Cost-Consequence Analysis

Definition and scope

An economic comparison that displays incremental costs and multiple relevant consequences separately, without combining all outcomes into one ratio or utility-weighted summary. The target population, use, and analysis remain protocol decisions; this card explains the choices and their consequences rather than approving a study.

Operational specification

Specify the decision population, alternatives, perspective, horizon, resource valuation, and each outcome with its unit and direction of benefit. Separate clinical, patient-reported, utilization, and financial consequences. State which outcomes overlap so downstream users do not add the same benefit twice.

Analysis and diagnostics

Present adjusted or otherwise justified contrasts and uncertainty for every consequence and cost category. Maintain an evidence/assumption column and distinguish unadjusted descriptive differences from causal effects. An outcome dashboard does not itself supply decision weights; show the trade-offs rather than hiding an implicit aggregate ranking.

When NOT to use and common errors

Do not interpret a cost-consequence table as proof of cost-effectiveness, add outcomes with incompatible units, omit unfavorable consequences, or equate a larger number of reported outcomes with greater benefit.

Worked example and implementation boundary

A synthetic service costs 1200 per patient versus 1000 for usual care and avoids 0.1 hospital admissions per patient. Calculate the incremental cost while keeping the admission contrast in its own column. Incremental cost is 200 per patient; the separate admission consequence is 0.1 fewer admissions, not a monetized benefit. The code performs that explicitly stated calculation with synthetic inputs. It is not a complete fitted analysis and does not demonstrate the identification assumptions, estimate sampling uncertainty, or certify application in another population.

Data-source and reporting requirements

Preserve the provenance, dates, units, and observation rule for each input. Claims capture services and transactions under observable benefits; absence of a record is not proof that an event or resource use did not occur. EHR and registry measurements depend on the collection process and may miss care outside the system. Linked sources add reconciliation and selection questions. Primary collection can improve a missing domain but still requires valid measurement and follow-up. Report which of these limitations affects the stated use, which evidence supports the chosen inputs, and what remains uncertain.

Worked example

Scenario

A synthetic service costs 1200 per patient versus 1000 for usual care and avoids 0.1 hospital admissions per patient. Calculate the incremental cost while keeping the admission contrast in its own column.

Dataset

Synthetic inputs with units defined in the scenario; no patient data.

Steps

Result

Incremental cost is 200 per patient; the separate admission consequence is 0.1 fewer admissions, not a monetized benefit.

Trade-offs

Code example

new_cost = 1200
usual_cost = 1000
result = new_cost-usual_cost
assert abs(result - 200) < 1e-9
print(result)

new_cost <- 1200
usual_cost <- 1000
result <- new_cost-usual_cost
stopifnot(abs(result - 200) < 1e-9)
print(result)

Citations

1. [1]Mauskopf JA, et al. The Role of Cost-Consequence Analysis in Healthcare Decision-Making. PharmacoEconomics. 1998.
2. [2]Husereau D, et al. Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 Explanation and Elaboration: A Report of the ISPOR CHEERS II Good Practices Task Force. Value in Health. 2022.