Restricted Net Treatment Benefit in oncology.

OBJECTIVES: The restricted Net Treatment Benefit (rNTB) is a clinically meaningful and tractable estimand of the overall treatment effect assessed in randomized trials when at least one survival endpoint with time restriction is used. Its interpretation does not rely on parametric assumptions such as proportional hazards, can be estimated without bias even in the presence of independent right-censoring, and can include a prespecified threshold of minimal clinically relevant difference. To demonstrate that the rNTB, corresponding to the NTB during a predefined time interval, is a meaningful and adaptable measure of treatment effect in clinical trials. METHODS: In this simulation study, we tested the impact on the rNTB value, estimation, and power of several factors including the presence of a delayed treatment effect, minimal clinically relevant difference threshold value, restriction time value, and the inclusion of both efficacy and toxicity in the rNTB definition. The impact of right censoring on rNTB was assessed in terms of bias. rNTB-derived statistical tests and log rank (LR) tests were compared in terms of power. RESULTS: RNTB estimates are unbiased even in case of right-censoring. rNTB may be used to estimate the benefit/risk ratio of a new treatment, for example, taking into account both survival and toxicity and include several prioritized outcomes. The estimated rNTB is much easier to interpret in this context compared to NTB in the presence of censoring since the latter is intrinsically dependent on the follow-up duration. Including toxicity increases the test power when the experimental treatment is less toxic. rNTB-derived test power increases when the experimental treatment is associated with longer survival and lower toxicity and might increase in the presence of a cure rate or a delayed treatment effect. Case applications on the PRODIGE, Checkmate-066, and Checkmate-067 trials are provided. CONCLUSIONS: RNTB is an interesting alternative to describe and test the treatment's effect in a clear and understandable way in case of restriction, particularly in scenarios with nonproportional hazards or when trying to balance benefit and safety. It can be tuned to take into consideration short- or long-term survival differences and one or more prioritized outcomes.

Generalized pairwise comparisons of prioritized outcomes are a powerful and patient-centric analysis of multi-domain scores.

BACKGROUND: Generalized pairwise comparisons (GPC) can be used to assess the net benefit of new treatments for rare diseases. We show the potential of GPC through simulations based on data from a natural history study in mucopolysaccharidosis type IIIA (MPS IIIA). METHODS: Using data from a historical series of untreated children with MPS IIIA aged 2 to 9 years at the time of enrolment and followed for 2 years, we performed simulations to assess the operating characteristics of GPC to detect potential (simulated) treatment effects on a multi-domain symptom assessment. Two approaches were used for GPC: one in which the various domains were prioritized, the other with all domains weighted equally. The net benefit was used as a measure of treatment effect. We used increasing thresholds of clinical relevance to reflect the magnitude of the desired treatment effects, relative to the standard deviation of the measurements in each domain. RESULTS: GPC were shown to have adequate statistical power (80% or more), even with small sample sizes, to detect treatment effects considered to be clinically worthwhile on a symptom assessment covering five domains (expressive language, daily living skills, and gross-motor, sleep and pain). The prioritized approach generally led to higher power as compared with the non-prioritized approach. CONCLUSIONS: GPC of prioritized outcomes is a statistically powerful as well as a patient-centric approach for the analysis of multi-domain scores in MPS IIIA and could be applied to other heterogeneous rare diseases.

Generalized Pairwise Comparisons to Assess Treatment Effects: JACC Review Topic of the Week.

A time-to-first-event composite endpoint analysis has well-known shortcomings in evaluating a treatment effect in cardiovascular clinical trials. It does not fully describe the clinical benefit of therapy because the severity of the events, events repeated over time, and clinically relevant nonsurvival outcomes cannot be considered. The generalized pairwise comparisons (GPC) method adds flexibility in defining the primary endpoint by including any number and type of outcomes that best capture the clinical benefit of a therapy as compared with standard of care. Clinically important outcomes, including bleeding severity, number of interventions, and quality of life, can easily be integrated in a single analysis. The treatment effect in GPC can be expressed by the net treatment benefit, the success odds, or the win ratio. This review provides guidance on the use of GPC and the choice of treatment effect measures for the analysis and reporting of cardiovascular trials.

On the use of extreme value tail modeling for generalized pairwise comparisons with censored outcomes.

In randomized clinical trials, methods of pairwise comparisons such as the 'Net Benefit' or the 'win ratio' have recently gained much attention when interests lies in assessing the effect of a treatment as compared to a standard of care. Among other advantages, these methods are usually praised for delivering a treatment measure that can easily handle multiple outcomes of different nature, while keeping a meaningful interpretation for patients and clinicians. For time-to-event outcomes, a recent suggestion emerged in the literature for estimating these treatment measures by providing a natural handling of censored outcomes. However, this estimation procedure may lead to biased estimates when tails of survival functions cannot be reliably estimated using Kaplan-Meier estimators. The problem then extrapolates to the other outcomes incorporated in the pairwise comparison construction. In this work, we suggest to extend the procedure by the consideration of a hybrid survival function estimator that relies on an extreme value tail model through the Generalized Pareto distribution. We provide an estimator of treatment effect measures that notably improves on bias and remains easily apprehended for practical implementation. This is illustrated in an extensive simulation study as well as in an actual trial of a new cancer immunotherapy.

The Net Benefit of a treatment should take the correlation between benefits and harms into account.

OBJECTIVE: The assessment of benefits and harms from experimental treatments often ignores the association between outcomes. In a randomized trial, generalized pairwise comparisons (GPC) can be used to assess a Net Benefit that takes this association into account. STUDY DESIGN AND SETTINGS: We use GPC to analyze a fictitious trial of treatment versus control, with a binary efficacy outcome (response) and a binary toxicity outcome, as well as data from two actual randomized trials in oncology. In all cases, we compute the Net Benefit for scenarios with different orders of priority between response and toxicity, and a range of odds ratios (ORs) for the association between these outcomes. RESULTS: The GPC Net Benefit was quite different from the benefit/harm computed using marginal treatment effects on response and toxicity. In the fictitious trial using response as first priority, treatment had an unfavorable Net Benefit if OR < 1, but favorable if OR > 1. With OR = 1, the Net Benefit was 0. Results changed drastically using toxicity as first priority. CONCLUSION: Even in a simple situation, marginal treatment effects can be misleading. In contrast, GPC assesses the Net Benefit as a function of the treatment effects on each outcome, the association between outcomes, and individual patient priorities.

A new measure of treatment effect in clinical trials involving competing risks based on generalized pairwise comparisons.

In survival analysis with competing risks, the treatment effect is typically expressed using cause-specific or subdistribution hazard ratios, both relying on proportional hazards assumptions. This paper proposes a nonparametric approach to analyze competing risks data based on generalized pairwise comparisons (GPC). GPC estimate the net benefit, defined as the probability that a patient from the treatment group has a better outcome than a patient from the control group minus the probability of the opposite situation, by comparing all pairs of patients taking one patient from each group. GPC allow using clinically relevant thresholds and simultaneously analyzing multiple prioritized endpoints. We show that under proportional subdistribution hazards, the net benefit for competing risks settings can be expressed as a decreasing function of the subdistribution hazard ratio, taking a value 0 when the latter equals 1. We propose four net benefit estimators dealing differently with censoring. Among them, the Péron estimator uses the Aalen-Johansen estimator of the cumulative incidence functions to classify the pairs for which the patient with the best outcome could not be determined due to censoring. We use simulations to study the bias of these estimators and the size and power of the tests based on the net benefit. The Péron estimator was approximately unbiased when the sample size was large and the censoring distribution's support sufficiently wide. With one endpoint, our approach showed a comparable power to a proportional subdistribution hazards model even under proportional subdistribution hazards. An application of the methodology in oncology is provided.