Extrapolating Survival Data Using Historical Trial-Based a Priori Distributions

Objectives: To show how clinical trial data can be extrapolated using historical trial data-based a priori distributions. Methods: Extrapolations based on 30-month pivotal multiplemyeloma trial data were compared with 75-month data from the same trial. The 30-month data represent a typical decision-making scenario where early results from a clinical trial are extrapolated. Mature historical trial data with the same comparator as in the pivotal trial were incorporated in 2 stages. First, the parametric distribution selection was based on the historical trial data. Second, the shape parameter estimate of the historical trial was used to define an informative a priori distribution for the shape of the 30-month pivotal trial data. The method was compared with standard approaches, fitting parametric distributions to the 30-month data with noninformative prior. The predicted survival of each method was compared with the observed survival (DAUC) in the 75-month trial data. Results: The Weibull had the best fit to the historical trial and the log-normal to the 30-month pivotal trial data. The DAUC of the Weibull with informative priors was considerably smaller compared with the standard Weibull. Also, the predicted median survival based on the Weibull with informative priors was more accurate (melphalan and prednisone [MP] 40 months, and bortezomib [V] combined with MP [VMP] 62 months) than based on the standard Weibull (MP 45 months and VMP 72 months) when compared with the observed median (MP 41.3 months and VMP 56.4 months). Conclusions: Extrapolation of clinical trial data is improved by using historical trial data-based informative a priori distributions

Comparison of Parametric Survival Extrapolation Approaches Incorporating General Population Mortality for Adequate Health Technology Assessment of New Oncology Drugs

Objectives: Survival extrapolation of trial outcomes is required for health economic evaluation. Generally, all-cause mortality (ACM) is modeled using standard parametric distributions, often without distinguishing disease-specific/excess mortality and general population background mortality (GPM). Recent National Institute for Health and Care Excellence guidance (Technical Support Document 21) recommends adding GPM hazards to disease-specific/excess mortality hazards in the log-likelihood function ("internal additive hazards"). This article compares alternative extrapolation approaches with and without GPM adjustment. Methods: Survival extrapolations using the internal additive hazards approach (1) are compared to no GPM adjustment (2), applying GPM hazards once ACM hazards drop below GPM hazards (3), adding GPM hazards to ACM hazards (4), and pro-portional hazards for ACM versus GPM hazards (5). The fit, face validity, mean predicted life-years, and corresponding uncertainty measures are assessed for the active versus control arms of immature and mature (30-and 75-month follow-up) multiple myeloma data and mature (64-month follow-up) breast cancer data. Results: The 5 approaches yielded considerably different outcomes. Incremental mean predicted life-years vary most in the immature multiple myeloma data set. The lognormal distribution (best statistical fit for approaches 1-4) produces survival increments of 3.5 (95% credible interval: 1.4-5.3), 8.5 (3.1-13.0), 3.5 (1.3-5.4), 2.9 (1.1-4.5), and 1.6 (0.4-2.8) years for approaches 1 to 5, respectively. Approach 1 had the highest face validity for all data sets. Uncertainty over parametric distributions was comparable for GPM-adjusted approaches 1, 3, and 4, and much larger for approach 2. Conclusion: This study highlights the importance of GPM adjustment, and particularly of incorporating GPM hazards in the log-likelihood function of standard parametric distributions

Risk of cardiovascular events, arrhythmia and all-cause mortality associated with clarithromycin versus alternative antibiotics prescribed for respiratory tract infections: a retrospective cohort study

Objective: To determine whether treatment with clarithromycin for respiratory tract infections was associated with an increased risk of cardiovascular (CV) events, arrhythmias or all-cause mortality compared with other antibiotics. Design: Retrospective cohort design comparing clarithromycin monotherapy for lower (LRTI) or upper respiratory tract infection (URTI) with other antibiotic monotherapies for the same indication. Setting: Routine primary care data from the UK Clinical Practice Research Datalink and inpatient data from the Hospital Episode Statistics (HES). Participants: Patients aged ≥35 years prescribed antibiotic monotherapy for LRTI or URTI 1998–2012 and eligible for data linkage to HES. Main outcome measures: The main outcome measures were: adjusted risk of first-ever CV event, within 37 days of initiation, in commonly prescribed antibiotics compared with clarithromycin. Secondarily, adjusted 37-day risks of first-ever arrhythmia and allcause mortality. Results: Of 700 689 treatments for LRTI and eligible for the CV analysis, there were 2071 CV events (unadjusted event rate: 29.6 per 10 000 treatments). Of 691 998 eligible treatments for URTI, there were 688 CV events (9.9 per 10 000 treatments). In LRTI and URTI, there were no significant differences in CV risk between clarithromycin and all other antibiotics combined: OR=1.00 (95% CI 0.82 to 1.22) and 0.82 (0.54 to 1.25), respectively. Adjusted CV risk in LRTI versus clarithromycin ranged from OR=1.42 (cefalexin; 95% CI 1.08 to 1.86) to 0.92 (doxycycline; 0.64 to 1.32); in URTI, from 1.17 (co-amoxiclav; 0.68 to 2.01) to 0.67 (erythromycin; 0.40 to 1.11). Adjusted mortality risk versus clarithromycin in LRTI ranged from 0.42 to 1.32; in URTI, from 0.75 to 1.43. For arrhythmia, adjusted risks in LRTI ranged from 0.68 to 1.05; in URTI, from 0.70 to 1.22. Conclusions: CV events were more likely after LRTI than after URTI. When analysed by specific indication, CV risk associated with clarithromycin was no different to other antibiotics

The Role of Expert Opinion in Projecting Long-Term Survival Outcomes Beyond the Horizon of a Clinical Trial

INTRODUCTION: Clinical trials often have short follow-ups, and long-term outcomes such as survival must be extrapolated. Current extrapolation methods often produce a wide range of survival values. To minimize uncertainty in projections, we developed a novel method that incorporates formally elicited expert opinion in a Bayesian analysis and used it to extrapolate survival in the placebo arm of DAPA-CKD, a phase 3 trial of dapagliflozin in patients with chronic kidney disease (NCT03036150). METHODS: A summary of mortality data from 13 studies that included DAPA-CKD-like populations and training on elicitation were provided to six experts. An elicitation survey was used to gather the experts' 10- and 20-year survival estimates for patients in the placebo arm of DAPA-CKD. These estimates were combined with DAPA-CKD mortality and general population mortality (GPM) data in a Bayesian analysis to extrapolate long-term survival using seven parametric distributions. Results were compared with those from standard frequentist approaches (with and without GPM data) that do not incorporate expert opinion. RESULTS: The group expert-elicited estimate for 20-year survival was 31% (lower estimate, 10%; upper estimate, 40%). In the Bayesian analysis, the 20-year extrapolated survival across the seven distributions was 14.9-39.1%, a range that was 2.4- and 1.6-fold smaller than those produced by the frequentist methods (0.0-56.9% without and 0.0-39.2% with GPM data). CONCLUSIONS: Using expert opinion in a Bayesian analysis provided a robust method for extrapolating long-term survival in the placebo arm of DAPA-CKD. The method could be applied to other populations with limited survival data

Real world evidence (RWE) – a disruptive innovation or the quiet evolution of medical evidence generation? [version 2; referees: 2 approved]

Stakeholders in healthcare are increasingly turning to real world evidence (RWE) to inform their decisions, alongside evidence from randomized controlled trials. RWE is generated by analysing data gathered from routine clinical practice, and can be used across the product lifecycle, providing insights into areas including disease epidemiology, treatment effectiveness and safety, and health economic value and impact. Recently, the US Food and Drug Administration and the European Medicines Agency have stated their ambition for greater use of RWE to support applications for new indications, and are now consulting with their stakeholders to formalize standards and expected methods for generating RWE. Pharmaceutical companies are responding to the increasing demands for RWE by developing standards and processes for each stage of the evidence generation pathway. Some conventions are already in place for assuring quality, whereas other processes are specific to the research question and data sources available. As evidence generation increasingly becomes a core role of medical affairs divisions in large pharmaceutical companies, standards of rigour will continue to evolve and improve. Senior pharmaceutical leaders can drive this change by making RWE a core element of their corporate strategy, providing top-level direction on how their respective companies should approach RWE for maximum quality. Here, we describe the current and future areas of RWE application within the pharmaceutical industry, necessary access to data to generate RWE, and the challenges in communicating RWE. Supporting and building on viewpoints from industry and publicly funded research, our perspective is that at each stage of RWE generation, quality will be critical to the impact that RWE has on healthcare decision-makers; not only where RWE is an established and evolving tool, but also in new areas that have the potential to disrupt and to improve drug development pathways

Real world evidence (RWE) – a disruptive innovation or the quiet evolution of medical evidence generation? [version 1; referees: 2 approved]

Stakeholders in healthcare are increasingly turning to real world evidence (RWE) to inform their decisions, alongside evidence from randomized controlled trials. RWE is generated by analysing data gathered from routine clinical practice, and can be used across the product lifecycle, providing insights into areas including disease epidemiology, treatment effectiveness and safety, and health economic value and impact. Recently, the US Food and Drug Administration and the European Medicines Agency have stated their ambition for greater use of RWE to support applications for new indications, and are now consulting with their stakeholders to formalize standards and expected methods for generating RWE. Pharmaceutical companies are responding to the increasing demands for RWE by developing standards and processes for each stage of the evidence generation pathway. Some conventions are already in place for assuring quality, whereas other processes are specific to the research question and data sources available. As evidence generation increasingly becomes a core role of medical affairs divisions in large pharmaceutical companies, standards of rigour will continue to evolve and improve. Senior pharmaceutical leaders can drive this change by making RWE a core element of their corporate strategy, providing top-level direction on how their respective companies should approach RWE for maximum quality. Here, we describe the current and future areas of RWE application within the pharmaceutical industry, necessary access to data to generate RWE, and the challenges in communicating RWE. Supporting and building on viewpoints from industry and publicly funded research, our perspective is that at each stage of RWE generation, quality will be critical to the impact that RWE has on healthcare decision-makers; not only where RWE is an established and evolving tool, but also in new areas that have the potential to disrupt and to improve drug development pathways

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

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

Definitions, acceptability, limitations, and guidance in the use and reporting of surrogate end points in trials: a scoping review

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

Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves

<p>Abstract</p> <p>Background</p> <p>The results of Randomized Controlled Trials (RCTs) on time-to-event outcomes that are usually reported are median time to events and Cox Hazard Ratio. These do not constitute the sufficient statistics required for meta-analysis or cost-effectiveness analysis, and their use in secondary analyses requires strong assumptions that may not have been adequately tested. In order to enhance the quality of secondary data analyses, we propose a method which derives from the published Kaplan Meier survival curves a close approximation to the original individual patient time-to-event data from which they were generated.</p> <p>Methods</p> <p>We develop an algorithm that maps from digitised curves back to KM data by finding numerical solutions to the inverted KM equations, using where available information on number of events and numbers at risk. The reproducibility and accuracy of survival probabilities, median survival times and hazard ratios based on reconstructed KM data was assessed by comparing published statistics (survival probabilities, medians and hazard ratios) with statistics based on repeated reconstructions by multiple observers.</p> <p>Results</p> <p>The validation exercise established there was no material systematic error and that there was a high degree of reproducibility for all statistics. Accuracy was excellent for survival probabilities and medians, for hazard ratios reasonable accuracy can only be obtained if at least numbers at risk or total number of events are reported.</p> <p>Conclusion</p> <p>The algorithm is a reliable tool for meta-analysis and cost-effectiveness analyses of RCTs reporting time-to-event data. It is recommended that all RCTs should report information on numbers at risk and total number of events alongside KM curves.</p