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Author(s): T. M. Fromhold (corresponding author) [1]; A. Patanè [1]; S. Bujkiewicz [1]; P. B. Wilkinson [1]; D. Fowler [1]; D. Sherwood [1]; S. P. Stapleton [1]; A. A. Krokhin [...]

Background: This systematic review aimed to investigate the current state of risk prediction for abdominal aortic aneurysm in the literature, identifying and comparing published models and describing their performance and applicability to a population-based targeted screening strategy., Methods: Electronic databases MEDLINE (via Ovid), Embase (via Ovid), MedRxiv, Web of Science, and the Cochrane Library were searched for papers reporting or validating risk prediction models for abdominal aortic aneurysm. Studies were included only if they were developed on a cohort or study group derived from the general population and used multiple variables with at least one modifiable risk factor. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool. A synthesis and comparison of the identified models was undertaken., Results: The search identified 4813 articles. After full-text review, 37 prediction models were identified, of which 4 were unique predictive models that were reported in full. Applicability was poor when considering targeted screening strategies using electronic health record-based populations. Common risk factors used for the predictive models were explored across all 37 models; the most common risk factors in predictive models for abdominal aortic aneurysm were: age, sex, biometrics (such as height, weight, or BMI), smoking, hypertension, hypercholesterolaemia, and history of heart disease. Few models had undergone standardized model development, adequate external validation, or impact evaluation., Conclusion: This study identified four risk models that can be replicated and used to predict abdominal aortic aneurysm with acceptable levels of discrimination. None of the models have been validated externally., (© The Author(s) 2024. Published by Oxford University Press on behalf of BJS Foundation Ltd.)

Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from the UK Medical Research Council for the submitted work. SB is a member of the NICE Decision Support Unit and NICE Guidelines Technical Support Unit; has served as a paid consultant, providing methodological advice, to NICE, Roche, IQVIA, and RTI Health Solutions; received payments for educational events from Roche; and has received research funding from European Federation of Pharmaceutical Industries and Associations and Johnson & Johnson. MOu works for and has shares in AstraZeneca. JSR is a deputy editor at JAMA, was formerly an associate editor at The BMJ, and is co-founder (unpaid) of medRxiv; has received research support through Yale University from Johnson & Johnson to develop methods of clinical trial data sharing, from the Medical Device Innovation Consortium as part of the National Evaluation System for Health Technology, from the Food and Drug Administration for the Yale-Mayo Clinic Center for Excellence in Regulatory Science and Innovation programme (U01FD005938), from the Agency for Healthcare Research and Quality (R01HS022882), from the National Heart, Lung and Blood Institute of the National Institutes of Health (R01HS025164, R01HL144644), and from Arnold Ventures; was an expert witness at the request of Relator’s attorneys, the Greene Law Firm, in a qui tam suit alleging violations of the False Claims Act and Anti-Kickback Statute against Biogen that was settled in September 2022. NJB has received consulting fees from Nobias Therapeutics. AA and YR are associate editors at BMC Trials. OC is an associate editor for Value in Health and has received consulting fees from MSD and Janssen. RC is a founding member of the OMERACT Technical Advisory Group, which might be perceived as a possible conflict of interest. RH has shares in Johnson & Johnson. JHP has been a consultant for AdaptivePhage, Arrevus, Atheln, BavariaNordic, Cellularity, Eicos, Evofem, Eyecheck, Gilead, GSK, Mustang, OPKO, Otsuka, Resolve, Romark, SpineBioPPharma, and UTIlity,Vir. GSC is a statistics editor for The BMJ, and director of the UK EQUATOR Centre. CJW has undertaken consultancy for AB Science, for which his institution has received a fee. DD is an associate editor of Value in Health.

Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from the UK Medical Research Council for the submitted work. SB is a member of the NICE Decision Support Unit and NICE Guidelines Technical Support Unit; has served as a paid consultant, providing methodological advice, to NICE, Roche, IQVIA, and RTI Health Solutions; received payments for educational events from Roche; and has received research funding from European Federation of Pharmaceutical Industries and Associations and Johnson & Johnson. MOu works for and has shares in AstraZeneca. JSR is a deputy editor at JAMA, was formerly an associate editor at The BMJ, and is co-founder (unpaid) of medRxiv; has received research support through Yale University from Johnson & Johnson to develop methods of clinical trial data sharing, from the Medical Device Innovation Consortium as part of the National Evaluation System for Health Technology, from the Food and Drug Administration for the Yale-Mayo Clinic Center for Excellence in Regulatory Science and Innovation programme (U01FD005938), from the Agency for Healthcare Research and Quality (R01HS022882), from the National Heart, Lung and Blood Institute of the National Institutes of Health (R01HS025164, R01HL144644), and from Arnold Ventures; was an expert witness at the request of Relator’s attorneys, the Greene Law Firm, in a qui tam suit alleging violations of the False Claims Act and Anti-Kickback Statute against Biogen that was settled in September 2022. NJB has received consulting fees from Nobias Therapeutics. AA and YR are associate editors at BMC Trials. OC is an associate editor for Value in Health and has received consulting fees from MSD and Janssen. RC is a founding member of the OMERACT Technical Advisory Group, which might be perceived as a possible conflict of interest. RH has shares in Johnson & Johnson. JHP has been a consultant for AdaptivePhage, Arrevus, Atheln, BavariaNordic, Cellularity, Eicos, Evofem, Eyecheck, Gilead, GSK, Mustang, OPKO, Otsuka, Resolve, Romark, SpineBioPPharma, and UTIlity,Vir. GSC is a statistics editor for The BMJ, and director of the UK EQUATOR Centre. CJW has undertaken consultancy for AB Science, for which his institution has received a fee. DD is an associate editor of Value in Health.

During drug development, evidence can emerge to suggest a treatment is more effective in a specific patient subgroup. Whilst early trials may be conducted in biomarker-mixed populations, later trials are more likely to enroll biomarker-positive patients alone, thus leading to trials of the same treatment investigated in different populations. When conducting a meta-analysis, a conservative approach would be to combine only trials conducted in the biomarker-positive subgroup. However, this discards potentially useful information on treatment effects in the biomarker-positive subgroup concealed within observed treatment effects in biomarker-mixed populations. We extend standard random-effects meta-analysis to combine treatment effects obtained from trials with different populations to estimate pooled treatment effects in a biomarker subgroup of interest. The model assumes a systematic difference in treatment effects between biomarker-positive and biomarker-negative subgroups, which is estimated from trials which report either or both treatment effects. The systematic difference and proportion of biomarker-negative patients in biomarker-mixed studies are used to interpolate treatment effects in the biomarker-positive subgroup from observed treatment effects in the biomarker-mixed population. The developed methods are applied to an illustrative example in metastatic colorectal cancer and evaluated in a simulation study. In the example, the developed method improved precision of the pooled treatment effect estimate compared with standard random-effects meta-analysis of trials investigating only biomarker-positive patients. The simulation study confirmed that when the systematic difference in treatment effects between biomarker subgroups is not very large, the developed method can improve precision of estimation of pooled treatment effects while maintaining low bias., (© 2024 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.)

Simulated treatment comparison (STC) is an established method for performing population adjustment for the indirect comparison of two treatments, where individual patient data (IPD) are available for one trial but only aggregate level information is available for the other. The most commonly used method is what we call 'standard STC'. Here we fit an outcome model using data from the trial with IPD, and then substitute mean covariate values from the trial where only aggregate level data are available, to predict what the first of these trial's outcomes would have been if its population had been the same as the second. However, this type of STC methodology does not involve simulation and can result in bias when the link function used in the outcome model is non-linear. An alternative approach is to use the fitted outcome model to simulate patient profiles in the trial for which IPD are available, but in the other trial's population. This stochastic alternative presents additional challenges. We examine the history of STC and propose two new simulation-based methods that resolve many of the difficulties associated with the current stochastic approach. A virtue of the simulation-based STC methods is that the marginal estimands are then clearly targeted. We illustrate all methods using a numerical example and explore their use in a simulation study., (© 2023 John Wiley & Sons Ltd.)

Objectives: We aimed to develop a network meta-analytic model for the evaluation of treatment effectiveness within predictive biomarker subgroups, by combining evidence from individual participant data (IPD) from digital sources (in the absence of randomized controlled trials) and aggregate data (AD)., Study Design and Setting: A Bayesian framework was developed for modeling time-to-event data to evaluate predictive biomarkers. IPD were sourced from electronic health records, using a target trial emulation approach, or digitized Kaplan-Meier curves. The model is illustrated using two examples: breast cancer with a hormone receptor biomarker, and metastatic colorectal cancer with the Kirsten Rat Sarcoma (KRAS) biomarker., Results: The model allowed for the estimation of treatment effects in two subgroups of patients defined by their biomarker status. Effectiveness of taxanes did not differ in hormone receptor positive and negative breast cancer patients. Epidermal growth factor receptor inhibitors were more effective than chemotherapy in KRAS wild type colorectal cancer patients but not in patients with KRAS mutant status. Use of IPD reduced uncertainty of the subgroup-specific treatment effect estimates by up to 49%., Conclusion: Utilization of IPD allowed for more detailed evaluation of predictive biomarkers and cancer therapies and improved precision of the estimates compared to use of AD alone., Competing Interests: Declaration of competing interest S.B. is a member of the NICE Decision Support Unit (DSU). She has served as a paid consultant, providing methodological advice, to NICE, Roche, RTI Health Solutions and IQVIA, has received payments for educational events from Roche and has received research funding from European Federation of Pharmaceutical Industries & Associations (EEPIA) and Johnson & Johnson. R.K.O. is a member of the National Institute for Health and Care Excellence (NICE) Technology Appraisal Committee, member of the NICE Decision Support Unit (DSU), and associate member of the NICE Technical Support Unit (TSU). She has served as a paid consultant providing unrelated methodological advice to AstraZeneca, Cogentia Healthcare Ltd, Daiichi Sankyo, NICE, Norwegian Institute of Public Health, Roche, and Vifor Pharma. She reports teaching fees from the Association of British Pharmaceutical Industry (ABPI) and the University of Bristol. K.R.A. is a member of the National Institute for Health and Care Excellence (NICE) Diagnostics Advisory Committee, member of the NICE Decision Support Unit (DSU), and the NICE Technical Support Unit (TSU). He has served as a paid consultant, providing unrelated methodological advice to Abbvie, AstraZeneca, Bayer, Bristol-Meyers Squibb, Medtronic, NICE/DHSC, Novartis, Pfizer, and Roche, and has received research funding from Bayer, Association of the British Pharmaceutical Industry (ABPI), European Federation of Pharmaceutical Industries & Associations (EFPIA), Pfizer, Sanofi, and Swiss Precision Diagnostics. He is a Partner and Director of Visible Analytics Limited, a Health Technology Assessment (HTA) consultancy company. P.T. is a member of the National Institute for Health and Care Excellence (NICE) Technology Appraisal Committee, and a member of the NICE Decision Support Unit (DSU). He reports teaching fees from the Association of British Pharmaceutical Industry (ABPI)., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Interventional trials that evaluate treatment effects using surrogate endpoints have become increasingly common. This paper describes four linked empirical studies and the development of a framework for defining, interpreting and reporting surrogate endpoints in trials., Methods: As part of developing the CONSORT (Consolidated Standards of Reporting Trials) and SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) extensions for randomised trials reporting surrogate endpoints, we undertook a scoping review, e-Delphi study, consensus meeting, and a web survey to examine current definitions and stakeholder (including clinicians, trial investigators, patients and public partners, journal editors, and health technology experts) interpretations of surrogate endpoints as primary outcome measures in trials., Findings: Current surrogate endpoint definitional frameworks are inconsistent and unclear. Surrogate endpoints are used in trials as a substitute of the treatment effects of an intervention on the target outcome(s) of ultimate interest, events measuring how patients feel, function, or survive. Traditionally the consideration of surrogate endpoints in trials has focused on biomarkers (e.g., HDL cholesterol, blood pressure, tumour response), especially in the medical product regulatory setting. Nevertheless, the concept of surrogacy in trials is potentially broader. Intermediate outcomes that include a measure of function or symptoms (e.g., angina frequency, exercise tolerance) can also be used as substitute for target outcomes (e.g., all-cause mortality)-thereby acting as surrogate endpoints. However, we found a lack of consensus among stakeholders on accepting and interpreting intermediate outcomes in trials as surrogate endpoints or target outcomes. In our assessment, patients and health technology assessment experts appeared more likely to consider intermediate outcomes to be surrogate endpoints than clinicians and regulators., Interpretation: There is an urgent need for better understanding and reporting on the use of surrogate endpoints, especially in the setting of interventional trials. We provide a framework for the definition of surrogate endpoints (biomarkers and intermediate outcomes) and target outcomes in trials to improve future reporting and aid stakeholders' interpretation and use of trial surrogate endpoint evidence., Funding: SPIRIT-SURROGATE/CONSORT-SURROGATE project is Medical Research Council Better Research Better Health (MR/V038400/1) funded., Competing Interests: Sylwia Bujkiewicz is a member of the NICE Decision Support Unit (DSU) and NICE Guidelines Technical Support Unit (TSU), has served as a paid consultant, providing methodological advice, to NICE, Roche, IQVIA and RTI Health Solutions, received payments for educational events from NICE and Roche and has received research funding from European Federation of Pharmaceutical Industries & Associations (EEPIA) and Johnson & Johnson. Mario Ouwens works for and has shares in AstraZeneca. Joseph Ross is an Associate Editor at BMJ and co-founder (unpaid) of medRxiv; research support through Yale University from Johnson and Johnson to develop methods of clinical trial data sharing, from the Medical Device Innovation Consortium as part of the National Evaluation System for Health Technology (NEST), from the Food and Drug Administration for the Yale-Mayo Clinic Center for Excellence in Regulatory Science and Innovation (CERSI) program (U01FD005938), from the Agency for Healthcare Research and Quality (R01HS022882), from the National Heart, Lung and Blood Institute of the National Institutes of Health (NIH) (R01HS025164, R01HL144644), and from the Laura and John Arnold Foundation to establish the Good Pharma Scorecard at Bioethics International; expert witness at the request of Relator's attorneys, the Greene Law Firm, in a qui tam suit alleging violations of the False Claims Act and Anti-Kickback Statute against Biogen Inc. Nancy Butcher has received consulting fees from Nobias Therapeutics, Inc. Alain Amstutz and Yousef Rezaei are Associate Editors at BMC Trials. Robin Christensen is a founding member of the OMERACT Technical Advisory Group which might be perceived as a possible conflict of interest. Ray Harris has shares in Johnson and Johnson. John Powers has been a consultant for Adaptive Phage, Arrevus, Atheln, Bavaria Nordic, Cellularity, Eicos, Evofem, Eyecheck, Gilead, GSK, Mustang, OPKO, Otsuka, Resolve, Romark, SpineBioPPharma, UTIlity, Vir. Achilles Thoma received royalties from Springer Publishing for his book “Evidence Based Surgery: A Guide to Understanding and Interpreting the Surgical Literature”, 2019., (© 2023 The Author(s).)

Objective: To synthesize the current literature on the use of surrogate end points, including definitions, acceptability, and limitations of surrogate end points and guidance for their design/reporting, into trial reporting items., Study Design and Setting: Literature was identified through searching bibliographic databases (until March 1, 2022) and gray literature sources (until May 27, 2022). Data were thematically analyzed into four categories: (1) definitions, (2) acceptability, (3) limitations and challenges, and (4) guidance, and synthesized into reporting guidance items., Results: After screening, 90 documents were included: 79% (n = 71) had data on definitions, 77% (n = 69) on acceptability, 72% (n = 65) on limitations and challenges, and 61% (n = 55) on guidance. Data were synthesized into 17 potential trial reporting items: explicit statements on the use of surrogate end point(s) and justification for their use (items 1-6); methodological considerations, including whether sample size calculations were informed by surrogate validity (items 7-9); reporting of results for composite outcomes containing a surrogate end point (item 10); discussion and interpretation of findings (items 11-14); plans for confirmatory studies, collecting data on the surrogate end point and target outcome, and data sharing (items 15-16); and informing trial participants about using surrogate end points (item 17)., Conclusion: The review identified and synthesized items on the use of surrogate end points in trials; these will inform the development of the Standard Protocol Items: Recommendations for Interventional Trials-SURROGATE and Consolidated Standards of Reporting Trials-SURROGATE extensions., Competing Interests: Declaration of competing interest Sylwia Bujkiewicz is a member of the NICE Decision Support Unit and NICE Guidelines Technical Support Unit. She has served as a paid consultant, providing methodological advice, to NICE, Roche, IQVIA, and RTI Health Solutions, received payments for educational events from Roche, and has received research funding from European Federation of Pharmaceutical Industries and Associations and Johnson & Johnson. Mario Ouwens works for and has shares in AstraZeneca. Joseph Ross is an Associate Editor at BMJ and co-founder (unpaid) of medRxiv; research support through Yale University from Johnson and Johnson to develop methods of clinical trial data sharing, from the Medical Device Innovation Consortium as part of the National Evaluation System for Health Technology, from the Food and Drug Administration for the Yale-Mayo Clinic Center for Excellence in Regulatory Science and Innovation program (U01FD005938); from the Agency for Healthcare Research and Quality (R01HS022882); from the National Heart, Lung, and Blood Institute of the National Institutes of Health (NIH) (R01HS025164, R01HL144644); and from the Laura and John Arnold Foundation to establish the Good Pharma Scorecard at Bioethics International; expert witness at the request of Relator's attorneys, the Greene Law Firm, in a qui tam suit alleging violations of the False Claims Act and Anti-Kickback Statute against Biogen Inc. Nancy Butcher has received consulting fees from Nobias Therapeutics, Inc., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objective: Traditionally, validation of surrogate endpoints has been carried out using randomized controlled trial (RCT) data. However, RCT data may be too limited to validate surrogate endpoints. In this article, we sought to improve the validation of surrogate endpoints with the inclusion of real-world evidence (RWE)., Methods: We use data from comparative RWE (cRWE) and single-arm RWE (sRWE) to supplement RCT evidence for the evaluation of progression-free survival (PFS) as a surrogate endpoint to overall survival (OS) in metastatic colorectal cancer (mCRC). Treatment effect estimates from RCTs, cRWE, and matched sRWE, comparing antiangiogenic treatments with chemotherapy, were used to inform surrogacy patterns and predictions of the treatment effect on OS from the treatment effect on PFS., Results: Seven RCTs, 4 cRWE studies, and 2 matched sRWE studies were identified. The addition of RWE to RCTs reduced the uncertainty around the estimates of the parameters for the surrogate relationship. The addition of RWE to RCTs also improved the accuracy and precision of predictions of the treatment effect on OS obtained using data on the observed effect on PFS., Conclusion: The addition of RWE to RCT data improved the precision of the parameters describing the surrogate relationship between treatment effects on PFS and OS and the predicted clinical benefit of antiangiogenic therapies in mCRC., Highlights: Regulatory agencies increasingly rely on surrogate endpoints when making licensing decisions, and for the decisions to be robust, surrogate endpoints need to be validated. In the era of precision medicine, when surrogacy patterns may depend on the drug's mechanism of action and trials of targeted therapies may be small, data from randomized controlled trials may be limited.Real-world evidence (RWE) is increasingly used at different stages of the drug development process. When used to enhance the evidence base for surrogate endpoint evaluation, RWE can improve inferences about the strength of surrogate relationships and the precision of predicted treatment effect on the final clinical outcome based on the observed effect on the surrogate endpoint in a new trial.Careful selection of RWE is needed to reduce risk of bias.