Section 6.2: Cost-Effectiveness and QALY Assessments

6.2.1 The “Why”: Moving from “What Is It?” to “How Do I Use It?”

In the previous section, we laid the conceptual foundation. You learned the “Big 4” types of economic analyses (CMA, CBA, CEA, CUA) and the crucial difference between value (what a drug is worth) and affordability (what a plan can pay for). You are now fluent in the high-level language of pharmacoeconomics.

This section is the masterclass. This is the deep, practical, “how-to” guide. We will move from definitions to calculations. You will learn not just what an ICER is, but exactly how to calculate one, how to interpret it, and—most importantly—how to critically appraise one when a drug manufacturer presents it to your P&T committee. We will dissect the single most important and controversial metric in all of health economics: the Quality-Adjusted Life Year (QALY). You will learn what it truly represents, how it is measured, and why it is the engine behind the most significant formulary decisions in modern medicine.

Why is this essential? Because every “Prior Authorization Required” and “Step-Therapy” protocol you encounter is born from these very calculations. A payer’s decision to prefer Drug A over Drug B is not arbitrary; it is a direct consequence of a Cost-Effectiveness Analysis (CEA) or Cost-Utility Analysis (CUA) that concluded Drug A provided a better “value” for their money. To effectively advocate for your patients—to know when to challenge a PA and when to accept the formulary alternative—you must be able to understand, and even replicate, the math that led to that decision. This section is your key to unlocking that black box.

6.2.2 Deep Dive: Cost-Effectiveness Analysis (CEA) Revisited

As we established, a Cost-Effectiveness Analysis (CEA) is the workhorse of P&T committees. It compares two or more interventions, where costs are measured in dollars and outcomes are measured in their natural, clinical units.

The entire analysis hinges on the selection of that one primary outcome. This outcome must be:

1. Clinically Relevant: It must be an outcome that clinicians and patients actually care about. “Reduction in a serum biomarker” is a weak outcome. “Avoided hospitalizations” or “life-years gained” are strong outcomes.
2. Credibly Measured: The data must come from high-quality sources, ideally head-to-head randomized controlled trials (RCTs) or large meta-analyses.
3. Common to Both Interventions: You must be able to measure the same outcome for both the new drug and the old standard of care.

Masterclass Table: Common “Natural Units” (Outcomes) in CEA

As you can see, the major weakness of CEA is that none of these outcomes are comparable. You cannot compare the “value” of a \$50,000 cost per fracture avoided to a \$100,000 cost per cure of Hepatitis C. This is the problem that CUA and the QALY were invented to solve.

6.2.3 The Engine of CEA: A Tutorial on the ICER

The Incremental Cost-Effectiveness Ratio (ICER) is the primary output of 99% of the economic studies you will read. It is absolutely essential that you understand it inside and out. It is not an answer, but a “price.”

• It is NOT the cost of the drug.
• It is NOT the cost of the disease.
• It IS the extra cost to get one extra unit of health outcome by using a new, more expensive therapy instead of the older, cheaper one.

The formula, again, is the change in cost divided by the change in effect:

Tutorial: Calculating and Interpreting a CEA

Let’s walk through a common, practical scenario: a hospital P&T committee is evaluating a new, expensive antibiotic (Newmycin) for treating severe hospital-acquired pneumonia (HAP) compared to the standard generic regimen (Standardmycin).

Perspective: The Hospital (Provider)

Outcome: Clinical Cure Rate (%)

First, you must gather your data. As the clinical pharmacist, you are responsible for this. You find an RCT and also pull your own hospital’s data.

Masterclass Table: Data for HAP Analysis (Hospital Perspective)

A rookie mistake is to only look at the drug cost. A \$3,600 difference! But you are an advanced practitioner. You must calculate the total cost of care from the hospital’s perspective. This includes the cost of failure.

Step 1: Calculate the Total Cost (C) for Each Strategy

You must calculate the “expected cost” for an average patient, which is a weighted average of the cost of success and the cost of failure.

Cost for Standardmycin (Old):

• Cost of Success (70% of patients): \$400 (drug) + (10 days * \$2,500) = \$25,400
• Cost of Failure (30% of patients): \$400 (drug) + (20 days * \$2,500) = \$50,400
• Total Cost (Old): (0.70 * \$25,400) + (0.30 * \$50,400) = \$17,780 + \$15,120 = \$32,900

Cost for Newmycin (New):

• Cost of Success (80% of patients): \$4,000 (drug) + (8 days * \$2,500) = \$24,000
• Cost of Failure (20% of patients): \$4,000 (drug) + (18 days * \$2,500) = \$49,000
• Total Cost (New): (0.80 * \$24,000) + (0.20 * \$49,000) = \$19,200 + \$9,800 = \$29,000

Step 2: Calculate $\Delta C$ and $\Delta E$

• $\Delta C$ (Cost): \$29,000 (New) – \$32,900 (Old) = -\$3,900
• $\Delta E$ (Effect): 80% (New) – 70% (Old) = +10%

Step 3: Analyze the Result

Let’s plot this on our Cost-Effectiveness Plane.

• The $\Delta C$ is negative (it’s cheaper).
• The $\Delta E$ is positive (it’s more effective).

This falls into Quadrant IV: Dominant. No ICER calculation is even needed. The new, expensive-looking drug is actually cost-saving. It is cheaper and better.

6.2.4 The Most Important Metric: A Masterclass on the QALY

Welcome to the most important, most powerful, and most controversial concept in health economics. The Quality-Adjusted Life Year (QALY). As we’ve discussed, its brilliance is that it combines quantity of life (how long) and quality of life (how well) into a single number, allowing us to compare any two health interventions, no matter how different.

Official Definition: A QALY is a measure of disease burden, including both the quality and the quantity of life lived. It is used in economic evaluation to assess the value for money of medical interventions. One QALY is equal to one year of life lived in perfect health.

The formula is deceptively simple:

The “Time” part is easy (e.g., 5 years). The entire multi-billion dollar field of health economics hinges on that second variable: the Utility Value.

Deep Dive: The “Utility” Score

A utility is a number between 0.0 and 1.0 that represents the “preference” for a given state of health.

• 1.0 = Perfect Health
• 0.0 = Death

A chronic health state, like “living with migraine headaches,” might have a utility of 0.85. A more severe state, like “post-stroke, bed-bound,” might have a utility of 0.20. Some states, like “severe, untreatable pain,” can even be valued as worse than death, with a negative utility score.

Example QALY Calculations:

• Living 10 years in perfect health = $10 \text{ years} \times 1.0 = 10.0 \text{ QALYs}$.
• Living 10 years with migraines = $10 \text{ years} \times 0.85 = 8.5 \text{ QALYs}$.
• Living 10 years post-stroke = $10 \text{ years} \times 0.20 = 2.0 \text{ QALYs}$.

A treatment’s benefit is measured by the $\Delta E$ in QALYs. If a new drug for migraines raises a patient’s utility from 0.85 to 0.95 for 10 years, the total health gain is: $(0.95 – 0.85) \times 10 \text{ years} = \mathbf{1.0 \text{ QALY}}$

Tutorial: How Are Utility Scores Actually Measured?

You cannot just ask a patient, “On a scale of 0 to 1, how do you feel?” A utility is not a “feeling” score; it is an economic “preference” score, elicited through gambling-based thought experiments. There are two primary methods.

Method 1: The Standard Gamble (SG) – The “Risky Bet”

This is the gold standard. You force a person to gamble with their life to determine their preference.

The Scenario: A patient has chronic, moderate arthritis. You give them two choices.

The researcher adjusts the probability p up and down until the patient says, “I can’t decide. Both choices feel equal to me.” This is the indifference point.

Let’s say the patient is indifferent when $p = 0.8$ (an 80% chance of a cure, 20% chance of death).
At this point, the “expected utility” of both choices is equal.
$$\text{Utility(A)} = \text{Utility(B)}$$ $$u \times 10 \text{ years} = (p \times 1.0 \times 10 \text{ years}) + ((1-p) \times 0.0 \times 10 \text{ years})$$ $$u \times 10 \text{ years} = p \times 10 \text{ years}$$ $$u = p$$
Therefore, the utility of “living with moderate arthritis” for this patient is 0.8.

Method 2: The Time Trade-Off (TTO) – The “Time Bet”

This method is simpler to ask, as it doesn’t involve the concept of death. You are forcing a patient to “trade” time for quality.

The Scenario: Same patient, moderate arthritis. Two choices.

The researcher adjusts the time x down from 10 until the patient is indifferent.
Let’s say the patient says, “Living 10 years with my arthritis is equal to living 8 years in perfect health.”
At this indifference point:
$$\text{Utility(A)} = \text{Utility(B)}$$ $$u \times 10 \text{ years} = 1.0 \times 8 \text{ years}$$ $$u = \frac{8}{10}$$
Therefore, the utility of “living with moderate arthritis” is 0.8.

Method 3: Standardized Surveys (e.g., EQ-5D)

Because you can’t run a Standard Gamble on 10,000 trial patients, researchers use pre-validated surveys. The most famous is the EQ-5D. It asks patients to rate their health today on 5 dimensions:

1. Mobility: (No problems, Slight problems, Moderate problems, Severe problems, Unable to walk)
2. Self-Care: (No problems, Slight problems, Moderate problems, Severe problems, Unable to wash/dress)
3. Usual Activities: (No problems, Slight problems, Moderate problems, Severe problems, Unable to perform)
4. Pain/Discomfort: (No pain, Slight pain, Moderate pain, Severe pain, Extreme pain)
5. Anxiety/Depression: (Not anxious, Slightly anxious, Moderately anxious, Severely anxious, Extremely anxious)

A patient’s answers (e.g., “1-1-2-3-1”) create a 5-digit health state. This code is then plugged into a country-specific “algorithm” (that was created using Standard Gamble or TTO on a general population) to generate a single utility score. This is the main method used in clinical trials to calculate QALYs.

6.2.5 Putting It All Together: The CUA Tutorial

Now you are ready to conduct a full Cost-Utility Analysis (CUA). The output is an ICER, but the “E” is always QALYs. It is often called an ICUR (Incremental Cost-Utility Ratio).

Scenario: You are on the P&T committee for a large national payer. You are evaluating NewDrug-RA (a new biologic for Rheumatoid Arthritis) against the standard of care, Standard-Bio (an older biologic).

Perspective: Payer

Outcome: QALYs

Masterclass Table: Data for RA Analysis (Payer Perspective)

Step 1: Calculate the Total Cost (C) for Each Strategy (per year)

• Total Cost (Old): \$30,000 (drug) + \$5,000 (medical) = \$35,000
• Total Cost (New): \$50,000 (drug) + \$3,000 (medical) = \$53,000

Step 2: Calculate the Total Effect (E) in QALYs for Each Strategy (per year)

• Total QALYs (Old): 1 year * 0.65 (utility) = 0.65 QALYs
• Total QALYs (New): 1 year * 0.75 (utility) = 0.75 QALYs

Step 3: Calculate $\Delta C$ and $\Delta E$

• $\Delta C$ (Cost): \$53,000 (New) – \$35,000 (Old) = \$18,000
• $\Delta E$ (Effect): 0.75 QALYs (New) – 0.65 QALYs (Old) = 0.10 QALYs

Step 4: Calculate the ICUR (ICER per QALY)

Step 5: The Payer Decision

You now have your final “price tag.” The P&T committee is told: “To gain one extra QALY for our RA patients, it will cost us \$180,000.”

Now, the committee must compare this to their Willingness-to-Pay (WTP) Threshold.

• Let’s assume the plan’s standard WTP threshold is \$150,000 per QALY.
• The ICUR (\$180,000) is ABOVE this threshold.

The Real-World Consequence:
The P&T committee votes that NewDrug-RA is “not cost-effective” at its current price. They will not make it a preferred agent. It will be placed on a non-preferred tier (e.g., Tier 4 or 5) with a high co-pay, and it will require a Prior Authorization with “Step-Therapy”.

The PA form you will receive will state: “Patient must have a documented trial and failure of at least two preferred agents (e.g., Standard-Bio) before NewDrug-RA will be approved.”

You now understand the exact math that created the administrative barrier you face every single day at the pharmacy counter. It was not an arbitrary clinical decision; it was a formal, evidence-based economic decision.

6.2.6 Your Role as the Clinical Skeptic: How to Critique a PE Study

You will never have to create one of these complex models from scratch. But you will absolutely be expected to read, understand, and critique them. When a manufacturer’s Managed Care Liaison presents a new drug to your P&T committee, they will present a glossy, beautiful CUA study that (coincidentally) shows their new drug is incredibly cost-effective.

Your job is to be the professional skeptic. Your job is to find the flaws. All the “secret ingredients” in a model can be manipulated to get a favorable result. You must know where to look.

6.2.7 Section Summary: You Are the Value Translator

You have now completed the most academically intensive part of this module. You have moved from a surface-level understanding to a deep, practical mastery of Cost-Effectiveness and Cost-Utility analysis. You understand that these are not abstract academic exercises; they are the financial engines that directly create the formularies, PA requirements, and step-therapies that define your daily workflow.

You’ve mastered the key concepts:

• CEA: The workhorse for comparing two drugs with the same “natural unit” (e.g., mmHg).
• CUA: The “universal translator” that compares any two interventions using QALYs.
• QALY: The master-metric of (Utility Score x Years of Life), where “utility” is a preference score derived from methods like the Standard Gamble or Time Trade-Off.
• ICER/ICUR: The final “price tag” of $\frac{\Delta C}{\Delta E}$, which represents the cost per additional unit of health gained.
• WTP Threshold: The payer’s “line in the sand” (e.g., \$150,000/QALY) that determines if an ICER is “cost-effective.”
• Critical Appraisal: Your role as the clinical skeptic, checking the model’s perspective, comparators, and assumptions to validate its conclusion.

You are now fully equipped to be the translator. You can read a manufacturer’s economic model, critique its assumptions, and explain to your P&T committee (in plain English) what the real-world value and financial impact will be. In the next section, we will see how these models are put into direct action through the most common payer strategies: step-therapy and utilization review.