Analysis of composite time-to-event endpoints in cardiovascular outcome trials.

Composite time-to-event endpoints are commonly used in cardiovascular outcome trials. For example, the IMPROVE-IT trial comparing ezetimibe+simvastatin to placebo+simvastatin in 18,144 patients with acute coronary syndrome used a primary composite endpoint with five component outcomes: (1) cardiovascular death, (2) non-fatal stroke, (3) non-fatal myocardial infarction, (4) coronary revascularization ≥30 days after randomization, and (5) unstable angina requiring hospitalization. In such settings, the traditional analysis compares treatments using the observed time to the occurrence of the first (i.e. earliest) component outcome for each patient. This approach ignores information for subsequent outcome(s), possibly leading to reduced power to demonstrate the benefit of the test versus the control treatment. We use real data examples and simulations to contrast the traditional approach with several alternative approaches that use data for all the intra-patient component outcomes, not just the first.

Statistical approaches for component-wise censored composite endpoints.

Composite endpoints defined as the time to the earliest of two or more events are often used as primary endpoints in clinical trials. Component-wise censoring arises when different components of the composite endpoint are censored differently. We focus on a composite of death and a non-fatal event where death time is right censored and the non-fatal event time is interval censored because the event can only be detected during study visits. Such data are most often analysed using methods for right censored data, treating the time the non-fatal event was first detected as the time it occurred. This can lead to bias, particularly when the time between assessments is long. We describe several approaches for estimating the event-free survival curve and the effect of treatment on event-free survival via the hazard ratio that are specifically designed to handle component-wise censoring. We apply the methods to a randomized study of breastfeeding versus formula feeding for infants of mothers infected with human immunodeficiency virus.

Survival (Time-To-Event) Curve Names and Endpoints.

In oncology, "survival curves" frequently appear in journal articles and meeting presentations. The most common labels on survival curves are: Overall Survival, Relapse Free Survival, Progression Free Survival, Distant Metastasis Free Survival and Local and/or Regional Control. Unfortunately, consistency in the definition of an event differs between authors for the same prescribed survival analyses. Furthermore, the quality of a survival curves can be greatly impacted by the methodology used for endpoint selection. This paper will briefly explain widely used names and event endpoints for survival analyses in a way that will help radiation oncologists consistently present and interpret experimental findings that influence clinical practice decisions.

Prognostic significance of compliance with fractional flow reserve guidance on diverse vessel-related clinical outcomes.

Background: In patients underwent fractional flow reserve (FFR) assessment, a noteworthy proportion of adverse events occur in vessels in which FFR has not been measured. However, the effect of these non-target vessel-related events on the evaluation of FFR-related benefits remains unknown. Methods and results: In this retrospective study, vessels subjected to FFR measurement were grouped as FFR-based approach and non-compliance with FFR based on whether they received FFR-based treatment. Using inverse probability of treatment weighting (IPTW) to account for potential confounding, we investigated the association between compliance with FFR and 5-year target vessel failure (TVF) non-target vessel failure (NTVF) and vessel-oriented composite endpoints (VOCEs). Of the 1,119 vessels, 201 did not receive FFR-based treatment. After IPTW adjustment, a significantly lower hazard of TVF was observed in the FFR-based approach group (HR: 0.56; 95% CI: 0.34-0.92). While, the intergroup difference in hazard of NTVF (HR: 1.02; 95% CI: 0.45-2.31) and VOCEs (HR: 0.69; 95% CI: 0.45-1.05) were nonsignificant. Conclusions: In patients with CAD subjected to FFR, the FFR-based treatment yields a sustained clinical benefit in terms of the risks of target vessel-related events. The dilution of non-target vessel-related events renders the difference favoring the FFR-based approach nonsignificant.

What can patients tell us in Sjögren's syndrome?

In Sjögren's Syndrome (SS), clinical heterogeneity and discordance between disease activity measures and patient experience are key obstacles to effective therapeutic development. Patient reported outcome measures (PROMs) are useful tools for understanding the unmet needs from the patients' perspective and therefore they are key for the development of patient centric healthcare systems. Initial concern about the subjectivity of PROMs has given way to methodological rigour and clear guidance for the development of PROMs. To date, several studies of patient stratification using PROMs have identified similar symptom-based subgroups. There is evidence to suggest that these subgroups may represent different disease endotypes with differing responses to therapeutic interventions. Stratified medicine approaches, alongside sensitive outcome measures, have the potential to improve our understanding of SS pathobiology and therapeutic development. The inclusion of PROMs is important for the success of such approaches. In this review we discuss the opportunities of using PROMs in understanding the pathogenesis of and therapeutic development for SS.

Statistical inference for time-to-event data in non-randomized cohorts with selective attrition.

In multi-season clinical trials with a randomize-once strategy, patients enrolled from previous seasons who stay alive and remain in the study will be treated according to the initial randomization in subsequent seasons. To address the potentially selective attrition from earlier seasons for the non-randomized cohorts, we develop an inverse probability of treatment weighting method using season-specific propensity scores to produce unbiased estimates of survival functions or hazard ratios. Bootstrap variance estimators are used to account for the randomness in the estimated weights and the potential correlations in repeated events within each patient from season to season. Simulation studies show that the weighting procedure and bootstrap variance estimator provide unbiased estimates and valid inferences in Kaplan-Meier estimates and Cox proportional hazard models. Finally, data from the INVESTED trial are analyzed to illustrate the proposed method.

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).

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.

Power and Sample Size Calculations for the Restricted Mean Time Analysis of Prioritized Composite Endpoints.

As a new way of reporting treatment effect, the restricted mean time in favor (RMT-IF) of treatment measures the net average time the treated have had a less serious outcome than the untreated over a specified time window. With multiple outcomes of differing severity, this offers a more interpretable and data-efficient alternative to the prototypical restricted mean (event-free) survival time. To facilitate its adoption in actual trials, we develop simple approaches to power and sample size calculations and implement them in user-friendly R programs. In doing so we model the bivariate outcomes of death and a nonfatal event using a Gumbel-Hougaard copula with component-wise proportional hazards structures, under which the RMT-IF estimand is derived in closed form. In a standard set-up for censoring, the variance of the nonparametric effect-size estimator is simplified and computed via a hybrid of numerical and Monte Carlo integrations, allowing us to compute the power and sample size as functions of component-wise hazard ratios. Simulation studies show that these formulas provide accurate approximations in realistic settings. To illustrate our methods, we consider designing a new trial to evaluate treatment effect on the composite outcomes of death and cancer relapse in lymph node-positive breast cancer patients, with baseline parameters calculated from a previous study.

Composite endpoints, including patient reported outcomes, in rare diseases.

BACKGROUND: When assessing the efficacy of a treatment in any clinical trial, it is recommended by the International Conference on Harmonisation to select a single meaningful endpoint. However, a single endpoint is often not sufficient to reflect the full clinical benefit of a treatment in multifaceted diseases, which is often the case in rare diseases. Therefore, the use of a combination of several clinically meaningful outcomes is preferred. Many methodologies that allow for combining outcomes in a so-called composite endpoint are however limited in a number of ways, not in the least in the number and type of outcomes that can be combined and in the poor small-sample properties. Moreover, patient reported outcomes, such as quality of life, often cannot be integrated in a composite analysis, in spite of their intrinsic value. RESULTS: Recently, a class of non-parametric generalized pairwise comparisons tests have been proposed, which members do allow for any number and type of outcomes, including patient reported outcomes. The class enjoys good small-sample properties. Moreover, this very flexible class of methods allows for prioritizing the outcomes by clinical severity, allows for matched designs and for adding a threshold of clinical relevance. Our aim is to introduce the generalized pairwise comparison ideas and concepts for rare disease clinical trial analysis, and demonstrate their benefit in a post-hoc analysis of a small-sample trial in epidermolysis bullosa. More precisely, we will include a patient relevant outcome (Quality of life), in a composite endpoint. This publication is part of the European Joint Programme on Rare Diseases (EJP RD) series on innovative methodologies for rare diseases clinical trials, which is based on the webinars presented within the educational activity of EJP RD. This publication covers the webinar topic on composite endpoints in rare diseases and includes participants' response to a questionnaire on this topic. CONCLUSIONS: Generalized pairwise comparisons is a promising statistical methodology for evaluating any type of composite endpoints in rare disease trials and may allow a better evaluation of therapy efficacy including patients reported outcomes in addition to outcomes related to the diseases signs and symptoms.

The analysis of multiple outcomes, multiple variables and variables selection in hematopoietic cell transplantation studies.

It is common to study time-to-event data in cancer research such as hematopoietic cell transplantation (HCT) for leukemia. The extensive work has been done for the univariate survival outcome, that is, one event type. However, in practice a subject is often exposed to multiple types of outcomes. In this article, we review various types of right-censored data with multiple outcome types including competing risks data, recurrent event data, and composite endpoints. We also provide hematopoietic cell transplantation data examples.

Endpoint selection and evaluation in hematology studies.

Observational studies and clinical trials in hematology aim to examine treatments for blood disorders. The outcomes being studied must address the goals of the study and provide meaningful information about treatment course, disease progression, describe patients' survival experience and quality of life. Endpoints are the specific measures of these outcomes, and much consideration should be given to their selection. In this review, we describe the outcomes and endpoints frequently used in studying hematologic diseases and provide general guidelines for their statistical analysis. The main focus is on clinical outcomes which are commonly used in establishing treatment safety and efficacy. We also briefly discuss the role surrogate and composite endpoints play in hematology studies. The importance of patient reported outcomes to comprehensive assessment of the treatment effectiveness is highlighted. Provided practical considerations for choosing primary and secondary endpoints may be helpful in designing hematology clinical trials.

Study design for restricted mean time analysis of recurrent events and death.

The restricted mean time in favor (RMT-IF) of treatment has just been added to the analytic toolbox for composite endpoints of recurrent events and death. To help practitioners design new trials based on this method, we develop tools to calculate the sample size and power. Specifically, we formulate the outcomes as a multistate Markov process with a sequence of transient states for recurrent events and an absorbing state for death. The transition intensities, in this case the instantaneous risks of another nonfatal event or death, are assumed to be time-homogeneous but nonetheless allowed to depend on the number of past events. Using the properties of Coxian distributions, we derive the RMT-IF effect size under the alternative hypothesis as a function of the treatment-to-control intensity ratios along with the baseline intensities, the latter of which can be easily estimated from historical data. We also reduce the variance of the nonparametric RMT-IF estimator to calculable terms under a standard set-up for censoring. Simulation studies show that the resulting formulas provide accurate approximation to the sample size and power in realistic settings. For illustration, a past cardiovascular trial with recurrent-hospitalization and mortality outcomes is analyzed to generate the parameters needed to design a future trial. The procedures are incorporated into the rmt package along with the original methodology on the Comprehensive R Archive Network (CRAN).

The Role of Sodium-Glucose Cotransporter-2 Inhibitors in Heart Failure Management: The Continuing Challenge of Clinical Outcome Endpoints in Heart Failure Trials.

The introduction of sodium-glucose cotransporter-2 (SGLT2) inhibitors in the management of heart failure with preserved ejection fraction (HFpEF) may be regarded as the first effective treatment in these patients. However, this proposition must be evaluated from the perspective of the complexity of clinical outcome endpoints in heart failure. The major goals of heart failure treatment have been categorized as: (1) reduction in (cardiovascular) mortality, (2) prevention of recurrent hospitalizations due to worsening heart failure, and (3) improvement in clinical status, functional capacity, and quality of life. The use of the composite primary endpoint of cardiovascular death and hospitalization for heart failure in SGLT2 inhibitor HFpEF trials flowed from the assumption that hospitalization for heart failure is a proxy for subsequent cardiovascular death. The use of this composite endpoint was not justified since the effect of the intervention on both components was clearly distinct. Moreover, the lack of convincing and clinically meaningful effects of SGLT2 inhibitors on metrics of heart failure-related health status indicates that the effect of this class of drugs in HFpEF patients is essentially restricted to an effect on hospitalization for heart failure. In conclusion, SGLT2 inhibitors do not represent a substantial breakthrough in the management of HFpEF.

Using a patient-centred composite endpoint in a secondary analysis of the Control of Hypertension in Pregnancy Study (CHIPS) Trial.

BACKGROUND: Clinical trials commonly use multiple endpoints to measure the impact of an intervention. While this improves the comprehensiveness of outcomes, it can make trial results difficult to interpret. We examined the impact of integrating patient weights into a composite endpoint on the interpretation of Control of Hypertension in Pregnancy Study (CHIPS) Trial results. METHODS: Outcome weights were extracted from a previous patient preferences study in pregnancy hypertension (N = 183 women) which identified (i) seven outcomes most important to women (taking medication, severe hypertension, pre-eclampsia, blood transfusion, Caesarean, delivery < 34 weeks, and baby born smaller-than-expected) and (ii) three preference subgroups: (1) 'equal prioritizers', 62%; (2) 'early delivery avoiders', 23%; and (3) 'medication minimizers', 14%. Outcome weights from the preference subgroups were integrated with CHIPS data for the seven outcomes identified in the preference study. A weighted composite score was derived for each participant by multiplying the preference weight for each outcome by the binary outcome if it occurred. Analyses considered equal weights and those from the preference subgroups. The mean composite scores were compared between trial arms (t-tests). RESULTS: Composite scores were similar between trial arms with the use of equal weights or those of subgroup (1) (95% confidence intervals [CIs]: - 0.03, 0.02; p > 0.50 for each). 'Tight' control was superior when using subgroup (2) weights (95% CIs: 0.002, 0.07; p = 0.03), and 'less-tight' control was superior when using subgroup (3) weights (95% CIs: - 0.11, - 0.04; p < 0.01). CONCLUSIONS: Evidence-based recommendations for 'tight' control are consistent with most women's preferences, but for a sixth of women, 'less-tight' control is more preference consistent. Depending on patient preferences, a single trial may support different interventions. Future trials should specify component weights to improve interpretation. TRIAL REGISTRATION: ClinicalTrials.gov NCT01192412.

Nonparametric inference of general while-alive estimands for recurrent events.

Measuring the treatment effect on recurrent events like hospitalization in the presence of death has long challenged statisticians and clinicians alike. Traditional inference on the cumulative frequency unjustly penalizes survivorship as longer survivors also tend to experience more adverse events. Expanding a recently suggested idea of the "while-alive" event rate, we consider a general class of such estimands that adjust for the length of survival without losing causal interpretation. Given a user-specified loss function that allows for arbitrary weighting, we define as estimand the average loss experienced per unit time alive within a target period and use the ratio of this loss rate to measure the effect size. Scaling the loss rate by the width of the corresponding time window gives us an alternative, and sometimes more photogenic, way of showing the data. To make inferences, we construct a nonparametric estimator for the loss rate through the cumulative loss and the restricted mean survival time and derive its influence function in closed form for variance estimation and testing. As simulations and analysis of real data from a heart failure trial both show, the while-alive approach corrects for the false attenuation of treatment effect due to patients living longer under treatment, with increased statistical power as a result. The proposed methods are implemented in the R-package WA, which is publicly available from the Comprehensive R Archive Network (CRAN).

The maraca plot: A novel visualization of hierarchical composite endpoints.

BACKGROUND: Hierarchical composite endpoints are complex endpoints combining outcomes of different types and different clinical importance into an ordinal outcome that prioritizes the clinically most important (e.g. most severe) event of a patient. Hierarchical composite endpoint can be analysed with the win odds, an adaptation of win ratio to include ties. One of the difficulties in interpreting hierarchical composite endpoint is the lack of proper tools for visualizing the treatment effect captured by hierarchical composite endpoint, given the complex nature of the endpoint which combines events of different types. METHODS: Hierarchical composite endpoints usually combine time-to-event outcomes and continuous outcomes into a composite; hence, it is important to capture not only the shift from more severe categories to less severe categories in the active group in comparison to the control group (as in any ordinal endpoint), but also changes occurring within each category. We introduce the novel maraca plot which combines violin plots (with nested box plots) to visualize the density of the distribution of the continuous outcome and Kaplan-Meier plots for time-to-event outcomes into a comprehensive visualization. CONCLUSION: The novel maraca plot is suggested for visualization of hierarchical composite endpoints consisting of several time-to-event outcomes and a continuous outcome. It has a very simple structure and therefore easily communicates both the overall treatment effect and the effect on individual components.

On restricted mean time in favor of treatment.

The restricted mean time in favor (RMT-IF) of treatment is a nonparametric effect size for complex life history data. It is defined as the net average time the treated spend in a more favorable state than the untreated over a prespecified time window. It generalizes the familiar restricted mean survival time (RMST) from the two-state life-death model to account for intermediate stages in disease progression. The overall estimand can be additively decomposed into stage-wise effects, with the standard RMST as a component. Alternate expressions of the overall and stage-wise estimands as integrals of the marginal survival functions for a sequence of landmark transitioning events allow them to be easily estimated by plug-in Kaplan-Meier estimators. The dynamic profile of the estimated treatment effects as a function of follow-up time can be visualized using a multilayer, cone-shaped "bouquet plot." Simulation studies under realistic settings show that the RMT-IF meaningfully and accurately quantifies the treatment effect and outperforms traditional tests on time to the first event in statistical efficiency thanks to its fuller utilization of patient data. The new methods are illustrated on a colon cancer trial with relapse and death as outcomes and a cardiovascular trial with recurrent hospitalizations and death as outcomes. The R-package rmt implements the proposed methodology and is publicly available from the Comprehensive R Archive Network (CRAN).

Sample size requirement in trials that use the composite endpoint major adverse cardiovascular events (MACE): new insights.

BACKGROUND: The real impact of the degree of association (DoA) between endpoint components of a composite endpoint (CE) on sample size requirement (SSR) has not been explored. We estimate the impact of the DoA between death and acute myocardial infarction (AMI) on SSR of trials using use the CE of major adverse cardiac events (MACE). METHODS: A systematic review and quantitative synthesis of trials that include MACE as the primary outcome through search strategies in MEDLINE and EMBASE electronic databases. We limited to articles published in journals indexed in the first quartile of the Cardiac & Cardiovascular Systems category (Journal Citation Reports, 2015-2020). The authors were contacted to estimate the DoA between death and AMI using joint probability and correlation. We analyzed the SSR variation using the DoA estimated from RCTs. RESULTS: Sixty-three of 134 publications that reported event rates and the therapy effect in all component endpoints were included in the quantitative synthesis. The most frequent combination was death, AMI, and revascularization (n = 20; 31.8%). The correlation between death and AMI, estimated from 5 trials¸ oscillated between - 0.02 and 0.31. SSR varied from 14,602 in the scenario with the strongest correlation to 12,259 in the scenario with the weakest correlation; the relative impact was 16%. CONCLUSIONS: The DoA between death and AMI is highly variable and may lead to a considerable SSR variation in a trial including MACE.

Stratified proportional win-fractions regression analysis.

The recently proposed proportional win-fractions (PW) model extends the two-sample win ratio analysis of prioritized composite endpoints to regression. Its proportionality assumption ensures that the covariate-specific win ratios are invariant to the follow-up time. However, this assumption is strong and may not be satisfied by every covariate in the model. We develop a stratified PW model that adjusts for certain prognostic factors without setting them as covariates, thus bypassing the proportionality requirement. We formulate the stratified model based on pairwise comparisons within each stratum, with a common win ratio across strata modeled as a multiplicative function of the covariates. Correspondingly, we construct an estimating function for the regression coefficients in the form of an incomplete U $$ U $$ -statistic consisting of within-stratum pairs. Two types of asymptotic variance estimators are developed depending on the number of strata relative to the sample size. This in particular allows valid inference even when the strata are extremely small, such as with matched pairs. Simulation studies in realistic settings show that the stratified model outperforms the unstratified version in robustness and efficiency. Finally, real data from a major cardiovascular trial are analyzed to illustrate the potential benefits of stratification. The proposed methods are implemented in the R package WR, publicly available on the Comprehensive R Archive Network (CRAN).