Bivariate network meta-analysis for surrogate endpoint evaluation

Challenges and Opportunities in Establishing Appropriate Intermediate Endpoints Reflecting Patient Benefit: A Roadmap for Research and Clinical Application in Nonmetastatic Prostate Cancer

Defining meaningful endpoints for research of early-stage high-risk prostate cancer is challenging, with established measures such as overall survival and metastasis-free survival facing limitations related to feasibility and adequate reflection of patient relevance. Developing endpoints must cater to diverse perspectives across scientific, clinical, regulatory, and patient viewpoints. Endpoints such as pathological complete response, no evidence of disease, and prevention of prostate-specific antigen relapse may reflect patient benefit by accounting for diagnostic and treatment burdens.

A Systematic Review of Potential Biomarkers for Bacterial Burden and Treatment Efficacy Assessment in Tuberculosis Platform-Based Clinical Trials

Adaptive platform trials can be more efficient than classic trials for developing new treatments. Moving from culture-based to simpler- or faster-to-measure biomarkers as efficacy surrogates may enhance this advantage. We performed a systematic review of treatment efficacy biomarkers in adults with tuberculosis. Platform trials can span different development phases. We grouped biomarkers as: α, bacterial load estimates used in phase 2a trials; β, early and end-of treatment end points, phase 2b-c trials; γ, posttreatment or trial-level estimates, phase 2c-3 trials. We considered as analysis unit (biomarker entry) each combination of biomarker, predicted outcome, and their respective measurement times or intervals. Performance metrics included: sensitivity, specificity, area under the receiver-operator curve (AUC), and correlation measures, and classified as poor, promising, or good. Eighty-six studies included 22 864 participants. From 1356 biomarker entries, 318 were reported with the performance metrics of interest, with 103 promising and 41 good predictors. Group results were: α, mycobacterial RNA and lipoarabinomannan (LAM) in sputum, and host metabolites in urine; β, mycobacterial RNA and host transcriptomic or cytokine signatures for early treatment response; and γ, host transcriptomics for recurrence. A combination of biomarkers from different categories could help in designing more efficient platform trials. Efforts to develop efficacy surrogates should be better coordinated.

Network meta analysis to predict the efficacy of an approved treatment in a new indication

Drug repurposing refers to the process of discovering new therapeutic uses for existing medicines. Compared to traditional drug discovery, drug repurposing is attractive for its speed, cost, and reduced risk of failure. However, existing approaches for drug repurposing involve complex, computationally-intensive analytical methods that are not widely used in practice. Instead, repurposing decisions are often based on subjective judgments from limited empirical evidence. In this article, we develop a novel Bayesian network meta-analysis (NMA) framework that can predict the efficacy of an approved treatment in a new indication and thereby identify candidate treatments for repurposing. We obtain predictions using two main steps: first, we use standard NMA modeling to estimate average relative effects from a network comprised of treatments studied in both indications in addition to one treatment studied in only one indication. Then, we model the correlation between relative effects using various strategies that differ in how they model treatments across indications and within the same drug class. We evaluate the predictive performance of each model using a simulation study and find that the model minimizing root mean squared error of the posterior median for the candidate treatment depends on the amount of available data, the level of correlation between indications, and whether treatment effects differ, on average, by drug class. We conclude by discussing an illustrative example in psoriasis and psoriatic arthritis and find that the candidate treatment has a high probability of success in a future trial.

Comparing Bayesian hierarchical meta-regression methods and evaluating the influence of priors for evaluations of surrogate endpoints on heterogeneous collections of clinical trials

BACKGROUND

Surrogate endpoints, such as those of interest in chronic kidney disease (CKD), are often evaluated using Bayesian meta-regression. Trials used for the analysis can evaluate a variety of interventions for different sub-classifications of disease, which can introduce two additional goals in the analysis. The first is to infer the quality of the surrogate within specific trial subgroups defined by disease or intervention classes. The second is to generate more targeted subgroup-specific predictions of treatment effects on the clinical endpoint.

METHODS

Using real data from a collection of CKD trials and a simulation study, we contrasted surrogate endpoint evaluations under different hierarchical Bayesian approaches. Each approach we considered induces different assumptions regarding the relatedness (exchangeability) of trials within and between subgroups. These include partial-pooling approaches, which allow subgroup-specific meta-regressions and, yet, facilitate data adaptive information sharing across subgroups to potentially improve inferential precision. Because partial-pooling models come with additional parameters relative to a standard approach assuming one meta-regression for the entire set of studies, we performed analyses to understand the impact of the parameterization and priors with the overall goals of comparing precision in estimates of subgroup-specific meta-regression parameters and predictive performance.

RESULTS

In the analyses considered, partial-pooling approaches to surrogate endpoint evaluation improved accuracy of estimation of subgroup-specific meta-regression parameters relative to fitting separate models within subgroups. A random rather than fixed effects approach led to reduced bias in estimation of meta-regression parameters and in prediction in subgroups where the surrogate was strong. Finally, we found that subgroup-specific meta-regression posteriors were robust to use of constrained priors under the partial-pooling approach, and that use of constrained priors could facilitate more precise prediction for clinical effects in trials of a subgroup not available for the initial surrogacy evaluation.

CONCLUSION

Partial-pooling modeling strategies should be considered for surrogate endpoint evaluation on collections of heterogeneous studies. Fitting these models comes with additional complexity related to choosing priors. Constrained priors should be considered when using partial-pooling models when the goal is to predict the treatment effect on the clinical endpoint.

Conduct and reporting of multivariate network meta-analyses: a scoping review

OBJECTIVES

Combining multivariate and network meta-analysis methods simultaneously in a multivariate network meta-analysis (MVNMA) provides the methodological framework to analyze the largest amount of evidence relevant to decision-makers (i.e., from indirect evidence and correlated outcomes). The objectives of this scoping review were to summarize the characteristics of MVNMAs published in the health sciences literature and map the methodological guidance available for MVNMA.

STUDY DESIGN AND SETTING

We searched MEDLINE, Embase, and the Cumulative Index to Nursing and Allied Health Literature from inception to 28 August 2023, along with citations of included studies, for quantitative evidence syntheses that applied MVNMA and articles addressing MVNMA methods. Pairs of reviewers independently screened potentially eligible studies. Collected data included bibliographic, methodological, and analytical characteristics of included studies. We reported results as total numbers, frequencies, and percentages for categorical variables and medians and interquartile ranges for continuous variables that were not normally distributed.

RESULTS

After screening 1,075 titles and abstracts, and 112 full texts, we included 38 unique studies, of which, 10 were quantitative evidence syntheses that applied MVNMA and 28 were articles addressing MVNMA methods. Among the 10 MVNMAs, the first was published in 2013, four used studies identified from already published systematic reviews, and eight addressed pharmacological interventions, which were the most common interventions. They evaluated interventions for metastatic melanoma, colorectal cancer, prostate cancer, oral hygiene, disruptive behavior disorders, rheumatoid arthritis, narcolepsy, type 2 diabetes, and overactive bladder syndrome. Five MVNMAs analyzed two outcomes simultaneously, and four MVNMAs analyzed three outcomes simultaneously. Among the articles addressing MVNMA methods, the first was published in 2007 and the majority provided methodological frameworks for conducting MVNMAs (26/28, 93%). One study proposed criteria to standardize reporting of MVNMAs and two proposed items relevant to the quality assessment of MVNMAs. Study authors used data from 18 different illnesses to provide illustrative examples within their methodological guidance.

CONCLUSIONS

The application of MVNMA in the health sciences literature is uncommon. Many methodological frameworks are published; however, standardization and specific criteria to guide reporting and quality assessment are lacking. This overview of the current landscape may help inform future conduct of MVNMAs and research on MVNMA methods.

Handling missing within-study correlations in the evaluation of surrogate endpoints

Rigorous evaluation of surrogate endpoints is performed in a trial-level analysis in which the strength of the association between treatment effects on the clinical and surrogate endpoints is quantified across a collection of previously conducted trials. To reduce bias in measures of the performance of the surrogate, the statistical model must account for the sampling error in each trial's estimated treatment effects and their potential correlation. Unfortunately, these within-study correlations can be difficult to obtain, especially for meta-analysis of published trial results where individual patient data is not available. As such, these terms are frequently partially or completely missing in the analysis. We show that improper handling of these missing terms can meaningfully alter the perceived quality of the surrogate and we introduce novel strategies to handle the missingness.

Scoping and targeted reviews to support development of SPIRIT and CONSORT extensions for randomised controlled trials with surrogate primary endpoints: protocol

INTRODUCTION

Using a surrogate endpoint as a substitute for a primary patient-relevant outcome enables randomised controlled trials (RCTs) to be conducted more efficiently, that is, with shorter time, smaller sample size and lower cost. However, there is currently no consensus-driven guideline for the reporting of RCTs using a surrogate endpoint as a primary outcome; therefore, we seek to develop SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) and CONSORT (Consolidated Standards of Reporting Trials) extensions to improve the design and reporting of these trials. As an initial step, scoping and targeted reviews will identify potential items for inclusion in the extensions and participants to contribute to a Delphi consensus process.

METHODS AND ANALYSIS

The scoping review will search and include literature reporting on the current understanding, limitations and guidance on using surrogate endpoints in trials. Relevant literature will be identified through: (1) bibliographic databases; (2) grey literature; (3) handsearching of reference lists and (4) solicitation from experts. Data from eligible records will be thematically analysed into potential items for inclusion in extensions. The targeted review will search for RCT reports and protocols published from 2017 to 2021 in six high impact general medical journals. Trial corresponding author contacts will be listed as potential participants for the Delphi exercise.

ETHICS AND DISSEMINATION

Ethical approval is not required. The reviews will support the development of SPIRIT and CONSORT extensions for reporting surrogate primary endpoints (surrogate endpoint as the primary outcome). The findings will be published in open-access publications.This review has been prospectively registered in the OSF Registration DOI: 10.17605/OSF.IO/WP3QH.

Protocol for the development of SPIRIT and CONSORT extensions for randomised controlled trials with surrogate primary endpoints: SPIRIT-SURROGATE and CONSORT-SURROGATE

INTRODUCTION

Randomised controlled trials (RCTs) may use surrogate endpoints as substitutes and predictors of patient-relevant/participant-relevant final outcomes (eg, survival, health-related quality of life). Translation of effects measured on a surrogate endpoint into health benefits for patients/participants is dependent on the validity of the surrogate; hence, more accurate and transparent reporting on surrogate endpoints is needed to limit misleading interpretation of trial findings. However, there is currently no explicit guidance for the reporting of such trials. Therefore, we aim to develop extensions to the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) and CONSORT (Consolidated Standards of Reporting Trials) reporting guidelines to improve the design and completeness of reporting of RCTs and their protocols using a surrogate endpoint as a primary outcome.

METHODS AND ANALYSIS

The project will have four phases: phase 1 (literature reviews) to identify candidate reporting items to be rated in a Delphi study; phase 2 (Delphi study) to rate the importance of items identified in phase 1 and receive suggestions for additional items; phase 3 (consensus meeting) to agree on final set of items for inclusion in the extensions and phase 4 (knowledge translation) to engage stakeholders and disseminate the project outputs through various strategies including peer-reviewed publications. Patient and public involvement will be embedded into all project phases.

ETHICS AND DISSEMINATION

The study has received ethical approval from the University of Glasgow College of Medical, Veterinary and Life Sciences Ethics Committee (project no: 200210051). The findings will be published in open-access peer-reviewed publications and presented in conferences, meetings and relevant forums.

Bridging disconnected networks of first and second lines of biologic therapies in rheumatoid arthritis with registry data: bayesian evidence synthesis with target trial emulation

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.

Development of a framework and decision tool for the evaluation of health technologies based on surrogate endpoint evidence

In the drive toward faster patient access to treatments, health technology assessment (HTA) agencies and payers are increasingly faced with reliance on evidence based on surrogate endpoints, increasing decision uncertainty. Despite the development of a small number of evaluation frameworks, there remains no consensus on the detailed methodology for handling surrogate endpoints in HTA practice. This research overviews the methods and findings of four empirical studies undertaken as part of COMED (Pushing the Boundaries of Cost and Outcome Analysis of Medical Technologies) program work package 2 with the aim of analyzing international HTA practice of the handling and considerations around the use of surrogate endpoint evidence. We have synthesized the findings of these empirical studies, in context of wider contemporary body of methodological and policy-related literature on surrogate endpoints, to develop a web-based decision tool to support HTA agencies and payers when faced with surrogate endpoint evidence. Our decision tool is intended for use by HTA agencies and their decision-making committees together with the wider community of HTA stakeholders (including clinicians, patient groups, and healthcare manufacturers). Having developed this tool, we will monitor its use and we welcome feedback on its utility.

Correlations between objective response rate and survival-based endpoints in first-line advanced non-small cell lung Cancer: A systematic review and meta-analysis

INTRODUCTION

The study objective was to estimate the relationship between objective response and survival-based endpoints by drug class, in first-line advanced non-small cell lung cancer (aNSCLC).

MATERIALS AND METHODS

A systematic literature review identified randomized controlled trials (RCTs) of first-line aNSCLC therapies reporting overall survival (OS), progression-free survival (PFS), and/or objective response rate (ORR). Trial-level and arm-level linear regression models were fit, accounting for inclusion of immunotherapy (IO)-based or chemotherapy-only RCT arms. Weighted least squares-based R² were calculated along with 95% confidence intervals (CIs). For the main trial-level analysis of OS vs. ORR, the surrogate threshold effect was estimated. Exploratory analyses involved further stratification by: IO monotherapy vs. chemotherapy, dual-IO therapy vs. chemotherapy, and IO + chemotherapy vs. chemotherapy.

RESULTS

From 17,040 records, 57 RCTs were included. In the main analysis, trial-level associations between OS and ORR were statistically significant in both the IO-based and chemotherapy-only strata, with R² estimates of 0.54 (95% CI: 0.26-0.81) and 0.34 (0.05-0.63), respectively. OS gains associated with a given ORR benefit were statistically significantly larger within IO vs. chemotherapy comparisons compared to chemotherapy vs. chemotherapy comparisons (p < 0.001). Exploratory analysis suggested a trend by IO type: for a given change in ORR, 'pure' IO (IO monotherapy and dual-IO) vs. chemotherapy RCTs tended to have a larger OS benefit than IO + chemotherapy vs. chemotherapy RCTs. For ORR vs. PFS, trial-level correlations were strong in the IO-based vs. chemotherapy (R² = 0.84; 0.72-0.95), and chemotherapy vs. chemotherapy strata (R² = 0.69; 0.49-0.88). For OS vs. PFS, correlations were moderate in both strata (R² = 0.49; 0.20-0.78 and R² = 0.49; 0.23-0.76).

CONCLUSION

The larger OS benefit per unit of ORR benefit in IO-based RCTs compared to chemotherapy-only RCTs provides an important addition to the established knowledge regarding the durability and depth of response in IO-based treatments.

Correlation Between Early Time-to-Event Outcomes and Overall Survival in Patients With Locally Advanced Head and Neck Squamous Cell Carcinoma Receiving Definitive Chemoradiation Therapy: Systematic Review and Meta-Analysis

Background

Overall survival (OS) is the most patient-relevant outcome in oncology; however, in early cancers, large sample sizes and extended follow-up durations are needed to detect statistically significant differences in OS between interventions. Use of early time-to-event outcomes as surrogates for OS can help facilitate faster approval of cancer therapies. In locally advanced head and neck squamous cell carcinoma (LA-HNSCC), event-free survival (EFS) was previously evaluated as a surrogate outcome (Michiels 2009) and demonstrated a strong correlation with OS. The current study aimed to further assess the correlation between EFS and OS in LA-HNSCC using an updated systematic literature review (SLR) focusing on patients receiving definitive chemoradiation therapy (CRT).

Methods

An SLR was conducted on May 27, 2021 to identify randomized controlled trials assessing radiotherapy alone or CRT in the target population. Studies assessing CRT and reporting hazard ratios (HRs) or Kaplan-Meier data for OS and EFS were eligible for the analysis. CRT included any systemic treatments administered concurrently or sequentially with radiation therapy. Trial-level EFS/OS correlations were assessed using regression models, and the relationship strength was measured with Pearson correlation coefficient (R). Correlations were assessed across all CRT trials and in trial subsets assessing concurrent CRT, sequential CRT, RT+cisplatin, targeted therapies and intensity-modulated RT. Subgroup analysis was conducted among trials with similar EFS definitions (i.e. EFS including disease progression and/or death as events) and longer length of follow-up (i.e.≥ 5 years).

Results

The SLR identified 149 trials of which 31 were included in the analysis. A strong correlation between EFS and OS was observed in the overall analysis of all CRT trials (R=0.85, 95% confidence interval: 0.72-0.93). Similar results were obtained in the sensitivity analyses of trials assessing concurrent CRT (R=0.88), sequential CRT (R=0.83), RT+cisplatin (R=0.82), targeted therapies (R=0.83) and intensity-modulated RT (R=0.86), as well as in trials with similar EFS definitions (R=0.87), with longer follow-up (R=0.81).

Conclusion

EFS was strongly correlated with OS in this trial-level analysis. Future research using individual patient-level data can further investigate if EFS could be considered a suitable early clinical endpoint for evaluation of CRT regimens in LA-HNSCC patients receiving definitive CRT.

Modelling approaches for histology-independent cancer drugs to inform NICE appraisals: a systematic review and decision-framework

BACKGROUND

The first histology-independent marketing authorisation in Europe was granted in 2019. This was the first time that a cancer treatment was approved based on a common biomarker rather than the location in the body at which the tumour originated. This research aims to explore the implications for National Institute for Health and Care Excellence appraisals.

METHODS

Targeted reviews were undertaken to determine the type of evidence that is likely to be available at the point of marketing authorisation and the analyses required to support National Institute for Health and Care Excellence appraisals. Several challenges were identified concerning the design and conduct of trials for histology-independent products, the greater levels of heterogeneity within the licensed population and the use of surrogate end points. We identified approaches to address these challenges by reviewing key statistical literature that focuses on the design and analysis of histology-independent trials and by undertaking a systematic review to evaluate the use of response end points as surrogate outcomes for survival end points. We developed a decision framework to help to inform approval and research policies for histology-independent products. The framework explored the uncertainties and risks associated with different approval policies, including the role of further data collection, pricing schemes and stratified decision-making.

RESULTS

We found that the potential for heterogeneity in treatment effects, across tumour types or other characteristics, is likely to be a central issue for National Institute for Health and Care Excellence appraisals. Bayesian hierarchical methods may serve as a useful vehicle to assess the level of heterogeneity across tumours and to estimate the pooled treatment effects for each tumour, which can inform whether or not the assumption of homogeneity is reasonable. Our review suggests that response end points may not be reliable surrogates for survival end points. However, a surrogate-based modelling approach, which captures all relevant uncertainty, may be preferable to the use of immature survival data. Several additional sources of heterogeneity were identified as presenting potential challenges to National Institute for Health and Care Excellence appraisal, including the cost of testing, baseline risk, quality of life and routine management costs. We concluded that a range of alternative approaches will be required to address different sources of heterogeneity to support National Institute for Health and Care Excellence appraisals. An exemplar case study was developed to illustrate the nature of the assessments that may be required.

CONCLUSIONS

Adequately designed and analysed basket studies that assess the homogeneity of outcomes and allow borrowing of information across baskets, where appropriate, are recommended. Where there is evidence of heterogeneity in treatment effects and estimates of cost-effectiveness, consideration should be given to optimised recommendations. Routine presentation of the scale of the consequences of heterogeneity and decision uncertainty may provide an important additional approach to the assessments specified in the current National Institute for Health and Care Excellence methods guide.

FURTHER RESEARCH

Further exploration of Bayesian hierarchical methods could help to inform decision-makers on whether or not there is sufficient evidence of homogeneity to support pooled analyses. Further research is also required to determine the appropriate basis for apportioning genomic testing costs where there are multiple targets and to address the challenges of uncontrolled Phase II studies, including the role and use of surrogate end points.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Evidence Synthesis programme and will be published in full in Health Technology Assessment; Vol. 25, No. 76. See the NIHR Journals Library website for further project information.

177Lu-PSMA Radioligand Therapy Is Favorable as Third-Line Treatment of Patients with Metastatic Castration-Resistant Prostate Cancer. A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials

In this systematic review and network meta-analysis (NMA), we aimed to assess the benefits and harms of third-line (L3) treatments in randomized controlled trials (RCTs) of patients with metastatic castration-resistant prostate cancer (mCRPC). Two reviewers searched for publications from 1 January 2006 to 30 June 2021. The review analyzed seven RCTs that included 3958 patients and eight treatments. Treatment with prostate-specific membrane antigen (PSMA)-based radioligand therapy (PRLT) resulted in a 1.3-times-higher rate of median PSA decline ≥50% than treatment with abiraterone, enzalutamide, mitoxantrone, or cabazitaxel (p = 0.00001). The likelihood was 97.6% for PRLT to bring about the best PSA response, out of the examined treatments. PRLT resulted in a 1.1-times-higher six-month rate of median radiographic progression-free survival. Treatment with PRLT in the VISION trial resulted in 1.05-times-higher twelve-month median overall survival than L3 treatment with cabazitaxel in other RCTs. PRLT more often resulted in severe thrombocytopenia and less often in severe leukopenia than did cabazitaxel. In conclusion, for patients with mCRPC, L3 treatment with PRLT is highly effective and safe.

HTA methodology and value frameworks for evaluation and policy making for cell and gene therapies

This last decade has been marked by significant advances in the development of cell and gene (C&G) therapies, such as gene targeting or stem cell-based therapies. C&G therapies offer transformative benefits to patients but present a challenge to current health technology decision-making systems because they are typically reviewed when clinical efficacy data are very limited and when there is uncertainty about the long-term durability of outcomes. These challenges are not unique to C&G therapies, but they face more of these barriers, reflecting the need for adapting existing value assessment frameworks. Still, C&G therapies have the potential to be cost-effective even at very high price points. The impact on healthcare budgets will depend on the success rate of pipeline assets and on the extent to which C&G therapies will expand to wider pathologies beyond rare or ultra-rare diseases. Getting pricing and reimbursement models right is important for incentivising research and development investment while not jeopardising the sustainability of healthcare systems. Payers and manufacturers therefore need to acknowledge each other's constraints-limitations in the evidence generation on the manufacturer side, budget considerations on the payer side-and embrace innovative thinking and approaches to ensure timely delivery of therapies to patients. Several experts in health technology assessment and clinical experts have worked together to produce this publication and identify methodological and policy options to improve the assessment of C&G therapies, and make it happen better, faster and sustainably in the coming years.

Exploiting relationships between outcomes in Bayesian multivariate network meta-analysis with an application to relapsing-remitting multiple sclerosis

In multivariate network meta-analysis (NMA), the piecemeal nature of the evidence base means that there may be treatment-outcome combinations for which no data is available. Most existing multivariate evidence synthesis models are either unable to estimate the missing treatment-outcome combinations, or can only do so under particularly strong assumptions, such as perfect between-study correlations between outcomes or constant effect size across outcomes. Many existing implementations are also limited to two treatments or two outcomes, or rely on model specification that is heavily tailored to the dimensions of the dataset. We present a Bayesian multivariate NMA model that estimates the missing treatment-outcome combinations via mappings between the population mean effects, while allowing the study-specific effects to be imperfectly correlated. The method is designed for aggregate-level data (rather than individual patient data) and is likely to be useful when modeling multiple sparsely reported outcomes, or when varying definitions of the same underlying outcome are adopted by different studies. We implement the model via a novel decomposition of the treatment effect variance, which can be specified efficiently for an arbitrary dataset given some basic assumptions regarding the correlation structure. The method is illustrated using data concerning the efficacy and liver-related safety of eight active treatments for relapsing-remitting multiple sclerosis. The results indicate that fingolimod and interferon beta-1b are the most efficacious treatments but also have some of the worst effects on liver safety. Dimethyl fumarate and glatiramer acetate perform reasonably on all of the efficacy and safety outcomes in the model.

Is Disease Intensity a Good Surrogate for Yield Loss or Toxin Contamination? A Case Study with Fusarium Head Blight of Wheat

Sometimes plant pathologists assess disease intensity when they are primarily interested in other response variables, such as yield loss or toxin concentration in harvested products. In these situations, disease intensity potentially could be considered a surrogate of yield or toxin. A surrogate is a variable which can be used instead of the variable of interest in the evaluation of experimental treatments or in making predictions. Surrogates can be measured earlier, more conveniently, or more cheaply than the variable of primary interest, but the reliability or validity of the surrogate must be shown. We demonstrate ways of quantifying two facets of surrogacy by using a protocol originally developed by Buyse and colleagues for medical research. Coefficient-of-determination type statistics can be used to conveniently assess the strength of surrogacy on a unitless scale. As a case study, we evaluated whether field severity of Fusarium head blight (i.e., FHB index) can be used as a surrogate for yield loss and deoxynivalenol (DON) toxin concentration in harvested wheat grain. Bivariate mixed models and corresponding approximations were fitted to data from 82 uniform fungicide trials conducted from 2008 to 2013. Individual-level surrogacy-for predicting the variable of interest (yield or DON) from the surrogate (index) in plots with the same treatment-was very low. Trial-level surrogacy-for predicting the effect of treatment (e.g., mean difference) for the variable of interest based on the effect of the treatment on the surrogate (index)-was moderate for yield, and only low for DON. Challenges in using disease severity as a surrogate for yield and toxin are discussed.

Bayesian hierarchical meta-analytic methods for modeling surrogate relationships that vary across treatment classes using aggregate data

Surrogate endpoints play an important role in drug development when they can be used to measure treatment effect early compared to the final clinical outcome and to predict clinical benefit or harm. Such endpoints are assessed for their predictive value of clinical benefit by investigating the surrogate relationship between treatment effects on the surrogate and final outcomes using meta‐analytic methods. When surrogate relationships vary across treatment classes, such validation may fail due to limited data within each treatment class. In this paper, two alternative Bayesian meta‐analytic methods are introduced which allow for borrowing of information from other treatment classes when exploring the surrogacy in a particular class. The first approach extends a standard model for the evaluation of surrogate endpoints to a hierarchical meta‐analysis model assuming full exchangeability of surrogate relationships across all the treatment classes, thus facilitating borrowing of information across the classes. The second method is able to relax this assumption by allowing for partial exchangeability of surrogate relationships across treatment classes to avoid excessive borrowing of information from distinctly different classes. We carried out a simulation study to assess the proposed methods in nine data scenarios and compared them with subgroup analysis using the standard model within each treatment class. We also applied the methods to an illustrative example in colorectal cancer which led to obtaining the parameters describing the surrogate relationships with higher precision.

Combining tumour response and progression free survival as surrogate endpoints for overall survival in advanced colorectal cancer

BACKGROUND

Progression free survival (PFS) and tumour response (TR) have been investigated as surrogate endpoints for overall survival (OS) in advanced colorectal cancer (aCRC), however their validity has been shown to be suboptimal. In recent years, meta-analytic methods allowing for use of multiple surrogate endpoints jointly have been proposed. Our aim was to assess if PFS and TR used jointly as surrogate endpoints to OS improve their predictive value.

METHODS

Data were obtained from a systematic review of randomised controlled trials investigating effectiveness of pharmacological therapies in aCRC, including systemic chemotherapies, anti-epidermal growth factor receptor therapies and anti-angiogenic agents. Multivariate meta-analysis was used to model the association patterns between treatment effects on the surrogate endpoints (TR, PFS) and the final outcome (OS).

RESULTS

Analysis of 33 trials reporting treatment effects on all three outcomes showed reasonably strong association between treatment effects on PFS and OS, however the association parameters were obtained with a large uncertainty. A weak surrogate relationship was noted between the treatment effects on TR and OS. Modelling the two surrogate endpoints, TR and PFS, jointly as predictors of treatment effect on OS gave no marked improvement to surrogate association patterns. Modest improvement in the precision of the predicted treatment effects on the final outcome was noted in studies investigating anti-angiogenic therapy, however it was likely due to chance.

CONCLUSION

The joint use of two surrogate endpoints did not lead to marked improvement in the association between treatment effects on surrogate and final endpoints in advanced colorectal cancer.

Bivariate network meta-analysis for surrogate endpoint evaluation

Surrogate endpoints are very important in regulatory decision making in healthcare, in particular if they can be measured early compared to the long-term final clinical outcome and act as good predictors of clinical benefit. Bivariate meta-analysis methods can be used to evaluate surrogate endpoints and to predict the treatment effect on the final outcome from the treatment effect measured on a surrogate endpoint. However, candidate surrogate endpoints are often imperfect, and the level of association between the treatment effects on the surrogate and final outcomes may vary between treatments. This imposes a limitation on methods which do not differentiate between the treatments. We develop bivariate network meta-analysis (bvNMA) methods, which combine data on treatment effects on the surrogate and final outcomes, from trials investigating multiple treatment contrasts. The bvNMA methods estimate the effects on both outcomes for all treatment contrasts individually in a single analysis. At the same time, they allow us to model the trial-level surrogacy patterns within each treatment contrast and treatment-level surrogacy, thus enabling predictions of the treatment effect on the final outcome either for a new study in a new population or for a new treatment. Modelling assumptions about the between-studies heterogeneity and the network consistency, and their impact on predictions, are investigated using an illustrative example in advanced colorectal cancer and in a simulation study. When the strength of the surrogate relationships varies across treatment contrasts, bvNMA has the advantage of identifying treatment comparisons for which surrogacy holds, thus leading to better predictions.