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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.)

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.)

Objectives: We aim to use real-world data in evidence synthesis to optimize an evidence base for the effectiveness of biologic therapies in rheumatoid arthritis to allow for evidence on first-line therapies to inform second-line effectiveness estimates., Study Design and Setting: We use data from the British Society for Rheumatology Biologics Register for Rheumatoid Arthritis to supplement randomized controlled trials evidence obtained from the literature, by emulating target trials of treatment sequences to estimate treatment effects in each line of therapy. Treatment effects estimates from the target trials inform a bivariate network meta-analysis (NMA) of first-line and second-line treatments., Results: Summary data were obtained from 21 trials of biologic therapies including two for second-line treatment and results from six emulated target trials of both treatment lines. Bivariate NMA resulted in a decrease in uncertainty around the effectiveness estimates of the second-line therapies, when compared to the results of univariate NMA, and allowed for predictions of treatment effects not evaluated in second-line randomized controlled trials., Conclusion: Bivariate NMA provides effectiveness estimates for all treatments in first and second line, including predicted effects in second line where these estimates did not exist in the data. This novel methodology may have further applications; for example, for bridging networks of trials in children and adults., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objectives: To present graphical tools for reporting network meta-analysis (NMA) results aiming to increase the accessibility, transparency, interpretability, and acceptability of NMA analyses., Study Design and Settings: The key components of NMA results were identified based on recommendations by agencies such as the National Institute for Health and Care Excellence (United Kingdom). Three novel graphs were designed to amalgamate the identified components using familiar graphical tools such as the bar, line, or pie charts and adhering to good graphical design principles., Results: Three key components for presentation of NMA results were identified, namely relative effects and their uncertainty, probability of an intervention being best, and between-study heterogeneity. Two of the three graphs developed present results (for each pairwise comparison of interventions in the network) obtained from both NMA and standard pairwise meta-analysis for easy comparison. They also include options to display the probability best, ranking statistics, heterogeneity, and prediction intervals. The third graph presents rankings of interventions in terms of their effectiveness to enable clinicians to easily identify "top-ranking" interventions., Conclusions: The graphical tools presented can display results tailored to the research question of interest, and targeted at a whole spectrum of users from the technical analyst to the nontechnical clinician., (Copyright © 2014 Elsevier Inc. All rights reserved.)