// Workers AI · dad joke modeWhat did net treatment benefit say to its friend? "You're caught up in my benefits.
Net Treatment Benefit (NTB) is a statistical measure of treatment effect used in randomized clinical trials. Developed by Marc Buyse for multiple prioritized outcomes, it is derived from Generalized pairwise comparisons (GPC) and summarizes the overall difference between treatments as the net probability that a randomly selected patient receiving the experimental treatment has a better overall outcome than a randomly selected patient receiving the control treatment.[1]
Net Treatment Benefit can summarize treatment effects across multiple clinically relevant outcomes, including efficacy, safety and patient-reported outcomes, while preserving their relative clinical importance through pre-specified prioritization.[2]
Definition
[edit]For two treatment groups, experimental (E) and control (C), the Net Treatment Benefit is defined as
- NTB = P(E > C) − P(C > E)
where P(E > C) is the probability that a randomly selected patient receiving the experimental treatment has a better overall outcome than a randomly selected patient receiving the control treatment, and P(C > E) is the probability of the opposite situation.[1]
The NTB ranges from −1 to +1. A value of zero indicates no overall treatment difference. Positive values favor the experimental treatment, whereas negative values favor the control treatment.
Interpretation
[edit]NTB has a probabilistic interpretation as an absolute measure of treatment effect.
For example, an NTB of 0.15 indicates a net probability of 15% that a randomly selected patient receiving the experimental treatment will have a better overall outcome than a randomly selected patient receiving the control treatment, according to the pre-specified comparison rules.[1]
When multiple outcomes are prioritized, the interpretation remains unchanged. Rather than evaluating a single outcome, the comparison reflects the overall clinical benefit determined by the outcome hierarchy established before the trial. Patients are first compared on the outcome considered most clinically important, with lower-priority outcomes considered only when higher-priority outcomes do not distinguish between patients.[2]
This approach is often described in clinical trials as a hierarchical composite endpoint (HCE), in which multiple outcomes are assessed according to a pre-specified hierarchy. Net Treatment Benefit provides a measure of treatment effect for such prioritized outcome analyses, rather than being an endpoint itself.[3]
Because NTB is expressed on an absolute probability scale, its reciprocal has been proposed as a number needed to treat for the comparison of treatment effects on several prioritized outcomes.[4]
Comparison with the Win Ratio
[edit]The Win ratio is another summary measure of treatment effect derived from generalized pairwise comparisons for multiple prioritized outcomes.[5]
The two measures of treatment effect are based on the same pairwise comparisons, therefore tests of statistical significance are identical for the two measures. The win ratio is calculated as the ratio of favorable to unfavorable comparisons after excluding neutral pairs, whereas NTB is calculated as the difference between the probabilities of favorable and unfavorable comparisons.
Because NTB is expressed as an absolute probability difference, it has a direct probabilistic interpretation. Unlike the Win Ratio, NTB does not ignore neutral comparisons, it eliminates them by subtraction.[2]
Unlike the Win Ratio, Net Treatment Benefit provides a transparent measure of the individual contribution of each outcome in the overall assessment of treatment effect.[4] Authors have discussed limitations of the win ratio as a measure of treatment effect.[6]
Applications
[edit]NTB has been applied in clinical trials evaluating treatments across a range of therapeutic areas, including oncology, cardiovascular disease and rare diseases.[7][2]
Because it can jointly evaluate efficacy, safety and patient-reported outcomes within a single measure of treatment effect, NTB has also been proposed for benefit-risk assessment[8], patient-centered endpoint design[9] and dose optimization[10] in clinical development.
In line with the idea that clinical trials should ask “which treatment is better for patients?”, the Net Treatment Benefit changes the study objective from proving non-inferiority of efficacy alone to demonstrating superiority in overall patient benefit across multiple prioritized efficacy and safety outcomes.[11]
Limitations
[edit]The interpretation of NTB (and Win Ratio) depends on the comparison rules specified before the analysis, including the choice of outcomes, their order of priority and any thresholds defining clinically meaningful differences. NTB may differ across patient populations with different baseline risks, hence the NTB estimated in a specific sample of patients does not generalize to different patient populations.[4]
As with other measures of treatment effect, estimates may differ across study populations with different baseline risks. Calculation, including inferential statistics, may also become computationally intensive in large studies because every patient in one treatment group is compared with every patient in the other.[12]
References
[edit]- 1 2 3 Buyse, Marc (December 30, 2010). "Generalized pairwise comparisons of prioritized outcomes in the two-sample problem". Statistics in Medicine. 29 (30): 3245–3257. doi:10.1002/sim.3923. ISSN 0277-6715. PMID 21170918.
- 1 2 3 4 Verbeeck, Johan; De Backer, Mickaël; Verwerft, Jan; Salvaggio, Samuel; Valgimigli, Marco; Vranckx, Pascal; Buyse, Marc; Brunner, Edgar (September 2023). "Generalized Pairwise Comparisons to Assess Treatment Effects". Journal of the American College of Cardiology. 82 (13): 1360–1372. doi:10.1016/j.jacc.2023.06.047. hdl:1942/42083. PMID 37730293.
- ↑ Vart, Priya (2025-10-01). "Hierarchical composite endpoints in clinical trials for multimorbid older adults". eClinicalMedicine. 88. Elsevier. doi:10.1016/j.eclinm.2025.103474. ISSN 2589-5370. PMID 40969682.
- 1 2 3 Buyse M, Verbeeck J, Saad ED, De Backer M, Deltuvaite-Thomas V, Molenberghs G (eds.). Handbook of Generalized Pairwise Comparisons: Methods for Patient-Centric Analysis. Chapman & Hall/CRC; 2025.
- ↑ Pocock, S. J.; Ariti, C. A.; Collier, T. J.; Wang, D. (January 2012). "The win ratio: a new approach to the analysis of composite endpoints in clinical trials based on clinical priorities". European Heart Journal. 33 (2): 176–182. doi:10.1093/eurheartj/ehr352. ISSN 0195-668X. PMID 21900289.
- ↑ Butler, Javed; Stockbridge, Norman; Packer, Milton (May 2024). "Win Ratio: A Seductive But Potentially Misleading Method for Evaluating Evidence from Clinical Trials". Circulation. 149 (20): 1546–1548. doi:10.1161/CIRCULATIONAHA.123.067786. ISSN 0009-7322. PMID 38739696.
- ↑ Péron, Julien; Roy, Pascal; Conroy, Thierry; Desseigne, Françoise; Ychou, Marc; Gourgou-Bourgade, Sophie; Stanbury, Trevor; Roche, Laurent; Ozenne, Brice; Buyse, Marc (December 2016). "An assessment of the benefit-risk balance of FOLFIRINOX in metastatic pancreatic adenocarcinoma". Oncotarget. 7 (50): 82953–82960. doi:10.18632/oncotarget.12761. ISSN 1949-2553. PMC 5347744. PMID 27765912.
- ↑ Backer, Mickaël De; Sengar, Manju; Mathews, Vikram; Salvaggio, Samuel; Deltuvaite-Thomas, Vaiva; Chiêm, Jean-Christophe; Saad, Everardo D.; Buyse, Marc (April 2024). "Design of a clinical trial using generalized pairwise comparisons to test a less intensive treatment regimen". Clinical Trials. 21 (2). London, England: 180–188. doi:10.1177/17407745231206465. ISSN 1740-7753. PMC 11195000. PMID 37877379.
- ↑ Saúde-Conde, R.; Vandamme, T.; Backer, M. De; Martinive, P.; Covas, A.; Deleporte, A.; Dermine, A.; Forget, F.; Geboes, K.; Gilliaux, Q.; Gokburun, Y.; Gonne, E.; Joye, I.; Lecomte, S.; Liberale, G. (2024-06-01). "Efficacy and safety of short-course radiotherapy versus total neoadjuvant therapy in older rectal cancer patients: a randomised pragmatic trial (SHAPERS)". ESMO Gastrointestinal Oncology. 4 100067. Elsevier. doi:10.1016/j.esmogo.2024.100067. ISSN 2949-8198. PMC 12836652. PMID 41648032.
- ↑ Tannock, Ian F.; Vries, Elisabeth G. E. de; Fojo, Antonio; Buyse, Marc; Moja, Lorenzo (2025-03-01). "Dose optimisation to improve access to effective cancer medicines". The Lancet Oncology. 26 (3). Elsevier: e171–e180. doi:10.1016/S1470-2045(24)00648-X. hdl:11370/4a67bc5e-85c6-4456-b2e0-b3acce83c06f. ISSN 1470-2045. PMID 40049207.
- ↑ Tannock, Ian F.; Buyse, Marc; Backer, Mickael De; Earl, Helena; Goldstein, Daniel A.; Ratain, Mark J.; Saltz, Leonard B.; Sonke, Gabe S.; Strohbehn, Garth W. (2024-10-01). "The tyranny of non-inferiority trials". The Lancet Oncology. 25 (10). Elsevier: e520–e525. doi:10.1016/S1470-2045(24)00218-3. ISSN 1470-2045. PMID 39362263.
- ↑ Ozenne, Brice; Budtz-Jørgensen, Esben; Péron, Julien (November 2021). "The asymptotic distribution of the Net Benefit estimator in presence of right-censoring". Statistical Methods in Medical Research. 30 (11): 2399–2412. doi:10.1177/09622802211037067. ISSN 0962-2802. PMID 34633267.