Considerations for using potential surrogate endpoints in cancer screening trials.

The requirement of large-scale expensive cancer screening trials spanning decades creates considerable barriers to the development, commercialisation, and implementation of novel screening tests. One way to address these problems is to use surrogate endpoints for the ultimate endpoint of interest, cancer mortality, at an earlier timepoint. This Review aims to highlight the issues underlying the choice and use of surrogate endpoints for cancer screening trials, to propose criteria for when and how we might use such endpoints, and to suggest possible candidates. We present the current landscape and challenges, and discuss lessons and shortcomings from the therapeutic trial setting. It is hugely challenging to validate a surrogate endpoint, even with carefully designed clinical studies. Nevertheless, we consider whether there are candidates that might satisfy the requirements defined by research and regulatory bodies.

Methodological challenges in the development of endpoints for myelofibrosis clinical trials.

Myelofibrosis is a myeloid neoplasm characterised by the presence of JAK2, CALR, or MPL mutations (with a 90% mutation frequency) and trilineage myeloid proliferation with prominent megakaryocyte atypia. People with myelofibrosis have a lower survival rate and poorer quality of life than healthy individuals. Therapy for myelofibrosis uses Janus kinase inhibitors, which reduce splenomegaly and alleviate symptoms. Regulatory approvals for Janus kinase inhibitors have focused on this dual endpoint. In this Viewpoint, we discuss the validity of using spleen reduction as a surrogate endpoint for the disease-modifying activity of candidate drugs for myelofibrosis. We suggest alternative endpoints addressing unmet patient needs, including progression-free survival and overall survival. Moreover, we highlight the importance of selecting a core set of crucial outcomes with which we can individualise clinical decision making and standardise reporting of clinical trials results. We propose selecting patient-reported outcomes and anaemia response. We also suggest integrating economic considerations in the process of evaluating new drugs for myelofibrosis.

Improvement of assessment in surrogate endpoint and safety outcome of single-arm trials for anticancer drugs.

INTRODUCTION: Single-arm trials (SATs) and surrogate endpoints were adopted as pivotal evidence for accelerated approval of anticancer drugs for more than 30 years. However, concerns regarding clinical evidence quality in trials, particularly in the SATs of anticancer drugs have increasingly been raised. SAT may not always provide strong evidence due to the lack of control and endpoint of overall survival that is typically present in randomized controlled trials. AREAS COVERED: Clinical trial endpoint adjudication is a crucial factor in surrogate outcome measurement to ensure the data quality of the clinical trial of anticancer drugs. In this review, we systematically discuss the characteristics of adjudications in assessments in surrogate endpoint and safety outcome respectively, which are essential for ensuring reliable and transparent outcomes. Endpoint adjudication effectively reduces potential bias and mitigates variance that may be introduced by investigators when analyzing the medical records for the surrogate endpoints. We analyze the advantages and disadvantages of each type of adjudicator and provide a summary of the roles of adjudicators. EXPERT OPINION: By suggestion of improving data reliability and transparency in pivotal trials, this review aims to supply a strategy for better clinical investigation for anticancer drugs, ultimately leading to better patient outcomes.

Composite event-free-survival as an endpoint in oncology drug evaluation: Review and guidance perspectives from the Haute Autorité de Santé (HAS).

BACKGROUND: The use of right-censored composite endpoints, such as progression-free survival, has been questioned in haemato-oncology trials due to potential bias in estimated treatment effect. This may impact the accuracy of health technology evaluations. We hypothesized that there is heterogeneity and potential sources of bias in the reporting of composite endpoints to health technology assessment (HTA) bodies. METHODS: We reviewed the submissions for reimbursement of oncology drugs in 2021 and 2022 that used a composite endpoint in the pivotal trial, after appraisal by the French HTA body. The retrieved information included the clinical study report, protocol, and statistical analysis plan submitted by the industry. All events of the composite endpoint and all causes of censored observations were measured. The design characteristics and treatment effect estimates were recorded. FINDINGS: Seventy-six submissions were selected, including seven without a right-censored endpoint and four evaluating associations, resulting in 65 analysed records: 17 for haematological and 48 for solid tumours. Out these 65 submissions, 47 (72·3%) used a randomized controlled design, and 18 (27·7%) a non-comparative design. The most frequently used composite endpoint was progression-free survival, used in 54 (83·1%) of the submissions. Censoring was possibly informative in 51 (92·7%) cases, mostly due to the onset of new treatment (44/51, 86·3%) and/or discontinuation of follow-up (33/51, 64·7%). In contrast, 38 (58·5%) trials reported a quantification of censored observations, with only 12/51 (23·5%) quantifying the informative ones. The estimated treatment effect on the composite outcome increased with the amount of censoring, suggesting a higher benefit of the drug, but remained below that on survival with poor evidence of surrogacy (R-squared=0·23). INTERPRETATION: Clinical study reports should be improved in terms of reporting censoring, while stakeholders should be aware of this potential source of bias. At a minimum, sensitivity analysis that ignores intercurrent events should be requested.

Protocol for the development of a core outcome set and reporting guidelines for locoregional treatment in neoadjuvant systemic breast cancer treatment trials: the PRECEDENT project.

INTRODUCTION: Neoadjuvant systemic anticancer therapy (neoSACT) is increasingly used in the treatment of early breast cancer. Response to therapy is prognostic and allows locoregional and adjuvant systemic treatments to be tailored to minimise morbidity and optimise oncological outcomes and quality of life. Accurate information about locoregional treatments following neoSACT is vital to allow the translation of downstaging benefits into practice and facilitate meaningful interpretation of oncological outcomes, particularly locoregional recurrence. Reporting of locoregional treatments in neoSACT studies, however, is currently poor. The development of a core outcome set (COS) and reporting guidelines is one strategy by which this may be improved. METHODS AND ANALYSIS: A COS for reporting locoregional treatment (surgery and radiotherapy) in neoSACT trials will be developed in accordance with Core Outcome Measures in Effectiveness Trials (COMET) and Core Outcome Set-Standards for Development guidelines. Reporting guidance will be developed concurrently.The project will have three phases: (1) generation of a long list of relevant outcome domains and reporting items from a systematic review of published neoSACT studies and interviews with key stakeholders. Identified items and domains will be categorised and formatted into Delphi consensus questionnaire items. (2) At least two rounds of an international online Delphi survey in which at least 250 key stakeholders (surgeons/oncologists/radiologists/pathologists/trialists/methodologists) will score the importance of reporting each outcome. (3) A consensus meeting with key stakeholders to discuss and agree the final COS and reporting guidance. ETHICS AND DISSEMINATION: Ethical approval for the consensus process will be obtained from the Queen's University Belfast Faculty Ethics Committee. The COS/reporting guidelines will be presented at international meetings and published in peer-reviewed journals. Dissemination materials will be produced in collaboration with our steering group and patient advocates so the results can be shared widely. REGISTRATION: The study has been prospectively registered on the COMET website (https://www.comet-initiative.org/Studies/Details/2854).

Study protocol: a core outcome set for perioperative exercise clinical effectiveness trials for lung cancer patients.

BACKGROUND: Outcome assessment in perioperative exercise trials for lung cancer is heterogeneous, often omitting those that are important and patient-relevant. This heterogeneity hinders the synthesis of evidence. To address this issue, a core outcome set, an agreed-upon standardized set of outcomes to be measured and reported, is required to reduce heterogeneity among outcome measurements. This study protocol describes the methodology, aiming to develop a core outcome set for perioperative exercise intervention trials for lung cancer in clinical practice. METHODS: The project will follow the standard methodology recommended by the Core Outcome Measures in Effectiveness Trials (COMET) initiative, which is divided into four steps. Stage I: Conducting a scoping review of outcomes reported in clinical trials and protocols to develop a list of potential outcome domains. Stage II: Conducting semi-structured interviews to obtain important outcomes for patients. Stage III: Choosing the most important outcomes by conducting two rounds of the Delphi exercise. Stage IV: Achieving a consensus in a face-to-face meeting to discuss the final core outcome set. DISCUSSION: This is the first project identified for the core outcome set of perioperative exercise trials in lung cancer, which will enhance the quality, comparability, and usability of future trials and positively impact perioperative exercise and the care of patients with lung cancer. TRIALS REGISTRATION: Core Outcome Measurement in Effectiveness Trials (COMET) Initiative database registration: https://www.comet-initiative.org/Studies/Details/2091.

Using short-term endpoints to improve interim decision making and trial duration in two-stage phase II trials with nested binary endpoints.

In oncology, phase II clinical trials are often planned as single-arm two-stage designs with a binary endpoint, for example, progression-free survival after 12 months, and the option to stop for futility after the first stage. Simon's two-stage design is a very popular approach but depending on the follow-up time required to measure the patients' outcomes the trial may have to be paused undesirably long. To shorten this forced interruption, it was proposed to use a short-term endpoint for the interim decision, such as progression-free survival after 3 months. We show that if the assumptions for the short-term endpoint are misspecified, the decision-making in the interim can be misleading, resulting in a great loss of statistical power. For the setting of a binary endpoint with nested measurements, such as progression-free survival, we propose two approaches that utilize all available short-term and long-term assessments of the endpoint to guide the interim decision. One approach is based on conditional power and the other is based on Bayesian posterior predictive probability of success. In extensive simulations, we show that both methods perform similarly, when appropriately calibrated, and can greatly improve power compared to the existing approach in settings with slow patient recruitment. Software code to implement the methods is made publicly available.

Using a Clinically Interpretable End Point Composed of Multiple Outcomes to Evaluate Totality of Treatment Effect in Comparative Oncology Studies.

This cohort study demonstrates how to use cumulative event count curves to create a clinically meaningful end point by simultaneously considering recurrence, progression, and survival times from the individual patient.

Neutral theory: applicability and neutrality of clinical study endpoints where a disease-specific instrument is available.

BACKGROUND: There is a pressing need to improve the accuracy of rare disease clinical study endpoints. Neutral theory, first described here, can be used to assess the accuracy of endpoints and improve their selection in rare disease clinical studies, reducing the risk of patient misclassification. METHODS: Neutral theory was used to assess the accuracy of rare disease clinical study endpoints and the resulting probability of false positive and false negative classifications at different disease prevalence rates. Search strings were extracted from the Orphanet Register of Rare Diseases using a proprietary algorithm to conduct a systematic review of studies published until January 2021. Overall, 11 rare diseases with one disease-specific disease severity scale (133 studies) and 12 rare diseases with more than one disease-specific disease severity scale (483 studies) were included. All indicators from clinical studies were extracted, and Neutral theory was used to calculate their match to disease-specific disease severity scales, which were used as surrogates for the disease phenotype. For those with more than one disease-severity scale, endpoints were compared with the first disease-specific disease severity scale and a composite of all later scales. A Neutrality score of > 1.50 was considered acceptable. RESULTS: Around half the clinical studies for half the rare diseases with one disease-specific disease severity score (palmoplantar psoriasis, achalasia, systemic lupus erythematosus, systemic sclerosis and Fournier's gangrene) met the threshold for an acceptable match to the disease phenotype, one rare disease (Guillain-Barré syndrome) had one study with an acceptable match, and four diseases (Behcet's syndrome, Creutzfeldt-Jakob disease, atypical hemolytic uremic syndrome and Prader-Willi syndrome) had no studies. Clinical study endpoints in almost half the rare diseases with more than one disease-specific DSS (acromegaly, amyotrophic lateral sclerosis, cystic fibrosis, Fabry disease and juvenile rheumatoid arthritis) were a better match to the composite, while endpoints in the remaining rare diseases (Charcot Marie Tooth disease, Gaucher disease Type I, Huntington's disease, Sjogren's syndrome and Tourette syndrome) were a worse match. Misclassifications varied with increasing disease prevalence. CONCLUSIONS: Neutral theory confirmed that disease-severity measurement needs improvement in rare disease clinical studies, especially for some diseases, and suggested that the potential for accuracy increases as the body of knowledge on a disease increases. Using Neutral theory to benchmark disease-severity measurement in rare disease clinical studies may reduce the risk of misclassification, ensuring that recruitment and treatment effect assessment optimise medicine adoption and benefit patients.

The RoboCOS Study: Development of an international core outcome set for the comprehensive evaluation of patient, surgeon, organisational and population level impacts of robotic assisted surgery.

BACKGROUND: The introduction of robot-assisted surgery is costly and requires whole system transformation, which makes the assessment of benefits (or drawbacks) complex. To date, there has been little agreement on which outcomes should be used in this regard. The aim of the RoboCOS study was to develop a core outcome set for the evaluation of robot-assisted surgery that would account for its impact on the whole system. METHODS: Identification of a long-list of potentially relevant outcomes through systematic review of trials and health technology assessments; interviews with individuals from a range of stakeholder groups (surgeons, service managers, policy makers and evaluators) and a focus group with patients and public; prioritisation of outcomes via a 2-round online international Delphi survey; consensus meeting. RESULTS: 721 outcomes were extracted from the systematic reviews, interviews and focus group which were conceptualised into 83 different outcome domains across four distinct levels (patient, surgeon, organisation and population) for inclusion in the international Delphi prioritisation survey (128 completed both rounds). The consensus meeting led to the agreement of a 10-item core outcome set including outcomes at: patient level (treatment effectiveness; overall quality of life; disease-specific quality of life; complications (including mortality); surgeon level (precision/accuracy; visualisation); organisation (equipment failure; standardisation of operative quality; cost-effectiveness); and population (equity of access). CONCLUSION: The RoboCOS core outcome set, which includes the outcomes of importance to all stakeholders, is recommended for use in all future evaluations of robot-assisted surgery to ensure relevant and comparable reporting of outcomes.

The research status and progress of core outcome set in oral health.

The core outcome set (COS) refers to the minimum set of outcomes that should be reported by all clinical trials in a particular health field. The use of COS in clinical studies can reduce the heterogeneity caused by using different outcomes across different clinical studies, facilitate the systematic review of different clinical studies on the same topic, reduce selective reporting bias, and increase the utility of clinical studies. The importance of COS in oral health has recently been recognized. This review summarizes the history, necessity, and key methodological points of COS development, with emphasis on the research status and existing problems in COS development, in the field of oral health.

Using surrogate information to improve confirmatory platform trial with sample size re-estimation.

Platform design which allows exploring multiple arms with a common control simultaneously is becoming essential for efficient drug development. However, one of the critical challenges for confirmatory platform trials is immature data for interim decisions, particularly for the treatment arm selection and sample size determination with limited data available. We use a modified conditional power (CP) for both treatment arm selection and sample size determination at interim analysis for the proposed platform trial. The modified CP uses the available data from both primary and surrogate endpoints. We also demonstrated the application in a case study of a lung cancer trial.

OSPred Tool: A Digital Health Aid for Rapid Predictive Analysis of Correlations Between Early End Points and Overall Survival in Non-Small-Cell Lung Cancer Clinical Trials.

PURPOSE: Overall survival (OS) is the gold standard end point for establishing clinical benefits in phase III oncology trials. However, these trials are associated with low success rates, largely driven by failure to meet the primary end point. Surrogate end points such as progression-free survival (PFS) are increasingly being used as indicators of biologic drug activity and to inform early go/no-go decisions in oncology drug development. We developed OSPred, a digital health aid that combines actual clinical data and machine intelligence approaches to visualize correlation trends between early (PFS-based) and late (OS) end points and provide support for shared decision making in the drug development pipeline. METHODS: OSPred is based on a trial-level data set of 81 reports (35 anticancer drugs with various mechanisms of action; 156 observations) in non-small-cell lung cancer (NSCLC). OSPred was developed using R Shiny, with packages ggplot2, metafor, boot, dplyr, and mvtnorm, to analyze and visualize correlation results and predict OS hazard ratio (HR OS) on the basis of user-inputted PFS-based data, namely, HR PFS, or the odds ratio of PFS at 4 (OR PFS4) or 6 (OR PFS6) months. RESULTS: The three main features of the tool are as follows: prediction of HR OS on the basis of user-inputted early end point values; visualization of comparisons of the user's investigational drug with other drugs in the NSCLC setting, including by specific MoA; and creation of a probability density chart, providing point prediction and CIs for HR OS. A working version of the tool for download is linked. CONCLUSION: The OSPred tool offers interactive visualization of clinical trial end point correlations with reference to a large pool of historical NSCLC studies. Its focused capability has the potential to digitally transform and accelerate data-driven decision making as part of the drug development process.

Calculating power for the Finkelstein and Schoenfeld test statistic for a composite endpoint with two components.

The Finkelstein and Schoenfeld (FS) test is a popular generalized pairwise comparison approach to analyze prioritized composite endpoints (eg, components are assessed in order of clinical importance). Power and sample size estimation for the FS test, however, are generally done via simulation studies. This simulation approach can be extremely computationally burdensome, compounded by increasing number of composite endpoints and with increasing sample size. Here we propose an analytical solution to calculate power and sample size for commonly encountered two-component hierarchical composite endpoints. The power formulas are derived assuming underlying distributions in each of the component outcomes on the population level, which provide a computationally efficient and practical alternative to the standard simulation approach. Monte Carlo simulation results demonstrate that performance of the proposed power formulas are consistent with that of the simulation approach, and have generally desirable objective properties including robustness to mis-specified distributional assumptions. We demonstrate the application of the proposed formulas by calculating power and sample size for the Transthyretin Amyloidosis Cardiomyopathy Clinical Trial.

Confirmatory adaptive group sequential designs for single-arm phase II studies with multiple time-to-event endpoints.

Existing methods concerning the assessment of long-term survival outcomes in one-armed trials are commonly restricted to one primary endpoint. Corresponding adaptive designs suffer from limitations regarding the use of information from other endpoints in interim design changes. Here we provide adaptive group sequential one-sample tests for testing hypotheses on the multivariate survival distribution derived from multi-state models, while making provision for data-dependent design modifications based on all involved time-to-event endpoints. We explicitly elaborate application of the methodology to one-sample tests for the joint distribution of (i) progression-free survival (PFS) and overall survival (OS) in the context of an illness-death model, and (ii) time to toxicity and time to progression while accounting for death as a competing event. Large sample distributions are derived using a counting process approach. Small sample properties are studied by simulation. An already established multi-state model for non-small cell lung cancer is used to illustrate the adaptive procedure.

Development of a core outcome set and outcome measurement set for physiotherapy trials in adults with Bronchiectasis (COS-PHyBE study): A protocol.

BACKGROUND: Bronchiectasis is a chronic respiratory disease characterised by airways widening and recurrent infections, resulting in episodes of chronic cough, sputum expectoration, and dyspnoea. This leads to deterioration in daily function, repeated hospital admissions and poor quality of life. The prevalence and mortality related to bronchiectasis is increasing worldwide with growing economic burden on healthcare systems. Physiotherapy for bronchiectasis aims to decrease accumulation of sputum, dyspnoea, and improve exercise capacity and daily function. A robust evidence base to support physiotherapy in bronchiectasis is currently lacking. This is partly because of inconsistency and poor reporting of outcomes in available studies. A core outcome set is the minimum acceptable group of outcomes that should be used in clinical trials for a specific condition. This decreases research waste by improving consistency and reporting of key outcomes and facilitates the synthesis of study outcomes in systematic reviews and guidelines. The aim of the study is therefore to develop a core outcome set and outcome measurement set for physiotherapy research in adults with bronchiectasis. This will ensure outcomes important to key stakeholders are consistently used and reported in future research. METHODS AND ANALYSIS: This project will use the COMET Initiative and COSMIN guidelines of core outcome set development and will include three phases. In the first phase, a comprehensive list of outcomes will be developed using systematic review of reported outcomes and qualitative interviews with patients and physiotherapists. Then consensus on key outcomes will be established in phase two using a Delphi survey and a consensus meeting. Finally, in phase three, we will identify appropriate instruments to measure the core outcomes by evaluating the psychometric properties of available instruments and a stakeholders' meeting to establish consensus. ETHICS: The study was reviewed and has received ethical approval from the health-related Research Ethics Committee- Edge Hill University (ETH2021-0217). REGISTRATION: This study is registered with the COMET database. https://www.comet-initiative.org/Studies/Details/1931. The full systematic review protocol is registered in PROSPERO under the number CRD42021266247.

Sample size estimation using a latent variable model for mixed outcome co-primary, multiple primary and composite endpoints.

Mixed outcome endpoints that combine multiple continuous and discrete components are often employed as primary outcome measures in clinical trials. These may be in the form of co-primary endpoints, which conclude effectiveness overall if an effect occurs in all of the components, or multiple primary endpoints, which require an effect in at least one of the components. Alternatively, they may be combined to form composite endpoints, which reduce the outcomes to a one-dimensional endpoint. There are many advantages to joint modeling the individual outcomes, however in order to do this in practice we require techniques for sample size estimation. In this article we show how the latent variable model can be used to estimate the joint endpoints and propose hypotheses, power calculations and sample size estimation methods for each. We illustrate the techniques using a numerical example based on a four-dimensional endpoint and find that the sample size required for the co-primary endpoint is larger than that required for the individual endpoint with the smallest effect size. Conversely, the sample size required in the multiple primary case is similar to that needed for the outcome with the largest effect size. We show that the empirical power is achieved for each endpoint and that the FWER can be sufficiently controlled using a Bonferroni correction if the correlations between endpoints are less than 0.5. Otherwise, less conservative adjustments may be needed. We further illustrate empirically the efficiency gains that may be achieved in the composite endpoint setting.