Incorporating Prior Data in Quantitative Benefit-Risk Assessments: Case Study of a Bayesian Method.

BACKGROUND: Multiple criteria decision analysis (MCDA) and stochastic multi-criteria acceptability analysis (SMAA) in their current implementation cannot incorporate prior or external information on benefits and risks. We demonstrate how to incorporate prior data using a Bayesian mixture model approach while conducting quantitative benefit-risk assessments (qBRA) for medical products. METHODS: We implemented MCDA and SMAA in a Bayesian framework. To incorporate information from a prior study, we use mixture priors on each benefit and risk attribute that mixes information from a previous study with a vague prior distribution. The degree of borrowing is varied using a mixing proportion parameter. RESULTS: A demonstration case study for qBRA using the supplementary New Drug Application (sNDA) filing for Rivaroxaban for the indication of reduction in the risk of major thrombotic vascular events in patients with peripheral artery disease (PAD) was used to illustrate the method. Net utility scores, obtained from the randomized controlled trial data to support the sNDA, from the MCDA for Rivaraxoban and comparator were 0.48 and 0.56, respectively, with Rivaroxaban being the preferred alternative only 33% of the time. We show that with only 30% borrowing from a previous RCT, the MCDA and SMAA results are favorable for Rivaroxaban, accounting for the seemingly aberrant results on all-cause death in the trial data used to support the sNDA. CONCLUSION: Our method to formally incorporate prior data in MCDA and SMAA is easy to use and interpret. Software in the form of an RShiny App is available here: https://sai-dharmarajan.shinyapps.io/BayesianMCDA_SMAA/ .

Leveraging external evidence using Bayesian hierarchical model and propensity score in the presence of covariates.

In recent decades, there has been growing interest in leveraging external data information for clinical development as it improves the efficiency of the design and inference of clinical trials when utilized properly and more importantly, alleviates potential ethical and recruitment challenges. When it is of interest to augment the concurrent study's control arm using external control data, the potential outcome heterogeneity across data sources, also known as prior-data conflict, should be accounted for. In addition, in the outcome modeling, inclusion of prognostic covariates that may have impact on the outcome can avoid efficiency loss or potential bias. In this paper, we propose a Bayesian hierarchical modeling strategy incorporating covariate-adjusted meta-analytic predictive approach (cMAP) and also introduce a propensity score (PS) based sequential procedure that integrates the cMAP. In the simulation study, the proposed methods are found to have advantages in the estimation, power, and type I error control over the standard methods such as PS matching alone and hierarchical modeling that ignores the covariates. An illustrative example is used to illustrate the procedure.

Addressing statistical issues when leveraging external control data in pediatric clinical trials using Bayesian dynamic borrowing.

Conducting a well-powered and adequately controlled clinical trial in children is often challenging. Bayesian approaches are an attractive option for addressing such challenges as they provide a quantitatively rigorous and integrated framework that makes use of current control data to check and borrow information from historical control data. However various practical concerns and related statistical issues emerge when implementing such Bayesian borrowing approaches. In this manuscript we use a motivating case study to discuss a rigorous stepwise approach on how to address those issues within the Bayesian framework. Specifically, a comprehensive quantitative framework is proposed to assess the extent, synergy, and impact of borrowing. Steps on computing the measures to interpret borrowing are illustrated. Those measures can further help to determine whether additional discounting of external information is necessary.

Handling death as an intercurrent event in time to recovery analysis in COVID-19 treatment clinical trials.

In clinical trials with the objective to evaluate the treatment effect on time to recovery, such as investigational trials on therapies for COVID-19 hospitalized patients, the patients may face a mortality risk that competes with the opportunity to recover (e.g., be discharged from the hospital). Therefore, an appropriate analytical strategy to account for death is particularly important due to its potential impact on the estimation of the treatment effect. To address this challenge, we conducted a thorough evaluation and comparison of nine survival analysis methods with different strategies to account for death, including standard survival analysis methods with different censoring strategies and competing risk analysis methods. We report results of a comprehensive simulation study that employed design parameters commonly seen in COVID-19 trials and case studies using reconstructed data from a published COVID-19 clinical trial. Our research results demonstrate that, when there is a moderate to large proportion of patients who died before observing their recovery, competing risk analyses and survival analyses with the strategy to censor death at the maximum follow-up timepoint would be able to better detect a treatment effect on recovery than the standard survival analysis that treat death as a non-informative censoring event. The aim of this research is to raise awareness of the importance of handling death appropriately in the time-to-recovery analysis when planning current and future COVID-19 treatment trials.

Using the Bayesian detection of potential risk using inference on blinded safety data (BDRIBS) method to support the decision to refer an event for unblinded evaluation.

In the Sponsor Responsibilities-Safety Reporting Requirements and Safety Assessment for IND and Bioavailability/Bioequivalence Studies: Draft Guidance for Industry (June 2021) the Food and Drug Administration recommends that sponsors develop a Safety Surveillance Plan as a key element of a systematic approach to safety surveillance and describes two possible approaches to assess the aggregate safety data. One approach regularly analyzes unblinded serious adverse events (SAEs) by treatment group. The alternative approach prespecifies estimated background rates for anticipated SAEs in the study population (e.g., myocardial infarctions in an older adult population). If the event rate in the blinded data from the study population exceeds a "trigger rate," then an unblinded analysis by treatment group is conducted. The Bayesian detection of potential risk using inference on blinded safety data (BDRIBS) method has been previously described and offers a quantitative approach for assessing blinded events. In this article we provide a procedural workflow for blinded review of safety data that is consistent with the unblinding "trigger approach" for aggregate safety review. In addition, this publication contextualizes the use of BDRIBS within the broader safety surveillance framework, extends the method to allow for multiple studies, and offers examples of its use in various settings via an R-Shiny application that allows for dynamic visualization and assessment.

Modified Goldilocks Design with strict type I error control in confirmatory clinical trials.

Goldilocks Design (GD) utilizes predictive probability to adaptively select a trial's sample size based on accumulating data. In order to control type I error at a desired level for a subset of the null space, extensive simulations at the study design stage are required to choose critical values, which is a challenge for this type of Bayesian adaptive design to be used for confirmatory trials. In this article, we propose a Modified Goldilocks Design (MGD) where type I error is analytically controlled over the entire null space. We do so by applying the conditional invariance principle and a combination test approach on [Formula: see text]-values that are obtained from independent cohorts of subjects. Simulation studies show that despite analytic control of type I error rate, the proposed MGD has similar power when compared with the original GD. We further apply it to an example trial with time-to-event endpoint in oncology.

Bayesian detection of potential risk using inference on blinded safety data.

Safety surveillance is a critical issue for ongoing clinical trials to actively identify and evaluate important safety information. With the new regulatory emphasis on aggregate review of safety, sponsors are faced with the challenge to develop systematic and sound quantitative methods to assess risk from blinded safety data during the pre-approval period of a new therapy. To address this challenge, a novel statistical method is proposed to monitor and detect safety signals with data from blinded ongoing clinical trials, specifically for adverse events of special interest (AESI) when historical data are available to provide background rates. This new method is a two-step Bayesian evaluation of safety signals composed of a screening analysis followed by a sensitivity analysis. This Bayesian modeling framework allows making inference on the relative risk in blinded ongoing clinical trials to detect any safety signal for AESI. The blinded safety teams can use this method to assess the signal and decide if any safety signals should be escalated for unblinded review.

Evaluation of the efficacy and safety of etoricoxib in the treatment of hemophilic arthropathy.

This 2-part, double-blind, placebo-controlled study was conducted to determine the safety and efficacy of etoricoxib, a COX-2 selective inhibitor, for the treatment of hemophilic arthropathy. In part 1 (6 weeks), 102 patients (> or = 12 years old) with hemophilic arthropathy were randomized to receive 90 mg etoricoxib once daily or placebo (1:1 ratio). In part 2 (6 months), 51 patients taking placebo in part 1 were randomized to receive 90 mg etoricoxib or 25 mg rofecoxib once daily; patients taking etoricoxib in part 1 continued the same treatment. Efficacy end points included Patient Assessment of Arthropathy Pain, Patient Global Assessment of Arthropathy Disease Status, and Investigator Global Assessment of Arthropathy Disease Status. Safety was evaluated at each study visit. Etoricoxib provided significant improvement in all end points versus placebo (P < .001). Fewer patients taking etoricoxib discontinued due to a lack of efficacy versus placebo (P = .048). During part 2, efficacy was maintained; etoricoxib and rofecoxib demonstrated similar results. The most common adverse experiences were upper respiratory infection and headache. The incidence of joint bleeding during part 1 was similar between etoricoxib (66.7%) and placebo (72.6%) and during part 2 between etoricoxib (77.0%) and rofecoxib (78.9%). We conclude that etoricoxib provided superior efficacy versus placebo for the treatment of hemophilic arthropathy and was generally safe and well tolerated.

Effect of rofecoxib on prednisolone and prednisone pharmacokinetics in healthy subjects.

Patients receiving nonsteroidal anti-inflammatory drug therapy may also require administration of corticosteroids, particularly patients with rheumatoid arthritis. To investigate the effect of rofecoxib on the single-dose pharmacokinetics of oral prednisone and intravenous prednisolone, the authors conducted a randomized, double-blind, placebo-controlled crossover study in 12 healthy subjects. Oral rofecoxib (250.0 mg/day for 14 days) failed to influence prednisone or prednisolone pharmacokinetics after intravenous prednisolone or oral prednisone administration. The geometric mean ratio (GMR) (90% confidence interval) of prednisolone AUC infinity (rofecoxib/placebo) following intravenous and oral corticosteroid was 0.97 (0.94, 1.01) and 0.99 (0.91, 1.08), respectively. Similarly, the prednisone AUC infinity GMRs (rofecoxib/placebo) after intravenous and oral corticosteroid were 1.03 (0.95, 1.11) and 1.08 (0.92, 1.28), respectively. The absence of an effect of rofecoxib on the pharmacokinetics of oral prednisone or intravenous prednisolone indicates that no adjustment in dose of this corticosteroid is necessary when administered concurrently with rofecoxib.

Randomised double blind trial of etoricoxib and indometacin in treatment of acute gouty arthritis.

OBJECTIVE: To assess the safety and efficacy of etoricoxib, a selective cyclo-oxygenase-2 inhibitor, in comparison with indometacin in the treatment of acute gouty arthritis. DESIGN: Randomised, double blind, active comparator controlled trial. SETTING: 43 outpatient study centres in 11 countries. PARTICIPANTS: 142 men and eight women (75 patients per treatment group) aged 18 years or over presenting with clinically diagnosed acute gout within 48 hours of onset. INTERVENTIONS: Etoricoxib 120 mg administered orally once daily versus indometacin 50 mg administered orally three times daily, both for 8 days. MAIN OUTCOME MEASURES: Patients' assessment of pain in the study joint over days 2 to 5 (primary end point); investigators' and patients' global assessments of response to treatment and tenderness of the study joint (key secondary end points). RESULTS: Etoricoxib showed efficacy comparable to indometacin. Patients' assessment of pain in the study joint (0-4 point Likert scale, "no pain" to "extreme pain") over days 2 to 5 showed a least squares mean change from baseline of -1.72 (95% confidence interval -1.90 to -1.55) for etoricoxib and -1.83 (-2.01 to -1.65) for indometacin. The difference between treatment groups met prespecified comparability criteria. All other efficacy end points, including those reflecting reduction in inflammation and analgesia, provided corroborative evidence of comparable efficacy. Significant pain relief was evident at the first measurement, 4 hours after the first dose of treatment. Prespecified safety analyses revealed that drug related adverse experiences occurred significantly less frequently with etoricoxib (22.7%) than with indometacin (46.7%) (P=0.003), although overall adverse experience rates were similar between the two treatment groups. CONCLUSION: Etoricoxib 120 mg once daily provides rapid and effective treatment for acute gouty arthritis comparable to indometacin 50 mg three times daily. Etoricoxib was generally safe and well tolerated in this study.