Win statistics (win ratio, win odds, and net benefit) can complement one another to show the strength of the treatment effect on time-to-event outcomes

On approximate equality of Z-values of the statistical tests for win statistics (win ratio, win odds, and net benefit)

Dong et al. (2023) showed that the win statistics (win ratio, win odds, and net benefit) can complement each another to demonstrate the strength of treatment effects in randomized trials with prioritized multiple outcomes. This result was built on the connections among the point and variance estimates of the three statistics, and the approximate equality of Z-values in their statistical tests. However, the impact of this approximation was not clear. This Discussion refines this approach and shows that the approximate equality of Z-values for the win statistics holds more generally. Thus, the three win statistics consistently yield closely similar p-values. In addition, our simulations show an example that the naive approach without adjustment for censoring bias may produce a completely opposite conclusion from the true results, whereas the IPCW (inverse-probability-of-censoring weighting) approach can effectively adjust the win statistics to the corresponding true values (i.e. IPCW-adjusted win statistics are unbiased estimators of treatment effect).

The efficacy of finerenone on hierarchical composite endpoint analysed using win statistics in patients with heart failure and mildly reduced or preserved ejection fraction: A prespecified analysis of FINEARTS-HF

AIMS

FINEARTS-HF demonstrated the efficacy of finerenone in reducing total worsening heart failure (HF) events (first and recurrent) and cardiovascular death, compared to placebo, in patients with HF and mildly reduced or preserved ejection fraction. We examined the effect of finerenone on these events according to their clinical importance using win statistics.

METHODS AND RESULTS

We developed a prespecified hierarchical composite endpoint including the components of the original primary outcome: cardiovascular death (tier 1), total HF hospitalizations (tier 2), and total urgent HF visits (tier 3). For tiers 2 and 3, the number of events was analysed first, followed by the time-to-first event. Because win statistics are affected by the censoring distribution, we assessed the hierarchical composite outcome over a fixed period of 24 months. The 6001 participants analysed were randomized equally to finerenone (n = 3003) or placebo (n = 2998). At 24 months, a total of 825 cardiovascular deaths and worsening HF events were observed in the finerenone group, compared with 1012 events in the placebo group. The win ratio was 1.17 (95% confidence interval [CI] 1.04-1.32) (p = 0.010), demonstrating more wins than losses in the finerenone group. The win odds, corresponding to the treatment effect, was 1.05 (95% CI 1.01-1.09), and the net benefit, corresponding to the absolute risk difference, was 2.6% (95% CI 0.6-4.5%). The win ratio remained above 1.0 from 60 days after randomization and reached a plateau after approximately 12 months. HF hospitalizations contributed more to the overall results than cardiovascular death. The win odds at 12 months was 1.04 (95% CI 1.01-1.08), and when adding the Kansas City Cardiomyopathy Questionnaire total symptom score to the hierarchical endpoint as a continuous variable, that increased to 1.07, which is almost identical to the win ratio due to the decrease in ties.

CONCLUSION

Finerenone treatment led to a significant improvement in a composite hierarchical outcome that incorporated cardiovascular death, total HF hospitalizations, and total urgent HF visits, with early onset of benefit.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov ID NCT04435626.

Dose selection criteria to identify the optimal dose based on ranked efficacy-toxicity outcomes without reliance on clinical utilities

Recently, targeted and immunotherapy cancer treatments have motivated dose-finding based on efficacy-toxicity trade-offs rather than toxicity alone. The EffTox and utility-based Bayesian optimal interval (U-BOIN) dose-finding designs were developed in response to this need, but may be sensitive to elicited subjective design parameters that reflect the trade-off between efficacy and toxicity. To ease elicitation and reduce subjectivity, we propose dose desirability criteria that only depend on a preferential ordering of the joint efficacy-toxicity outcomes. We propose two novel order-based criteria and compare them with utility-based and contour-based criteria when paired with the design framework and probability models of EffTox and U-BOIN. The proposed dose desirability criteria simplify implementation and improve robustness to the elicited subjective design parameters and perform similarly in simulation studies to the established EffTox and U-BOIN designs when the ordering of the joint outcomes is equivalent. We also propose an alternative dose admissibility criteria based on the joint efficacy and toxicity profile of a dose rather than its marginal toxicity and efficacy profile. We argue that this alternative joint criterion is more consistent with defining dose desirability in terms of efficacy-toxicity trade-offs than the standard marginal admissibility criteria. The proposed methods enhance the usability and robustness of dose-finding designs that account for efficacy-toxicity trade-offs to identify the optimal biological dose.

Win Ratio Analyses Using a Modified Hierarchical Composite Outcome: Insights From PARAGLIDE-HF

BACKGROUND

The win ratio (WR) is an emerging alternative for reporting composite outcomes, prioritizing clinically significant events such as mortality while incorporating surrogate measures. However, its benefits should be weighed against limitations, particularly the influence of lower hierarchical outcomes. This secondary analysis of the PARAGLIDE-HF trial performed a WR sensitivity analysis using a modified hierarchical composite outcome to assess the utility of WR sensitivity analysis and the efficacy of sacubitril/valsartan versus valsartan.

METHODS

PARAGLIDE-HF compared sacubitril/valsartan with valsartan in heart failure (HF) patients with ejection fraction >40% (N = 466). A hierarchical outcome in the primary analysis included cardiovascular death, HF hospitalizations, urgent HF visits, and change in N-terminal pro-B-type natriuretic peptide (NT-proBNP), with a 25% decrease considered a win. In the prespecified subgroup with ejection fraction ≤60% (N = 357), sacubitril/valsartan showed a treatment effect on the hierarchical outcome (WR, 1.46; 95% CI, 1.08-1.97). Sensitivity analyses for this subgroup included: (1) excluding NT-proBNP change, (2) substituting the 25% proportion change of NT-proBNP with 10% or 50%, and (3) including renal outcomes within the hierarchical outcome. In addition to the WR, the win odds (WO), in which 50% of the ties are allocated to both the numerator and denominator of the WR-a potentially more suitable modification of the WR that accounts for the presence of ties-were presented.

RESULTS

Excluding NT-proBNP (WR, 1.49; 95% CI, 1.00-2.22; WO, 1.12; 95% CI, 1.00-1.26), adjusting the NT-proBNP threshold from 25% to 10% or 50% (WR, 1.41; 95% CI, 1.06-1.89; WO, 1.27; 95% CI, 1.04-1.56 for 10%; and WR, 1.54; 95% CI, 1.11-2.12; WO, 1.25; 95% CI, 1.06-1.48 for 50%), and incorporating renal outcomes (WR, 1.44; 95% CI, 1.07-1.94; WO, 1.28; 95% CI, 1.05-1.56) consistently favored sacubitril/valsartan.

CONCLUSIONS

Multiple WR sensitivity analyses support a consistent treatment benefit of sacubitril/valsartan versus valsartan in patients with ejection fraction >40% to 60%. Future studies could consider prespecifying WR sensitivity analysis for comprehensive assessment of treatment effects.

TRIAL REGISTRATION

PARAGLIDE-HF; ClinicalTrials.gov ID, NCT03988634 (https://clinicaltrials.gov/study/NCT03988634).

Impact of correlation structure on sample size requirements of statistical methods for multiple binary outcomes: A simulation study

BACKGROUND

In randomized clinical trials, multiple-testing procedures, composite endpoints, and prioritized outcome approaches are increasingly used to analyze multiple binary outcomes. Previous studies have shown that correlations between outcomes influence their sample size requirements. Although sample size is an important factor affecting the choice of statistical methods, the power and required sample sizes of methods for analyzing multiple binary outcomes have yet to be compared under the influence of outcome correlations.

METHODS

We conducted simulations to evaluate the power of co-primary and multiple primary endpoints, composite endpoints, and prioritized outcome approaches based on generalized pairwise comparisons with varying correlations, marginal proportions, treatment effects, and number of outcomes. We then conducted a case study on sample size using a clinical trial of a migraine treatment as an example.

RESULTS

The correlations significantly affected the statistical power and sample size of composite endpoints. The power and sample size of co-primary endpoints remained relatively stable across different correlations, though their power declined substantially when treatment effects were opposite on some components or more than two components were present. While the correlations influenced the power and sample size of all methods assessed, their direction and degree of influence varied between methods. Notably, the method with the greatest power and smallest sample size also differed depending on the correlations. When the correlations were the same between arms, prioritized outcome approaches usually had higher power and smaller sample sizes than other methods.

CONCLUSIONS

Anticipated correlations and their uncertainty should be considered when selecting statistical methods. Overall, co-primary endpoints remain a reliable option for evaluating the superiority of all components, although they are unsuitable for assessing the balance between treatment effects pointing in different directions. Generalized pairwise comparisons offer a useful alternative to deal with multiple prioritized outcomes, often providing the smallest sample sizes when the correlation structures are shared between the arms.

Defining estimand for the win ratio: Separate the true effect from censoring

The win ratio has been increasingly used in trials with hierarchical composite endpoints. While the outcomes involved and the rule for their comparisons vary with the application, there is invariably little attention to the estimand of the resulting statistic, causing difficulties in interpretation and cross-trial comparison. We make the case for articulating the estimand as a first step to win ratio analysis and establish that the root cause for its elusiveness is its intrinsic dependency on the time frame of comparison, which, if left unspecified, is set haphazardly by trial-specific censoring. From the statistical literature, we summarize two general approaches to overcome this uncertainty-a nonparametric one that pre-specifies the time frame for all comparisons, and a semiparametric one that posits a constant win ratio across all times-each with publicly available software and real examples. Finally, we discuss unsolved challenges, such as estimand construction and inference in the presence of intercurrent events.

Clinical trial design, end-points, and emerging therapies in pulmonary arterial hypertension

Clinical trials in pulmonary arterial hypertension (PAH) have led to the approval of several effective treatments that improve symptoms, exercise capacity and clinical outcomes. In phase 3 clinical trials, primary end-points must reflect how a patient "feels, functions or survives". In a rare disease like PAH, with an ever-growing number of treatment options and numerous candidate therapies being studied, future clinical trials are now faced with challenges related to sample size requirements, efficiency and demonstration of incremental benefit on traditional end-points in patients receiving background therapy with multiple drugs. Novel clinical trial end-points, innovative trial designs and statistical approaches and new technologies may be potential solutions to tackle the challenges facing future PAH trials, but these must be acceptable to patients and regulatory bodies while preserving methodological rigour. In this World Symposium on Pulmonary Hypertension task force article, we address emerging trial end-points and designs, biomarkers and surrogate end-point validation, the concept of disease modification, challenges and opportunities to address diversity and representativeness, and the use of new technologies such as artificial intelligence in PAH clinical trials.

Analysis of the PARAGON-HF Study Results Using Win Ratio

BACKGROUND

The PARAGON-HF study (Prospective Comparison of ARNI With ARB Global Outcomes in Heart Failure With Preserved Ejection Fraction) investigated the effect of sacubitril-valsartan in heart failure (HF) with preserved ejection fraction. The results, which were analyzed using conventional statistical methods, did not find a significant reduction in the primary composite end point of cardiovascular death and total hospitalization for HF. Recent clinical trials used win ratio statistics that enable the incorporation of multiple outcome aspects into the primary end point and can detect positive outcomes with fewer patients. In this study, we assessed the effect of sacubitril-valsartan on outcomes using the win ratio to analyze results from patients included in the PARAGON-HF study.

METHODS

In the PARAGON-HF study, 4822 patients with HF with preserved ejection fraction were randomized either to sacubitril-valsartan or valsartan groups. In the present study, the primary outcome was a hierarchical composite of time to cardiovascular death, total number of hospitalization for HF, time to first hospitalization for HF, time to renal composite outcome, and change in the Kansas City Cardiomyopathy Questionnaire total symptom score at 8 months analyzed using a win ratio statistical model.

RESULTS

Using this approach, we found that a greater number of patients who received sacubitril-valsartan experienced clinical benefits compared with those who received valsartan (win ratio, 1.13 [95% CI, 1.04-1.23]; P=0.005). This clinical advantage was evident in patients regardless of whether the left ventricular ejection fraction was above or below the median, that is, the left ventricular ejection fraction of 57%, and regardless of sex (Pinteraction=0.76 for the left ventricular ejection fraction and 0.73 for sex).

CONCLUSIONS

Employing the innovative win ratio approach, sacubitril-valsartan demonstrated significant clinical benefits among patients with HF with preserved ejection fraction. Notably, this benefit was observed irrespective of left ventricular ejection fraction and sex.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT01920711.

Association between COMMD1 gene polymorphism rs11125908 and rheumatoid arthritis in the Cuban population

OBJECTIVE

To evaluate the association of the rs11125908 polymorphism in the COMMD1 gene in the Cuban population with rheumatoid arthritis (RA).

METHODS

In this case-control study, 161 RA patients and 150 control subjects were genotyped for rs11125908 by the allele-specific polymerase chain reaction method. DNA sequencing was used to verify the assignation of the polymorphism. The odds ratios (OR) and their 95% confidence interval were calculated by logistic regression to determine the associations between genotypes and RA using the SNPStats software.

RESULTS

An association of the single nucleotide polymorphism with the disease was found in the overdominant model (p=0.025; OR=1.91) for the AG genotype. Our analyses revealed an association between rs11125908 and the subgroup of patients with swollen joints < median under the codominant model for AG (p=0.034; OR=2.30) and GG genotype (p=0.034; OR=0.82) and with the overdominant model (p=0.01; OR=2.38). The subgroup of patients with an age of onset lower than the mean and AG genotype showed an association in the overdominant model (p=0.027; OR=2.27). Disease activity score 28 with erythrocyte sedimentation rate and disease duration variables were not associated with the rs11125908 polymorphism.

CONCLUSIONS

rs11125908 was associated with RA and with the number of swollen joints and age of onset subgroup analyses. We provide concepts for treatments for RA, based on pharmacological management of COMMD1 expression.

Win Ratio Analyses of Piperacillin-Tazobactam Versus Meropenem for Ceftriaxone-Nonsusceptible Escherichia coli or Klebsiella pneumoniae Bloodstream Infections: Post Hoc Insights From the MERINO Trial

BACKGROUND

Clinical trials of treatments for serious infections commonly use the primary endpoint of all-cause mortality. However, many trial participants survive their infection and this endpoint may not truly reflect important benefits and risks of therapy. The win ratio uses a hierarchical composite endpoint that can incorporate and prioritize outcome measures by relative clinical importance.

METHODS

The win ratio methodology was applied post hoc to outcomes observed in the MERINO trial, which compared piperacillin-tazobactam with meropenem. We quantified the win ratio with a primary hierarchical composite endpoint, including all-cause mortality, microbiological relapse, and secondary infection. A win ratio of 1 would correspond to no difference between the 2 antibiotics, while a ratio <1 favors meropenem. Further analyses were performed to calculate the win odds and to introduce a continuous outcome variable in order to reduce ties.

RESULTS

With the hierarchy of all-cause mortality, microbiological relapse, and secondary infection, the win ratio estimate was 0.40 (95% confidence interval [CI], .22-.71]; P = .002), favoring meropenem over piperacillin-tazobactam. However, 73.4% of the pairs were tied due to the small proportion of events. The win odds, a modification of the win ratio accounting for ties, was 0.79 (95% CI, .68-.92). The addition of length of stay to the primary composite greatly minimized the number of ties (4.6%) with a win ratio estimate of 0.77 (95% CI, .60-.99; P = .04).

CONCLUSIONS

The application of the win ratio methodology to the MERINO trial data illustrates its utility and feasibility for use in antimicrobial trials.

Win Statistics in Observational Cancer Research: Integrating Clinical and Quality-of-Life Outcomes

Background: Quality-of-life metrics are increasingly important for oncological patients alongside traditional endpoints like mortality and disease progression. Statistical tools such as Win Ratio, Win Odds, and Net Benefit prioritize clinically significant outcomes using composite endpoints. In randomized trials, Win Statistics provide fair comparisons between treatment and control groups. However, their use in observational studies is complicated by confounding variables. Propensity score (PS) matching mitigates confounding variables but may reduce the sample size, affecting the power of win statistics analyses. Alternatively, PS matching can stratify samples, preserving the sample size. This study aims to assess the long-term impact of these methods on decision making, particularly in colorectal cancer patients. Methods: A motivating example involves a cohort of patients from the ReSARCh observational study (2016-2021) with locally advanced adenocarcinoma of the rectum, situated up to 12 cm from the anal verge. These patients underwent either a watch-and-wait approach (WW) or trans-anal local excision (LE). Win statistics compared the effects of WW and LE on a composite outcome (overall survival, recurrence, presence of ostomy, and rectum excision). For matched win statistics, we used robust inference techniques proposed by Matsouaka et al. (2022), and for stratified win statistics, we applied the method proposed by Dong et al. (2018). A simulation study assessed the coverage probability of matched and stratified win statistics in balanced and unbalanced groups, calculating how often the confidence intervals included the true values of WR, NB, and WO across 1000 simulations. Results: The results suggest a better efficacy of the LE approach when considering efficacy outcomes alone (WR: 0.47 (0.01 to 1.14); NB: -0.16 (-0.34 to 0.02); and WO: 0.73 (0.5 to 1.05)). However, when QoL outcomes are included in the analyses, the estimates are closer to 1 (WR: 0.87 (0.06 to 2.06); WO: 0.93 (0.61 to 1.4)) and to 0 (NB: -0.04 (-0.25 to 0.17)), indicating a negative impact of the treatment effect of LE regarding the presence of ostomy and the excision of the rectum. Moreover, based on the simulation study, our findings underscore the superior performance of matched compared to stratified win statistics in terms of coverage probability (matched WR: 97% vs. stratified WR: 33.3% in a high-imbalance setting; matched WR: 98% vs. stratified WR: 34.4% in a medium-imbalance setting; and matched WR: 99.2% vs. stratified WR: 37.4% in a low-imbalance setting). Conclusions: In conclusion, our study sheds light on the interpretation of the results of win statistics in terms of statistical significance, providing insights into the application of pairwise comparison in observational settings, promoting its use to improve outcomes for cancer patients.

A hierarchical kidney outcome using win statistics in patients with heart failure from the DAPA-HF and DELIVER trials

Win statistics offer a new approach to the analysis of outcomes in clinical trials, allowing the combination of time-to-event and longitudinal measurements and taking into account the clinical importance of the components of composite outcomes, as well as their relative timing. We examined this approach in a post hoc analysis of two trials that compared dapagliflozin to placebo in patients with heart failure and reduced ejection fraction (DAPA-HF) and mildly reduced or preserved ejection fraction (DELIVER). The effect of dapagliflozin on a hierarchical composite kidney outcome was assessed, including the following: (1) all-cause mortality; (2) end-stage kidney disease; (3) a decline in estimated glomerular filtration rate (eGFR) of ≥57%; (4) a decline in eGFR of ≥50%; (5) a decline in eGFR of ≥40%; and (6) participant-level eGFR slope. For this outcome, the win ratio was 1.10 (95% confidence interval (CI) = 1.06-1.15) in the combined dataset, 1.08 (95% CI = 1.01-1.16) in the DAPA-HF trial and 1.12 (95% CI = 1.05-1.18) in the DELIVER trial; that is, dapagliflozin was superior to placebo in both trials. The benefits of treatment were consistent in participants with and without baseline kidney disease, and with and without type 2 diabetes. In heart failure trials, win statistics may provide the statistical power to evaluate the effect of treatments on kidney as well as cardiovascular outcomes.

A win ratio-based framework to combine multiple clinical endpoints in exploratory basket trials

In contemporary exploratory phase of oncology drug development, there has been an increasing interest in evaluating investigational drug or drug combination in multiple tumor indications in a single basket trial to expedite drug development. There has been extensive research on more efficiently borrowing information across tumor indications in early phase drug development including Bayesian hierarchical modeling and the pruning-and-pooling methods. Despite the fact that the Go/No-Go decision for subsequent Phase 2 or Phase 3 trial initiation is almost always a multi-facet consideration, the statistical literature of basket trial design and analysis has largely been limited to a single binary endpoint. In this paper we explore the application of considering clinical priorities of multiple endpoints based on matched win ratio to the basket trial design and analysis. The control arm data will be simulated for each tumor indication based on the corresponding null assumptions that could be heterogeneous across tumor indications. The matched win ratio matching on the tumor indication can be performed for individual tumor indication, pooled data, or the pooled data after pruning depending on whether an individual evaluation or a simple pooling or a pruning-and-pooling method is used. We conduct the simulation studies to evaluate the performance of proposed win ratio-based framework and the results suggest the proposed framework could provide desirable operating characteristics.

Dissecting the restricted mean time in favor of treatment

The restricted mean time in favor (RMT-IF) summarizes the treatment effect on a hierarchical composite endpoint with mortality at the top. Its crude decomposition into "stage-wise effects," i.e., the net average time gained by the treatment prior to each component event, does not reveal the patient state in which the extra time is spent. To obtain this information, we break each stage-wise effect into subcomponents according to the specific state to which the reference condition is improved. After re-expressing the subcomponents as functionals of the marginal survival functions of outcome events, we estimate them conveniently by plugging in the Kaplan -- Meier estimators. Their robust variance matrices allow us to construct joint tests on the decomposed units, which are particularly powerful against component-wise differential treatment effects. By reanalyzing a cancer trial and a cardiovascular trial, we acquire new insights into the quality and composition of the extra survival times, as well as the extra time with fewer hospitalizations, gained by the treatment in question. The proposed methods are implemented in the rmt package freely available on the Comprehensive R Archive Network (CRAN).

Assessment of the DNA Mismatch Repair System Is Crucial in Colorectal Cancers Necessitating Adjuvant Treatment: A Propensity Score-Matched and Win Ratio Analysis

A deficient DNA mismatch repair (MMR) system is identified in a non-negligible part of sporadic colorectal cancers (CRCs), and its prognostic value remains controversial. High tumor mutational burden, along with a poor response to conventional chemotherapy and excellent results from immunotherapy, are the main features of this subset. The aim of this study was to evaluate the predictive value of DNA MMR system status for its best treatment. Four hundred and three CRC patients, operated on from 2014 to 2021 and not treated with immunotherapy, entered this study. Immunohistochemistry and polymerase chain reaction, as appropriate, were used to unequivocally group specimens into microsatellite stable (MSS) and instable (MSI) tumors. The win-ratio approach was utilized to compare composite outcomes. MSI tumors accounted for 12.9% of all series. The right tumor location represented the most important factor related to MSI. The status of the DNA MMR system did not appear to correlate with outcome in early-stage CRCs not requiring adjuvant treatment; in advanced stages undergoing conventional chemotherapy, MSI tumors showed significantly poorer overall and disease-free survival rates and the highest win ratio instead. The determination of DNA MMR status is crucial to recommending correct management. There is clear evidence that instable CRCs needing adjuvant therapy should undergo appropriate treatments.

Effect of Noninvasive Airway Management of Comatose Patients With Acute Poisoning: A Randomized Clinical Trial

Importance

Tracheal intubation is recommended for coma patients and those with severe brain injury, but its use in patients with decreased levels of consciousness from acute poisoning is uncertain.

Objective

To determine the effect of intubation withholding vs routine practice on clinical outcomes of comatose patients with acute poisoning and a Glasgow Coma Scale score less than 9.

Design, Setting, and Participants

This was a multicenter, randomized trial conducted in 20 emergency departments and 1 intensive care unit (ICU) that included comatose patients with suspected acute poisoning and a Glasgow Coma Scale score less than 9 in France between May 16, 2021, and April 12, 2023, and followed up until May 12, 2023.

Intervention

Patients were randomized to undergo conservative airway strategy of intubation withholding vs routine practice.

Main Outcomes and Measures

The primary outcome was a hierarchical composite end point of in-hospital death, length of ICU stay, and length of hospital stay. Key secondary outcomes included adverse events resulting from intubation as well as pneumonia within 48 hours.

Results

Among the 225 included patients (mean age, 33 years; 38% female), 116 were in the intervention group and 109 in the control group, with respective proportions of intubations of 16% and 58%. No patients died during the in-hospital stay. There was a significant clinical benefit for the primary end point in the intervention group, with a win ratio of 1.85 (95% CI, 1.33 to 2.58). In the intervention group, there was a lower proportion with any adverse event (6% vs 14.7%; absolute risk difference, 8.6% [95% CI, -16.6% to -0.7%]) compared with the control group, and pneumonia occurred in 8 (6.9%) and 16 (14.7%) patients, respectively (absolute risk difference, -7.8% [95% CI, -15.9% to 0.3%]).

Conclusions and Relevance

Among comatose patients with suspected acute poisoning, a conservative strategy of withholding intubation was associated with a greater clinical benefit for the composite end point of in-hospital death, length of ICU stay, and length of hospital stay.

Trial Registration

ClinicalTrials.gov Identifier: NCT04653597.

Validity and Utility of a Hierarchical Composite End Point for Clinical Trials of Kidney Disease Progression: A Review

Clinical trials in nephrology often use composite end points comprising clinical events, such as onset of ESKD and initiation of kidney function replacement therapy, along with a sustained large ( e.g. , ≥50%) decrease in GFR. Such events typically occur late in the disease course, resulting in large trials in which most participants do not contribute clinical events. In addition, components of the end point are considered of equal importance; however, their clinical significance varies. For example, kidney function replacement therapy initiation is likely to be clinically more meaningful than GFR decline of ≥50%. By contrast, hierarchical composite end points (HCEs) combine multiple outcomes and prioritize each patient's most clinically relevant outcome for inclusion in analysis. In this review, we consider the use of HCEs in clinical trials of CKD progression, emphasizing the potential to combine dichotomous clinical events such as those typically used in CKD progression trials, with the continuous variable of GFR over time, while ranking all components according to clinical significance. We consider maraca plots to visualize overall treatment effects and the contributions of individual components, discuss the application of win odds in kidney HCE trials, and review general design considerations for clinical trials for CKD progression with kidney HCE as an efficacy end point.

Design of paediatric trials with benefit-risk endpoints using a composite score of adverse events of interest (AEI) and win-statistics

A fundamental problem in the regulatory evaluation of a therapy is assessing whether the benefit outweighs the associated risks. This work proposes designing a trial that assesses a composite endpoint consisting of benefit and risk, hence, making the core of the design of the study, to assess benefit and risk. The proposed benefit risk measure consists of efficacy measure(s) and a risk measure that is based on a composite score obtained from pre-defined adverse events of interest (AEI). This composite score incorporates full aspects of adverse events of interest (i.e. the incidence, severity, and duration of the events). We call this newly proposed score the AEI composite score. After specifying the priorities between the components of the composite endpoint, a win-statistic (i.e. win ratio, win odds, or net benefit) is used to assess the difference between treatments in this composite endpoint. The power and sample size requirements of such a trial design are explored via simulation. Finally, using Dupixent published adult study results, we show how we can design a paediatric trial where the primary outcome is a composite of prioritized outcomes consisting of efficacy endpoints and the AEI composite score endpoint. The resulting trial design can potentially substantially reduce sample size compared to a trial designed to assess the co-primary efficacy endpoints, therefore it may address the challenge of slow enrollment and patient availability for paediatric studies.

Use of Win Statistics to Analyze Outcomes in the DAPA-HF and DELIVER Trials

BACKGROUND: The primary end point in most heart failure (HF) trials is a composite of time to a first worsening HF event or cardiovascular death. Prevention of recurrent events and improvements in symptoms/quality of life are also important for patients but are usually analyzed separately. Win statistics can integrate all these outcomes into a single composite end point, which is analyzed in hierarchical order, reflecting the clinical importance of each component. METHODS: The Dapagliflozin and Prevention of Adverse Outcomes in Heart Failure (DAPA-HF, n=4744) and Dapagliflozin Evaluation to Improve the Lives of Patients with Preserved Ejection Fraction Heart Failure (DELIVER, n=6263) trials enrolled patients with New York Heart Association class II, III, or IV HF, elevated natriuretic peptides, and either an ejection fraction of 40% or less (DAPA-HF) or greater than 40% and left atrial enlargement/left ventricular hypertrophy (DELIVER). We examined the effects of dapagliflozin compared with placebo on a hierarchical composite outcome, including cardiovascular death, total (first and recurrent) HF hospitalizations, total urgent HF visits, and improvement/deterioration in Kansas City Cardiomyopathy Questionnaire total symptom score (KCCQ-TSS; range from 0 to 100, with a higher score indicating fewer symptoms and physical limitations) at 8 months. RESULTS: For this composite outcome, the win ratio was 1.30 (95% confidence interval [CI], 1.23 to 1.36) in the pooled cohort, 1.33 (95% CI, 1.23 to 1.43) in the DAPA-HF trial, and 1.27 (95% CI, 1.18 to 1.36) in the DELIVER trial. Win odds and net benefit in overall patients were 1.19 (95% CI, 1.14 to 1.24) and 8.7% (95% CI, 6.6 to 10.9%), respectively. In the overall pooled cohort, the majority of wins and losses were accounted for by KCCQ-TSS; 52.4% were settled by the KCCQ-TSS tier in the pooled cohort. CONCLUSIONS: In both the DAPA-HF and DELIVER trials, dapagliflozin led to a significant improvement in composite outcomes that incorporated patient-reported outcomes along with total HF events, as well as cardiovascular deaths. These analyses provided a comprehensive presentation of win statistics and illustrated the utility and flexibility of win statistics in describing the effects of dapagliflozin in two recent clinical trials in patients with HF. (Funded by British Heart Foundation Centre of Research Excellence and others; clinical trial registration numbers, NCT03036124 and NCT03619213.)

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.

The win odds: statistical inference and regression

Generalized pairwise comparisons and win statistics (i.e., win ratio, win odds and net benefit) are advantageous in analyzing and interpreting a composite of multiple outcomes in clinical trials. An important limitation of these statistics is their inability to adjust for covariates other than by stratified analysis. Because the win ratio does not account for ties, the win odds, a modification that includes ties, has attracted attention. We review and combine information on the win odds to articulate the statistical inferences for the win odds. We also show alternative variance estimators based on the exact permutation and bootstrap as well as statistical inference via the probabilistic index. Finally, we extend multiple-covariate regression probabilistic index models to the win odds with a univariate outcome. As an illustration we apply the regression models to the data in the CHARM trial.

Generalised pairwise comparisons for trend: An extension to the win ratio and win odds for dose-response and prognostic variable analysis with arbitrary statements of outcome preference

The win ratio is a novel approach for handling complex patient outcomes that have seen considerable interest in the medical statistics literature, and operates by considering all-to-all pairwise statements of preference on outcomes. Recent extensions to the method have focused on the two-group case, with few developments made for considering the impact of a well-ordered explanatory variable, which would allow for dose-response analysis or the analysis of links between complex patient outcomes and prognostic variables. Where such methods have been developed, they are semiparametric methods that can only be applied to survival outcomes. In this article, we introduce the generalised pairwise comparison for trend, a modified form of Agresti's generalised odds ratio. This approach is capable of considering arbitrary statements of preference, thus enabling its use across all types of outcome data. We provide a simulation study validating the approach and illustrate it with three clinical applications in stroke research.

The stratified win statistics (win ratio, win odds, and net benefit)

The win odds and the net benefit are related directly to each other and indirectly, through ties, to the win ratio. These three win statistics test the same null hypothesis of equal win probabilities between two groups. They provide similar p-values and powers, because the Z-values of their statistical tests are approximately equal. Thus, they can complement one another to show the strength of a treatment effect. In this article, we show that the estimated variances of the win statistics are also directly related regardless of ties or indirectly related through ties. Since its introduction in 2018, the stratified win ratio has been applied in designs and analyses of clinical trials, including Phase III and Phase IV studies. This article generalizes the stratified method to the win odds and the net benefit. As a result, the relations of the three win statistics and the approximate equivalence of their statistical tests also hold for the stratified win statistics.

Statistical power considerations in the use of win ratio in cardiovascular outcome trials

BACKGROUND

In cardiovascular outcome trials, the win ratio (WR) method models the composite endpoint under a hierarchical structure to account for clinical priorities. It also can be applied to both survival and nonsurvival outcomes.

METHODS

In this article, we assess the performance of the WR method via extensive simulation studies and real data analyses and discuss power considerations of the method with respect to hierarchical order, variable type, magnitude of treatment effect, and event rates when applied to clinical studies.

RESULTS AND CONCLUSION

In the hierarchy of the WR method, the first-ordered component (e.g., death) plays a dominant role in statistical power, especially when that component has a large treatment effect and a high event rate. This is in contrast with the score test of the Cox proportional hazards model in which the power is more likely affected by the nonfatal events that are usually observed earlier. Furthermore, when adding an additional component to the composite endpoint, the performance of the WR method varies depending on the treatment effect, event rate, and hierarchical position of the component. If the additional component has a relatively smaller or no treatment effect, the statistical power will decrease; if the additional component has a relatively larger treatment effect and higher event rate, the statistical power will increase. When adding a nonsurvival continuous outcome (e.g., 6-min walk distance) with even a tiny treatment effect, the statistical power could dramatically increase.

Evidence synthesis analysis with prioritized benefit outcomes in oncology clinical trials

Overall survival, progression-free survival, objective response/complete response, and duration of (complete) response are frequently used as the primary and secondary efficacy endpoints for designs and analyses of oncology clinical trials. However, these endpoints are typically analyzed separately. In this article, we introduce an evidence synthesis approach to prioritize the benefit outcomes by applying the generalized pairwise comparisons (GPC) method, and use win statistics (win ratio, win odds and net benefit) to quantify treatment benefit. Under the framework of GPC, the main advantage of this evidence synthesis approach is the ability to combine relevant outcomes of various types into a single summary statistic without relying on any parametric assumptions. It is particularly relevant since health authorities and the pharmaceutical industry are increasingly incorporating structured quantitative methodologies in their benefit-risk assessment. We apply this evidence synthesis approach to an oncology phase 3 study in first-line renal cell carcinoma to assess the overall effect of an investigational treatment by ranking the most clinically relevant endpoints in cancer drug development. This application and a simulation study demonstrate that the proposed approach can synthesize the evidence of treatment effect from multiple prioritized benefit outcomes, and has substantial advantage over conventional methods that analyze each individual endpoint separately. We also introduce a newly developed R package WINS for statistical inference based on win statistics.