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An assurance calculation is a Bayesian alternative to a power calculation. One may be performed to aid the planning of a clinical trial, specifically setting the sample size or to support decisions about whether or not to perform a study. Immuno-oncology is a rapidly evolving area in the development of anticancer drugs. A common phenomenon that arises in trials of such drugs is one of delayed treatment effects, that is, there is a delay in the separation of the survival curves. To calculate assurance for a trial in which a delayed treatment effect is likely to be present, uncertainty about key parameters needs to be considered. If uncertainty is not considered, the number of patients recruited may not be enough to ensure we have adequate statistical power to detect a clinically relevant treatment effect and the risk of an unsuccessful trial is increased. We present a new elicitation technique for when a delayed treatment effect is likely and show how to compute assurance using these elicited prior distributions. We provide an example to illustrate how this can be used in practice and develop open-source software to implement our methods. Our methodology has the potential to improve the success rate and efficiency of Phase III trials in immuno-oncology and for other treatments where a delayed treatment effect is expected to occur., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Introduction: Real-world evidence is receiving considerable attention as a way to evaluate the efficacy and safety of medical products for substance use disorders (SUDs). However, the feasibility of using real-world data (RWD) to emulate clinical trials evaluating treatments for SUDs is uncertain. The aim of this study is to identify the number of clinical trials evaluating treatments for SUDs with reported results that could be feasibly emulated using observational data from contemporary insurance claims and/or electronic health record (EHR) data., Methods: In this cross-sectional study, all phase 2-4 trials evaluating treatments for SUDs registered on ClinicalTrials.gov with reported results were identified. Each trial was evaluated to determine if the indications, interventions, at least 80% of eligibility criteria, comparators, and primary end points could be ascertained using contemporarily available administrative claims and/or structured EHR data., Results: There were 272 SUD trials on ClinicalTrials.gov with reported results. Of these, when examining feasibility using contemporarily available administrative claims and/or structured EHR data, 262 (96.3%) had indications that were ascertainable; 194 (71.3%) had interventions that were ascertainable; 21 (7.7%) had at least 80% of eligibility criteria that were ascertainable; 17 (6.3%) had active comparators that were ascertainable; and 61 (22.4%) had primary end points that were ascertainable. In total, there were no trials for which all 5 characteristics were ascertainable using contemporarily available administrative claims and/or structured EHR data. When considering placebo comparators as ascertainable, there were 6 (2.2%) trials that had all 5 key characteristics classified as ascertainable from contemporarily available administrative claims and/or structured EHR data., Conclusions: No trials evaluating treatments for SUDs could be feasibly emulated using contemporarily available RWD, demonstrating a need for an increase in the resolution of data capture within a public health system to facilitate trial emulation., (© 2024. The Author(s).)

The failure rates of phase 3 trials are high. Incorrect sample size due to uncertainty of effect size could be a critical contributing factor. Adaptive sequential design (ASD), which may include one or more sample size re-estimations (SSR), has been a popular approach for dealing with such uncertainties. The operating characteristics (OCs) of ASD, including the unconditional power and mean sample size, can be substantially affected by many factors, including the planned sample size, the interim analysis schedule and choice of critical boundaries and rules for interim analysis. We propose a systematic, comprehensive strategy which uses iterative simulations to investigate the operating characteristics of adaptive designs and help achieve adequate unconditional power and cost-effective mean sample size if the effect size is in a pre-identified range.

Background: The last years have seen unprecedented improvement in breast cancer (BC) survival rates. However, this entirely apply to female BC patients, since gender minorities (male, transgender/gender-diverse) are neglected in BC phase III registration clinical trials., Methods: We conducted a scoping review of phase III clinical trials of agents with a current positioning within the therapeutic algorithms of BC., Results: We selected 51 phase III trials. Men enrollment was allowed in 35.3% of trials. In none of the trial inclusion/exclusion criteria referred to transgender/gender-diverse people. A numerical higher rate of enrolled men was observed in the contemporary as compared to historical group. We found a statistically significant association between the drug class and the possibility of including men: 100%, 80%, 50%, 33.3%, 25%, 10% and 9.1% of trials testing ICI/PARP-i, ADCs, PI3K/AKT/mTOR-i, anti-HER2 therapy, CDK4/6-i, ET alone, and CT alone. Overall, 77409 patients were enrolled, including 112 men (0.2%). None of the trial reported transgender/gender-diverse people proportion. Studies investigating PARP-i were significantly associated with the highest rate of enrolled men (1.42%), while the lowest rates were observed for trials of CT (0.13%), ET alone (0.10%), and CDK 4/6-I (0.08%), p < 0.001., Conclusions: We confirmed that gender minorities are severely underrepresented among BC registration trials. We observed a lower rate of men in trials envisaging endocrine manipulation or in less contemporary trials. This work sought to urge the scientific community to increase the awareness level towards the issue of gender minorities and to endorse more inclusive criteria in clinical trials., Competing Interests: Declaration of competing interest FM reports personal fees from Roche, Novartis, Pfizer, Seagen, Gilead, Astrazeneca, Lilly, Menarini all outside the submitted work (all the disclosed activities are outside the submitted work). CDA reports advisory role for Roche, Lilly, Novartis, Astrazeneca, Pfizer, Seagen, Daiichi-Sankyo, Gilead, and GSK and speaker honoraria from Roche, Lilly, Novartis, Pfizer, Seagen, GSK, GILEAD, and Daiichi-Sankyo; travel Grants from Gilead and research support (to the Institution) from Novartis, GILEAD, and Daiichi-Sankyo outside the submitted work (all the disclosed activities are outside the submitted work). RC reports fees for talk or consultation from Novartis, Lilly, MSD, Gilead, Roche, Pfizer, Veracyte, Seagen, Astra Zeneca, Daichii Sankyo, Menarini, Pierre-Fabre (all the disclosed activities are outside the submitted work). EB has received grants or contracts from Astra-Zeneca, Roche and honoraria for lectures from Merck-Sharp & Dome, Astra-Zeneca, Pfizer, Eli-Lilly, Bristol-Myers Squibb, Novartis, Takeda and Roche; he has been member of Data Safety Monitoring Board or Advisory Board of Merck-Sharp & Dome, Pfizer, Novartis, Bristol-Myers Squibb, Astra-Zeneca, and Roche (all the disclosed activities are outside the submitted work). AV reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events: AndromedaE20, Oncotech; support for attending meetings and/or travel: Pierre Fabre, Lilly, Gilead, Seagen, Novartis, MSD, Menarini (all the disclosed activities are outside the submitted work). LDM reports institutional grants from Eli Lilly, Novartis, Roche, Daiichi Sankyo, Seagen, Astrazeneca, Gilead, Pierre Fabre; consulting fees: Eli Lilly, Gilead, Daiichi Sankyio; payment or honoraria for lectures, presentations, speakers bureaus: Roche, Novartis, Pfizer, Eli Lilly, Astrazeneca, MSD, Seagen, Gilead, Pierre Fabre, Eisai, Exact Sciences, Ipsen, GSK, Agendia, Stemline; support for attending meetings and/or travel: Roche, Pfizer, Eisai, Daiichi Sankyo, Astrazeneca, Gilead; Participation on a Data Safety Monitoring Board or Advisory Board: Novartis, Roche, Eli Lilly, Pfizer, Daiichi Sakyo, Exact Sciences, Gilead, Pierre Fabre, Eisai, Astrazeneca, Agendia, GSK, Seagen, Olema, MSD, Stemline Menarini (all the disclosed activities are outside the submitted work). FM reports consultancy fees from Roche, Novartis, AstraZeneca, Daiichy Sankyo, SeaGen, MSD, Eli Lilly, Pfizer, and Pierre Fabre, and Travel Grants from Roche and Astra Zeneca; from May 15th, 2023, FM is Roche employee (all the disclosed activities are outside the submitted work). VG reports personal fees for advisory board membership for AstraZeneca, Daiichi Sankyo, Eisai, Eli Lilly, Exact Sciences, Gilead, Merck Serono, MSD, Novartis, Pfizer, Olema Oncology, Pierre Fabre; personal fees as an invited speaker for AstraZeneca, Daiichi Sankyo, Eli Lilly, Exact Sciences, Gilead, GSK, Novartis, Roche and Zentiva; personal fees for expert testimony for Eli Lilly. All the remaining authors have no conflict of interest to report., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Vaccine efficacy (VE) assessed in a randomized controlled clinical trial can be affected by demographic, clinical, and other subject-specific characteristics evaluated as baseline covariates. Understanding the effect of covariates on efficacy is key to decisions by vaccine developers and public health authorities., Methods: This work evaluates the impact of including correlate of protection (CoP) data in logistic regression on its performance in identifying statistically and clinically significant covariates in settings typical for a vaccine phase 3 trial. The proposed approach uses CoP data and covariate data as predictors of clinical outcome (diseased versus non-diseased) and is compared to logistic regression (without CoP data) to relate vaccination status and covariate data to clinical outcome., Results: Clinical trial simulations, in which the true relationship between CoP data and clinical outcome probability is a sigmoid function, show that use of CoP data increases the positive predictive value for detection of a covariate effect. If the true relationship is characterized by a decreasing convex function, use of CoP data does not substantially change positive or negative predictive value. In either scenario, vaccine efficacy is estimated more precisely (i.e., confidence intervals are narrower) in covariate-defined subgroups if CoP data are used, implying that using CoP data increases the ability to determine clinical significance of baseline covariate effects on efficacy., Conclusions: This study proposes and evaluates a novel approach for assessing baseline demographic covariates potentially affecting VE. Results show that the proposed approach can sensitively and specifically identify potentially important covariates and provides a method for evaluating their likely clinical significance in terms of predicted impact on vaccine efficacy. It shows further that inclusion of CoP data can enable more precise VE estimation, thus enhancing study power and/or efficiency and providing even better information to support health policy and development decisions., (© 2024. Merck & Co., Inc., Rahway, NJ, USA and its affiliates, Institute of Computer Science of the Czech Academy of Sciences.)

Background: Older patients are underrepresented in the clinical trials that determine the standards of care for oncological treatment. We conducted a review to identify whether there have been age-restrictive inclusion criteria in clinical trials over the last twenty five years, focusing on patients with metastatic gastroesophageal cancer., Methods: A search strategy was developed encompassing Embase, PubMed and The Cochrane Library databases. Completed phase III randomised controlled trials evaluating systemic anti-cancer therapies in metastatic gastroesophageal malignancies from 1st January 1995 to 18th November 2020 were identified. These were screened for eligibility using reference management software (Covidence; Veritas Health Innovation Ltd). Data including age inclusion/exclusion criteria and median age of participants were recorded. The percentage of patients ≥ 65 enrolled was collected where available. The change over time in the proportion of studies using an upper age exclusion was estimated using a linear probability model., Results: Three hundred sixty-three phase III studies were identified and screened, with 66 trials remaining for final analysis. The majority of trials were Asian (48%; n = 32) and predominantly evaluated gastric malignancies, (86%; n = 56). The median age of participants was 62 (range 18-94). Thirty-two percent (n = 21) of studies specified an upper age limit for inclusion and over half of these were Asian studies. The median age of exclusion was 75 (range 65-80). All studies prior to 2003 used an upper age exclusion (n = 12); whereas only 9 that started in 2003 or later did (17%). Among later studies, there was a very modest downward yearly-trend in the proportion of studies using an upper age exclusion (-0.02 per year; 95%CI -0.05 to 0.01; p = 0.31). Fifty-two percent (n = 34) of studies specified the proportion of their study population who were ≥ 65 years. Older patients represented only 36% of the trial populations in these studies (range 7-60%)., Conclusions: Recent years have seen improvements in clinical trial protocols, with many no longer specifying restrictive age criteria. Reasons for poor representation of older patients are complex and ongoing efforts are needed to broaden eligibility criteria and prioritise the inclusion of older adults in clinical trials., (© 2021. The Author(s).)

Background: Studies focusing on prediction models are widespread in medicine. There is a trend in applying machine learning (ML) by medical researchers and clinicians. Over the years, multiple ML algorithms have been adapted to censored data. However, the choice of methodology should be motivated by the real-life data and their complexity. Here, the predictive performance of ML techniques is compared with statistical models in a simple clinical setting (small/moderate sample size and small number of predictors) with Monte-Carlo simulations., Methods: Synthetic data (250 or 1000 patients) were generated that closely resembled 5 prognostic factors preselected based on a European Osteosarcoma Intergroup study (MRC BO06/EORTC 80931). Comparison was performed between 2 partial logistic artificial neural networks (PLANNs) and Cox models for 20, 40, 61, and 80% censoring. Survival times were generated from a log-normal distribution. Models were contrasted in terms of the C-index, Brier score at 0-5 years, integrated Brier score (IBS) at 5 years, and miscalibration at 2 and 5 years (usually neglected). The endpoint of interest was overall survival., Results: PLANNs original/extended were tuned based on the IBS at 5 years and the C-index, achieving a slightly better performance with the IBS. Comparison with Cox models showed that PLANNs can reach similar predictive performance on simulated data for most scenarios with respect to the C-index, Brier score, or IBS. However, Cox models were frequently less miscalibrated. Performance was robust in scenario data where censored patients were removed before 2 years or curtailing at 5 years was performed (on training data)., Conclusion: Survival neural networks reached a comparable predictive performance with Cox models but were generally less well calibrated. All in all, researchers should be aware of burdensome aspects of ML techniques such as data preprocessing, tuning of hyperparameters, and computational intensity that render them disadvantageous against conventional regression models in a simple clinical setting., Competing Interests: The authors declare that they have no competing interests., (Copyright © 2021 Georgios Kantidakis et al.)