Meaningful Endpoints for Idiopathic Pulmonary Fibrosis (IPF) Clinical Trials: Emphasis on 'Feels, Functions, Survives'. Report of a Collaborative Discussion in a Symposium with Direct Engagement from Representatives of Patients, Investigators, the National Institutes of Health, a Patient Advocacy Organization, and a Regulatory Agency.

Background: Idiopathic pulmonary fibrosis (IPF) carries significant mortality and unpredictable progression, with limited therapeutic options. Designing trials with patient-meaningful endpoints, enhancing the reliability and interpretability of results, and streamlining the regulatory approval process are of critical importance to advancing clinical care in IPF. Methods: A landmark in-person symposium in June 2023 assembled 43 participants from the US and internationally, including patients with IPF, investigators, and regulatory representatives, to discuss the immediate future of IPF clinical trial endpoints. Patient advocates were central to discussions, which evaluated endpoints according to regulatory standards and the FDA's 'feels, functions, survives' criteria. Results: Three themes emerged: 1) consensus on endpoints mirroring the lived experiences of patients with IPF; 2) consideration of replacing forced vital capacity (FVC) as the primary endpoint, potentially by composite endpoints that include 'feels, functions, survives' measures or FVC as components; 3) support for simplified, user-friendly patient-reported outcomes (PROs) as either components of primary composite endpoints or key secondary endpoints, supplemented by functional tests as secondary endpoints and novel biomarkers as supportive measures (FDA Guidance for Industry (Multiple Endpoints in Clinical Trials) available at: https://www.fda.gov/media/162416/download). Conclusions: This report, detailing the proceedings of this pivotal symposium, suggests a potential turning point in designing future IPF clinical trials more attuned to outcomes meaningful to patients, and documents the collective agreement across multidisciplinary stakeholders on the importance of anchoring IPF trial endpoints on real patient experiences-namely, how they feel, function, and survive. There is considerable optimism that clinical care in IPF will progress through trials focused on patient-centric insights, ultimately guiding transformative treatment strategies to enhance patients' quality of life and survival.

Bayesian response adaptive randomization design with a composite endpoint of mortality and morbidity.

Allocating patients to treatment arms during a trial based on the observed responses accumulated up to the decision point, and sequential adaptation of this allocation, could minimize the expected number of failures or maximize total benefits to patients. In this study, we developed a Bayesian response-adaptive randomization (RAR) design targeting the endpoint of organ support-free days (OSFD) for patients admitted to the intensive care units. The OSFD is a mixture of mortality and morbidity assessed by the number of days of free of organ support within a predetermined post-randomization time-window. In the past, researchers treated OSFD as an ordinal outcome variable where the lowest category is death. We propose a novel RAR design for a composite endpoint of mortality and morbidity, for example, OSFD, by using a Bayesian mixture model with a Markov chain Monte Carlo sampling to estimate the posterior probability distribution of OSFD and determine treatment allocation ratios at each interim. Simulations were conducted to compare the performance of our proposed design under various randomization rules and different alpha spending functions. The results show that our RAR design using Bayesian inference allocated more patients to the better performing arm(s) compared to other existing adaptive rules while assuring adequate power and type I error rate control across a range of plausible clinical scenarios.

Association between late sleeping and major adverse cardiovascular events in patients with percutaneous coronary intervention.

BACKGROUND: Sleeping late has been a common phenomenon and brought harmful effects to our health. The purpose of this study was to investigate the association between sleep timing and major adverse cardiovascular events (MACEs) in patients with percutaneous coronary intervention (PCI). METHODS: Sleep onset time which was acquired by the way of sleep factors questionnaire in 426 inpatients was divided into before 22:00, 22:00 to 22:59, 23:00 to 23:59 and 24:00 and after. The median follow-up time was 35 months. The endpoints included angina pectoris (AP), new myocardial infarction (MI) or unplanned repeat revascularization, hospitalization for heart failure, cardiac death, nonfatal stroke, all-cause death and the composite endpoint of all events mentioned above. Cox proportional hazards regression was applied to analyze the relationship between sleep timing and endpoint events. RESULTS: A total of 64 composite endpoint events (CEEs) were reported, including 36 AP, 15 new MI or unplanned repeat revascularization, 6 hospitalization for heart failure, 2 nonfatal stroke and 5 all-cause death. Compared with sleeping time at 22:00-22:59, there was a higher incidence of AP in the bedtime ≥ 24:00 group (adjusted HR: 5.089; 95% CI: 1.278-20.260; P = 0.021). In addition, bedtime ≥ 24:00 was also associated with an increased risk of CEEs in univariate Cox regression (unadjusted HR: 2.893; 95% CI: 1.452-5.767; P = 0.003). After multivariable adjustments, bedtime ≥ 24:00 increased the risk of CEEs (adjusted HR: 3.156; 95% CI: 1.164-8.557; P = 0.024). CONCLUSION: Late sleeping increased the risk of MACEs and indicated a poor prognosis. It is imperative to instruct patients with PCI to form early bedtime habits.

Adaptive clinical trial designs with blinded selection of binary composite endpoints and sample size reassessment.

For randomized clinical trials where a single, primary, binary endpoint would require unfeasibly large sample sizes, composite endpoints (CEs) are widely chosen as the primary endpoint. Despite being commonly used, CEs entail challenges in designing and interpreting results. Given that the components may be of different relevance and have different effect sizes, the choice of components must be made carefully. Especially, sample size calculations for composite binary endpoints depend not only on the anticipated effect sizes and event probabilities of the composite components but also on the correlation between them. However, information on the correlation between endpoints is usually not reported in the literature which can be an obstacle for designing future sound trials. We consider two-arm randomized controlled trials with a primary composite binary endpoint and an endpoint that consists only of the clinically more important component of the CE. We propose a trial design that allows an adaptive modification of the primary endpoint based on blinded information obtained at an interim analysis. Especially, we consider a decision rule to select between a CE and its most relevant component as primary endpoint. The decision rule chooses the endpoint with the lower estimated required sample size. Additionally, the sample size is reassessed using the estimated event probabilities and correlation, and the expected effect sizes of the composite components. We investigate the statistical power and significance level under the proposed design through simulations. We show that the adaptive design is equally or more powerful than designs without adaptive modification on the primary endpoint. Besides, the targeted power is achieved even if the correlation is misspecified at the planning stage while maintaining the type 1 error. All the computations are implemented in R and illustrated by means of a peritoneal dialysis trial.

Achievement of glycaemic targets with weight loss and without hypoglycaemia in type 2 diabetes with the once-weekly glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonist tirzepatide: A post hoc analysis of the SURPASS-1 to -5 studies.

AIM: To assess composite endpoints combining glycaemic control (HbA1c < 7.0%, ≤ 6.5% or < 5.7%) with weight loss (≥ 5%, ≥ 10% or ≥ 15%) and without hypoglycaemia with tirzepatide in type 2 diabetes (T2D). MATERIALS AND METHODS: Data from the phase 3 SURPASS programme were evaluated post hoc by trial. Participants with T2D were randomized to tirzepatide (5, 10 and 15 mg), placebo (SURPASS-1,5), semaglutide 1 mg (SURPASS-2) or titrated basal insulin (SURPASS-3,4). The proportions of participants achieving the composite endpoints were compared between tirzepatide and the respective comparator groups at week 40/52. RESULTS: The proportions of participants achieving an HbA1c value of less than 7.0% with 5% or more weight loss and without hypoglycaemia ranged from 43% to 82% with tirzepatide across the SURPASS-1 to -5 trials versus 4%-5% with placebo, 51% with semaglutide 1 mg and 5% with basal insulin (P < .001 vs. all comparators). The proportions of participants achieving an HbA1c value of less than 7.0% with 10% or more, or 15% or more weight loss and without hypoglycaemia were significantly higher with all tirzepatide doses versus comparators across trials (P < .001 or P < .05). Similar results were observed for all other combinations of endpoints with an HbA1c value of 6.5% or less, or less than 5.7%, with more tirzepatide-treated participants achieving these endpoints versus those in the comparator groups, including semaglutide. CONCLUSIONS: Across the SURPASS-1 to -5 clinical trials, more tirzepatide-treated participants with T2D achieved clinically meaningful composite endpoints, which included reaching glycaemic targets with various degrees of weight loss and without hypoglycaemia, than those in the comparator groups.

Performance of Cox regression models for composite time-to-event endpoints with component-wise censoring in randomized trials.

BACKGROUND: Composite time-to-event endpoints are beneficial for assessing related outcomes jointly in clinical trials, but components of the endpoint may have different censoring mechanisms. For example, in the PRagmatic EValuation of evENTs And Benefits of Lipid-lowering in oldEr adults (PREVENTABLE) trial, the composite outcome contains one endpoint that is right censored (all-cause mortality) and two endpoints that are interval censored (dementia and persistent disability). Although Cox regression is an established method for time-to-event outcomes, it is unclear how models perform under differing component-wise censoring schemes for large clinical trial data. The goal of this article is to conduct a simulation study to investigate the performance of Cox models under different scenarios for composite endpoints with component-wise censoring. METHODS: We simulated data by varying the strength and direction of the association between treatment and outcome for the two component types, the proportion of events arising from the components of the outcome (right censored and interval censored), and the method for including the interval-censored component in the Cox model (upper value and midpoint of the interval). Under these scenarios, we compared the treatment effect estimate bias, confidence interval coverage, and power. RESULTS: Based on the simulation study, Cox models generally have adequate power to achieve statistical significance for comparing treatments for composite outcomes with component-wise censoring. In our simulation study, we did not observe substantive bias for scenarios under the null hypothesis or when the treatment has a similar relative effect on each component outcome. Performance was similar regardless of if the upper value or midpoint of the interval-censored part of the composite outcome was used. CONCLUSION: Cox regression is a suitable method for analysis of clinical trial data with composite time-to-event endpoints subject to different component-wise censoring mechanisms.

Triglyceride-glucose index on risk of adverse events after drug-coated balloon angioplasty.

BACKGROUND: The pathogenetic mechanism of atherosclerotic cardiovascular diseases is associated with insulin resistance (IR), which serves as a metabolic risk factor. As a novel indication for IR, triglyceride-glucose (TyG) index may predict cardiovascular disease outcomes. METHODS: In current study, a cohort of 157 individuals with newly developed de novo lesions who received DCB angioplasty between January 2017 and May 2021 were included. The midterm follow-up clinical results consisted of the presence of vessel-oriented composite endpoint (VOCE). The baseline TyG index was divided into three groups by tertiles. This study compared various clinical characteristics and parameters among different groups during DCB angioplasty. A multivariate Cox regression model was built to investigate the potential predictors. RESULTS: Higher TyG index indicated an increased risk of VOCE according to the adjusted model (HR = 4.0, 95%Cl: 1.0-15.4, P = 0.047). A non-linear correlation was uncovered between the index and VOCE from the smooth curve. Based on Kaplan-Meier curve, individuals in the highest TyG index group were more likely to develop VOCE (P < 0.05 for log-rank). CONCLUSIONS: The incidence of VOCE was shown to be independently and positively correlated with an elevated TyG index in individuals with de novo coronary lesions who received DCB angioplasty.

Glomerulosclerosis is a prognostic risk factor in patients with membranous nephropathy and non-nephrotic proteinuria.

OBJECTIVE: To explore the predictive value of the proportion of glomerulosclerosis (GS) incidences on the progression of membranous nephropathy with non-nephrotic proteinuria (NNP). METHODS: This study was a single-center, retrospective, cohort study. Patients with biopsy-proven idiopathic membranous nephropathy were divided into three groups based on the proportion of glomerular sclerosis, and their demographic, clinical, and pathological data were compared. The proportions of primary and secondary endpoints were recorded, and the relationship between GS and primary outcomes (progression to nephrotic syndrome, complete remission, and persistent NNP) and the renal composite endpoint was analyzed. RESULTS: A total of 112 patients were divided into three groups according to the proportions of glomerulosclerosis. The median follow-up time was 26.5 (13-51) months. There were significant differences in blood pressure (p < 0.01), renal interstitial lesions (p < 0.0001), and primary endpoints (p = 0.005). The survival analysis showed that prognosis was significantly worse in patients with a high proportion of GS than in those patients with a middle and low proportion of GS (p < 0.001). The Cox multivariate analysis showed that after adjusting for age, sex, BP, 24-h urinary protein, serum creatinine, treatment scheme, and pathological factors, the risk of renal composite outcome in the low proportion group was 0.076 times higher than that in the high proportion group (p = 0.009, HR = 0.076, 95% CI: 0.011-0.532). CONCLUSION: A high level of glomerulosclerosis was an independent risk factor for the prognosis of patients with membranous nephropathy with non-nephrotic proteinuria.

Improving power in PSA response analyses of metastatic castration-resistant prostate cancer trials.

BACKGROUND: To determine how much an augmented analysis approach could improve the efficiency of prostate-specific antigen (PSA) response analyses in clinical practice. PSA response rates are commonly used outcome measures in metastatic castration-resistant prostate cancer (mCRPC) trial reports. PSA response is evaluated by comparing continuous PSA data (e.g., change from baseline) to a threshold (e.g., 50% reduction). Consequently, information in the continuous data is discarded. Recent papers have proposed an augmented approach that retains the conventional response rate, but employs the continuous data to improve precision of estimation. METHODS: A literature review identified published prostate cancer trials that included a waterfall plot of continuous PSA data. This continuous data was extracted to enable the conventional and augmented approaches to be compared. RESULTS: Sixty-four articles, reporting results for 78 mCRPC treatment arms, were re-analysed. The median efficiency gain from using the augmented analysis, in terms of the implied increase to the sample size of the original study, was 103.2% (IQR [89.8,190.9%]). CONCLUSIONS: Augmented PSA response analysis requires no additional data to be collected and can be performed easily using available software. It improves precision of estimation to a degree that is equivalent to a substantial sample size increase. The implication of this work is that prostate cancer trials using PSA response as a primary endpoint could be delivered with fewer participants and, therefore, more rapidly with reduced cost.

Nonparametric estimation in an illness-death model with component-wise censoring.

In disease settings where study participants are at risk for death and a serious nonfatal event, composite endpoints defined as the time until the earliest of death or the nonfatal event are often used as the primary endpoint in clinical trials. In practice, if the nonfatal event can only be detected at clinic visits and the death time is known exactly, the resulting composite endpoint exhibits "component-wise censoring." The standard method used to estimate event-free survival in this setting fails to account for component-wise censoring. We apply a kernel smoothing method previously proposed for a marker process in a novel way to produce a nonparametric estimator for event-free survival that accounts for component-wise censoring. The key insight that allows us to apply this kernel method is thinking of nonfatal event status as an intermittently observed binary time-dependent variable rather than thinking of time to the nonfatal event as interval-censored. We also propose estimators for the probability in state and restricted mean time in state for reversible or irreversible illness-death models, under component-wise censoring, and derive their large-sample properties. We perform a simulation study to compare our method to existing multistate survival methods and apply the methods on data from a large randomized trial studying a multifactor intervention for reducing morbidity and mortality among men at above average risk of coronary heart disease.