Early Health Technology Assessment, Headroom Pricing and Benefit-Package Decisions

อ่านฉบับภาษาไทย (Thai version)

Abstract

Early health technology assessment asks what a developing intervention would need to achieve before wider investment or coverage. It begins with the intended population, current comparator and place in care. Preliminary scenarios can reveal the health gains, prices and evidence that would make further development worthwhile. Headroom is a conditional value-based price ceiling, not an entitlement to reimbursement. We derive that ceiling while keeping other incremental costs signed, so genuine savings increase the available price. An illustrative calculation shows the required price reduction when a proposal exceeds its ceiling. A separate calculation distinguishes willingness to pay for health from the health displaced by spending elsewhere. Neither calculation settles affordability, delivery capacity or fairness. Decisions therefore need explicit options, including negotiation, targeted access, further research and deferral. Each option should state the evidence still needed and a trigger for reconsideration. Early modeling is most useful when it changes the development or evidence plan before uncertainty becomes an expensive commitment.

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Start with a decision the model can change

Early health technology assessment (early HTA) evaluates a technology before all definitive evidence is available [1]. Name the target population, current comparator and place in care. State whether the decision concerns further development, a study, a price offer or coverage. Each decision has a different evidence requirement.

Use preliminary effectiveness scenarios with clear assumptions about adherence, duration and adverse effects. An optimistic technical performance result is not automatically a patient health gain. Include implementation and downstream resource changes. Test the comparator that would actually be displaced.

Keep the other-cost term signed

Headroom is the highest price consistent with the specified value criterion [2]. Let $q>0$ be purchased units, $P$ the incremental intervention price per unit, and $\Delta C_{other}$ signed other incremental costs. Then $\Delta C=qP+\Delta C_{other}$. Here comparator purchase costs are already removed in the definition of incremental price.

At willingness-to-pay $\lambda$, incremental net monetary benefit is $INMB=\lambda\Delta E-\Delta C$. Requiring nonnegative INMB gives $P_{max}=(\lambda\Delta E-\Delta C_{other})/q$. Negative other costs mean savings and increase headroom. If a positive saving is called $S$, the numerator becomes $\lambda\Delta E+S$.

This incremental price ceiling differs from the total selling price. Manufacturing margin additionally needs production cost, while commercial profit needs volumes and other business costs. A negative ceiling can mean no positive incremental price is justified under the scenario. It does not establish technical failure or a final coverage decision.

Calculate headroom with signed costs

# Writes: stdout only; coordinator captures results.
willingness_to_pay <- 160000
health_gain <- 0.400
other_cost <- -10000
units <- 1
proposed_price <- 100000
monetized <- willingness_to_pay * health_gain
ceiling <- (monetized - other_cost) / units
reduction <- 100 * (1 - ceiling / proposed_price)
cat("Monetized gain THB:", monetized, "\n")
cat("Incremental price ceiling THB:", ceiling, "\n")
cat("Price reduction (%):", formatC(reduction, digits=2, format="f"), "\n")

During startup - Warning messages:
1: Setting LC_COLLATE=C.UTF-8 failed
2: Setting LC_CTYPE=C.UTF-8 failed
3: Setting LC_MONETARY=C.UTF-8 failed
4: Setting LC_TIME=C.UTF-8 failed
Monetized gain THB: 64000
Incremental price ceiling THB: 74000
Price reduction (%): 26.00

A willingness-to-pay ceiling is not displaced health

Supply-side opportunity-cost $k$ measures spending required to produce health in displaced services. It is distinct from willingness-to-pay $\lambda$ [4]. Net health benefit is $NHB=\Delta E-\Delta C/k$, with matching cost, population and time definitions.

Separately assume actual total incremental cost 50,000 THB and illustrative k of 100,000 THB/QALY. Displaced health is 0.500 QALY. With gain 0.400, NHB is -0.100 QALY. This comparison uses a different price scenario from the ceiling calculation.

Budget impact also requires eligible numbers, uptake and annual cash timing. Greater uptake increases the total budget even when value per person is unchanged. Fixed setup costs or scale effects can also alter per-person value. Examine access and displaced services by population group before adding equity judgments.

Keep opportunity cost separate

# Writes: stdout only; coordinator captures results.
incremental_cost <- 50000
opportunity_cost_k <- 100000
health_gain <- 0.400
displaced <- incremental_cost / opportunity_cost_k
net_health <- health_gain - displaced
cat("Displaced QALY:", formatC(displaced, format="f", digits=3), "\n")
cat("Net health QALY:", formatC(net_health, format="f", digits=3), "\n")

During startup - Warning messages:
1: Setting LC_COLLATE=C.UTF-8 failed
2: Setting LC_CTYPE=C.UTF-8 failed
3: Setting LC_MONETARY=C.UTF-8 failed
4: Setting LC_TIME=C.UTF-8 failed
Displaced QALY: 0.500
Net health QALY: -0.100

Turn the ceiling into conditional options

A developer go/no-go decision weighs technical feasibility and the evidence investment against plausible value. A payer additionally assesses the available evidence, affordability, implementation and fairness [3]. An early model cannot replace either decision process.

Vary effectiveness, duration, unit use, other costs and uptake in coherent scenarios. Consider negotiation, targeted access, access conditional on evidence collection, further research or deferral. State which uncertainty each option addresses. Specify monitoring of outcomes, uptake and expenditure, with an agreed trigger for review.

Read [[icer-dominance-ce-plane-and-thresholds]], [[decision-trees-markov-models-and-psa]] and [[budget-impact-equity-and-limits-of-qaly]]. A13 supplies the evidence ledger; A15 develops the policy brief. Explore the Early HTA module in economic-evaluation.

The series, in learning order

1. Health Economics Series Guide: Read the Evidence Before the Costs [[health-economics-series-guide]]
2. Incidence, Prevalence and the Right Denominator: The Epidemiology Beneath Cost-of-Illness and Markov Models [[incidence-prevalence-and-the-right-denominator]]
3. Design, Model and Estimand: What Each Study Design Can Actually Estimate [[study-design-estimand-and-effect-measures]]
4. Risk Ratio, Odds Ratio, Risk Difference and NNT: One 2×2 Table, Four Answers [[risk-ratio-odds-ratio-risk-difference-nnt]]
5. The Hazard Ratio Is Not a Risk Ratio: Proportional Hazards, Restricted Mean Survival Time and Model Inputs [[hazard-ratio-proportional-hazards-rmst]]
6. Chance, Bias and Confounding: Why an Association Is Not Yet a Treatment Effect [[chance-bias-and-confounding]]
7. Beyond the Evidence Pyramid: GRADE Certainty and Choosing Effectiveness Inputs for Economic Models [[evidence-pyramid-versus-grade-certainty]]
8. From Sensitivity to Cost per Case Detected: Predictive Values, Prevalence and the Economics of Screening [[diagnostic-accuracy-predictive-values-screening]]
9. Whose Cost, in Which Year? Perspective, Opportunity Cost, Inflation and Discounting [[costing-perspective-inflation-discounting]]
10. QALY, DALY and the Utilities Behind Them: What Is Actually Being Measured [[qaly-daly-and-utility-elicitation]]
11. ICER, Dominance and the Cost-Effectiveness Plane: Reading the Thai Threshold of 160,000 Baht per QALY [[icer-dominance-ce-plane-and-thresholds]]
12. Decision Trees, Markov Cohorts and Probabilistic Sensitivity Analysis: How a Model Turns Evidence into a Decision [[decision-trees-markov-models-and-psa]]
13. Cost-Effective Is Not Affordable, and Efficient Is Not Fair: Budget Impact and Equity [[budget-impact-equity-and-limits-of-qaly]]
14. Parameter Sourcing, Reference Cost Lists and Utility Catalogues When Local Data Are Sparse
15. Early Health Technology Assessment, Headroom Pricing and Benefit-Package Decisions (this article)
16. Communicating Health Economic Evidence: From Model Outputs to Policy Briefs

Related in the wiki: ICER, Dominance and the Cost-Effectiveness Plane: Reading the Thai Threshold of 160,000 Baht per QALY · Decision Trees, Markov Cohorts and Probabilistic Sensitivity Analysis: How a Model Turns Evidence into a Decision · Cost-Effective Is Not Affordable, and Efficient Is Not Fair: Budget Impact and Equity