IQWiG threshold method for extent of added benefit (minor/considerable/major)
This method helps decide how much better a new drug is compared to standard treatment by classifying its benefit as minor, considerable, or major. It uses statistical data from clinical trials, especially for outcomes that are yes/no in nature (like recovery or no recovery), and sets clear rules to reduce personal judgment in the decision.
At a glance
Use when
When assessing the extent of added benefit of a new drug compared to standard therapy in early benefit assessment, particularly for binary patient-relevant outcomes.
Avoid when
When outcomes are continuous or time-to-event and cannot be meaningfully dichotomized. Also not suitable when effect measures other than relative risk are preferred or when no confidence interval is available.
Inputs
Dossier data from pharmaceutical companies, including clinical trial results in the form of 2×2 tables, relative risk estimates, and 95% confidence intervals for binary patient-relevant outcomes.
Outputs
Classification of added benefit as minor, considerable, or major based on predefined statistical thresholds applied to the confidence interval of the relative risk.
How it works
The method operationalizes the extent of added benefit for binary outcomes using relative risk and 2×2 tables. It classifies treatment effects based on whether the two-sided 95% confidence interval of the relative risk exceeds predefined thresholds away from the null effect (relative risk = 1). This approach ensures a transparent, standardized classification into minor, considerable, or major added benefit, as required under AMNOG for early benefit assessment in Germany.
- HTA domains
- Clinical Effectiveness
- Assumptions
- The method assumes that binary outcomes are patient-relevant and that the relative risk is an appropriate effect measure. It also assumes that the 95% confidence interval provides a reliable estimate of uncertainty, and that thresholds for benefit categories can be objectively defined with minimal value judgments in the initial assessment stage.
- Strengths
- Provides a transparent, standardized, and reproducible approach to classify added benefit. Reduces subjectivity in early assessment. Based on statistical rigor using confidence intervals. Supports consistency across evaluations.
- Limitations
- Limited to binary outcomes and relative risk as effect measure. May not capture clinical relevance fully when effects are small but statistically significant. Thresholds may not reflect all clinical contexts. Does not apply directly to continuous or time-to-event outcomes.
- Also known as
- IQWiG extent of benefit method, IQWiG added benefit classification method, AMNOG benefit extent method
Questions this answers
- › Does the new drug provide a clinically relevant improvement over the standard therapy?
- › How large is the added benefit of the new drug (minor, considerable, or major)?
- › Is the observed treatment effect statistically robust and clinically meaningful?
- › Can the benefit be consistently demonstrated across patient-relevant binary outcomes?
- › How does the magnitude of effect compare to predefined thresholds for benefit categories?
References & sources
Similar by meaning
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