85 0.03 $45 Total for Megacillin $650 21 Decision Analysis Rascati KL. Essentials of Pharmacoeconomics. Philadelphia, PA: Lippencott Williams & Wilkins

22 What to report Decision Analysis
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 Common pitfalls Transparency Decision Analysis Assumptions Inputs
Sources Calculations Decision Analysis

24 Economic Models Definitions Trends

25 Evolution of models Economic Models Deterministic Probabilistic
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.. Economic Models

26 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 Decision models embed Markov processes. Monte Carlo simulations are often used to solve Markov models. Economic Models

27 What to report Economic Models
At a minimum, the write-up should include: Type of model Assumptions Inputs Results Limitations Economic Models

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

29 Conclusions Lessons learned Looking ahead

30 Lessons learned Helpful resources Conclusions Conclusions

31 Conclusions Looking ahead Conclusions

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