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Definitive evidence of clinical efficacy in a Phase 3 trial is best shown by a beneficial impact on a clinically meaningful endpoint-that is, an endpoint that directly measures how a patient feels (symptoms), functions (the ability to perform activities in daily life), or survives. In idiopathic pulmonary fibrosis (IPF), we believe the endpoints that best meet these criteria are all-cause mortality and all-cause nonelective hospitalization. There are no validated measures of symptoms or broader constructs such as health status or functional status in IPF. A surrogate endpoint is defined as an indirect measure that is intended to substitute for a clinically meaningful endpoint. Surrogate endpoints can be appropriate outcome measures if validated. However, validation requires substantial evidence that the effect of an intervention on a clinically meaningful endpoint is reliably predicted by the effect of an intervention on the surrogate endpoint. For patients with IPF, there are currently no validated surrogate endpoints.

•Surrogate endpoints can accelerate early-phase drug development and support regulatory approval. They may be useful for internal decision-making process in Phase I/II trials, enabling faster go/no-go decisions and, in some cases, serve as a basis for conditional approval.•Interpretation of surrogate endpoint data requires cautionSurrogate markers may not reliably reflect true clinical benefit and can lead to misleading conclusions if they are not properly validated.•Clinical endpoints remain the gold standard for evaluating patient benefitWhenever feasible, trials should prioritize endpoints that directly measure how patients feel, function, or survive.•Rigorous validation is essential for surrogate endpoints used in pivotal trialsSurrogates endpoints must be supported by strong biological rationale and robust empirical evidence linking them to meaningful clinical outcomes. The use of biomarkers and surrogate endpoints has become increasingly important in drug development, regulatory decision-making, and clinical practice. This review provides an overview of the distinctions between clinical and surrogate endpoints, defines types of biomarkers, and outlines the criteria used to validate biomarkers as surrogate endpoints. Finally, selected examples in clinical trials in cardiovascular drug development, anticancer drug development and antidiabetic drugs drug development illustrate how these concepts are applied in real-world drug development.

Background Considering multiple endpoints in clinical trials provide a more comprehensive understanding of treatment effects and may lead to increased power or reduced sample size, which may be beneficial in rare diseases. Besides the small sample sizes, allocation bias is an issue that affects the validity of these trials. We investigate the impact of allocation bias on testing decisions in clinical trials with multiple endpoints and offer a tool for selecting an appropriate randomization procedure (RP). Methods We derive a model for quantifying the effect of allocation bias depending on the RP in the case of two-arm parallel group trials with continuous multiple endpoints. We focus on two approaches to analyze multiple endpoints, either the Šidák procedure to show efficacy in at least one endpoint and the all-or-none procedure to show efficacy in all endpoints. Results To evaluate the impact of allocation bias on the test decision we propose a biasing policy for multiple endpoints. The impact of allocation on the test decision is measured by the family-wise error rate of the Šidák procedure and the type I error rate of the all-or-none procedure. Using the biasing policy we derive formulas to calculate these error rates. In simulations we show that, for the Šidák procedure as well as for the all-or-none procedure, allocation bias leads to inflation of the mean family-wise error and mean type I error, respectively. The strength of this inflation is affected by the choice of the RP. Conclusion Allocation bias should be considered during the design phase of a trial to increase validity. The developed methodology is useful for selecting an appropriate RP for a clinical trial with multiple endpoints to minimize allocation bias effects.

The aim of this study is twofold: i) to determine innovative yet sensitive endpoints for sulfoxaflor and ii) to develop best practices for innovative teaching in ecotoxicology. To this end, a group of 52 MSc students participated in an environmental hackathon , during which they did creative toxicity testing on 5 freshwater invertebrate species: Daphnia magna, Chironomus riparius, Asellus aquaticus, Lymnaea stagnalis, and Anisus vortex . Involving the students in an active learning environment stimulated increased creativity and productivity. In total, 28 endpoints were investigated, including standard endpoints (e.g., mortality) as well as biomechanistic and energy-related endpoints. Despite high variances in the results, likely linked to the limited lab experience of the students and interpersonal differences, a promising set of endpoints was selected for further investigation. A more targeted follow-up experiment focused on the most promising organism and set of endpoints: biomechanistic endpoints of C. riparius larvae. Larvae were exposed to a range of sulfoxaflor concentrations (0.90–67.2 μg/L) for 21 days. Video tracking showed that undulation and swimming were significantly reduced at 11.1 μg sulfoxaflor/L after 9 days of exposure, and an EC 50 = 10.6 μg/L for mean velocities of the larvae in the water phase was found. Biomechanistic endpoints proved much more sensitive than mortality, for which an LC 50 value of 116 μg/L was found on Day 9. Our results show that performing a hackathon with students has excellent potential to find sensitive endpoints that can subsequently be verified using more targeted and professional follow-up experiments. Furthermore, utilising hackathon events in teaching can increase students’ enthusiasm about ecotoxicology, driving better learning experiences.

Despite a better understanding of the pathophysiology and development of newer therapeutic options, cancer remains an area with several unmet needs. Although overall survival (OS) remains a gold standard endpoint for all cancer therapies, it poses challenges such as the requirement of a long-term follow-up, a higher number of patients, and a higher financial burden. Therefore, surrogate endpoints such as progression-free survival, time to progression, duration of response, and objective response rate are being investigated and used in oncology studies. Patient-related outcomes that measure the patient's overall health, quality of life, and satisfaction in the long term are crucial surrogate endpoints considered for drug approval. Surrogate endpoints shorten oncology clinical studies and accelerate the evaluation and implementation of newer therapies. Emerging surrogate endpoints such as biomarkers, immune-related response criteria, minimal residual disease, and pathological complete response are increasingly being considered in oncology trials. Validation of surrogate endpoints enables their substitution for OS and gain market approval. The selection of surrogate endpoints for an oncology trial depends on cancer type and stage, the purpose of treatment, and expected duration of survival for the relevant disease. With the advent of individualized approach and complex study designs, the field of oncology is currently undergoing a paradigm shift. The use of newer surrogate endpoints will aid in accelerating the drug development process, making patient care for oncology more accessible.

Studies often combine several events, for example, death or myocardial infarction or stroke, into a single study outcome. This is called a composite endpoint. Composite endpoints make doing trials easier by reducing the sample size or follow-up period required to demonstrate the effectiveness of an intervention. However, interpreting the results of composite endpoints can be confusing. To avoid misleading conclusions about the effectiveness of an intervention, it is important for readers of studies reporting a composite endpoint to ascertain that the clinical importance, the frequency of events, and the effect of the intervention on each component of the composite endpoint are similar. •Studies often combine several events into a single study outcome, the composite endpoint.•Using composite endpoints reduces the sample size or follow-up period required to demonstrate the effectiveness of an intervention.•To avoid misleading conclusions about the effectiveness of an intervention, it is important to ascertain that the clinical importance, the frequency of events, and the effect of the intervention on each component of the composite endpoint are similar.

The main purpose of using a surrogate endpoint is to estimate the treatment effect on the true endpoint sooner than with a true endpoint. Based on a metaregression of historical randomized trials with surrogate and true endpoints, we discuss statistics for applying and evaluating surrogate endpoints. We computed statistics from 2 types of linear metaregressions for trial-level data: simple random effects and novel random effects with correlations among estimated treatment effects in trials with more than 2 arms. A key statistic is the estimated intercept of the metaregression line. An intercept that is small or not statistically significant increases confidence when extrapolating to a new treatment because of consistency with a single causal pathway and invariance to labeling of treatments as controls. For a regulator applying the metaregression to a new treatment, a useful statistic is the 95% prediction interval. For a clinical trialist planning a trial of a new treatment, useful statistics are the surrogate threshold effect proportion, the sample size multiplier adjusted for dropouts, and the novel true endpoint advantage. We illustrate these statistics with surrogate endpoint metaregressions involving antihypertension treatment, breast cancer screening, and colorectal cancer treatment. Regulators and trialists should consider using these statistics when applying and evaluating surrogate endpoints.

ABSTRACT Regulatory guidance documents exist on surrogate endpoints in the United States. In Japan, there are no established rules or guidance regarding the use of surrogate endpoints, and various aspects remain unclear. The aim of this study was to investigate the use of surrogate endpoints in Japan for drugs approved in Japan from 1999 to 2022, referring to the list established by the Food and Drug Administration. Precisely 2307 drugs were approved in Japan during the 24‐year survey period. Of these, 1012 drugs (43.9%) were indicated for diseases for which surrogate endpoints were specified in the Surrogate Endpoint Table established by the Food and Drug Administration. After examining the endpoints used in clinical trials for 1012 drugs, 947 (93.6%) were approved based on the same surrogate endpoint as the Food and Drug Administration, whereas 65 (6.4%) were approved based on a different surrogate endpoint. In areas such as diabetes, there was a tendency to use surrogate endpoints established by the Food and Drug Administration, whereas for pharmaceuticals targeting pathogenic organisms, Japan's surrogate endpoints were typically used. Our findings demonstrate that several pharmaceuticals in Japan use surrogate endpoints similar to those of the Food and Drug Administration. These findings are expected to aid in the formulation of guidelines for the use of surrogate endpoints in clinical trials for future drug approvals in Japan.

Background Many clinical trials focus on the comparison of the treatment effect between two or more groups concerning a rarely occurring event. In this situation, showing a relevant effect with an acceptable power requires the observation of a large number of patients over a long period of time. For feasibility issues, it is therefore often considered to include several event types of interest, non-fatal or fatal, and to combine them within a composite endpoint. Commonly, a composite endpoint is analyzed with standard survival analysis techniques by assessing the time to the first occurring event. This approach neglects that an individual may experience more than one event which leads to a loss of information. As an alternative, composite endpoints could be analyzed by models for recurrent events. There exists a number of such models, e.g. regression models based on count data or Cox-based models such as the approaches of Andersen and Gill, Prentice, Williams and Peterson or, Wei, Lin and Weissfeld. Although some of the methods were already compared within the literature there exists no systematic investigation for the special requirements regarding composite endpoints. Methods Within this work a simulation-based comparison of recurrent event models applied to composite endpoints is provided for different realistic clinical trial scenarios. Results We demonstrate that the Andersen-Gill model and the Prentice- Williams-Petersen models show similar results under various data scenarios whereas the Wei-Lin-Weissfeld model delivers effect estimators which can considerably deviate under commonly met data scenarios. Conclusion Based on the conducted simulation study, this paper helps to understand the pros and cons of the investigated methods in the context of composite endpoints and provides therefore recommendations for an adequate statistical analysis strategy and a meaningful interpretation of results.

The evaluation of health benefits and harms of an intervention with GRADE Evidence to Decision (EtD) frameworks includes judgments if the effects are “trivial,” “small,” “moderate,” or “large.” Such judgments ideally require the a priori establishment of decision thresholds (DTs), whose empirical derivation for single outcomes has been previously described. In this article, we provide a methodological approach to estimate DTs for composite endpoints based on disutilities. We generated an approach that involves the computation of pooled disutilities, in which the disutility of each outcome comprised in the composite endpoint is weighted by the respective relative frequency. We also applied a modeling approach based on probability distributions to account for uncertainty in the estimates. We present a practical example of the determination of DTs associated with the development of at least one adverse event following treatment with intranasal medications for rhinitis that we used in the Allergic Rhinitis and its Impact on Asthma guidelines. We provide the methodological steps of a modeling-based approach to compute pooled disutilities and, as a result, DTs for composite endpoints. We have developed a webtool (https://compositedt.med.up.pt/) that allows for a simple implementation of the proposed approach. Applying our approach to a practical example, we concluded that rhinitis nasal medications, compared to placebo, were associated with a trivial harm from adverse events. We propose an approach for estimating DTs for composite endpoints, which may be particularly valuable whenever composite endpoints are used for clinical research, clinical practice, and decision-making. •An approach has been developed to compute DTs for composite endpoints.•The approach is based on weighted disutilities and allows to consider uncertainty.•We provide detailed stepwise guidance and a webtool to implement this approach.