Non-inferiority and networks: inferring efficacy from a web of data

Propensity-score-based meta-analytic predictive prior for incorporating real-world and historical data

As the availability of real-world data sources (eg, EHRs, claims data, registries) and historical data has rapidly surged in recent years, there is an increasing interest and need from investigators and health authorities to leverage all available information to reduce patient burden and accelerate both drug development and regulatory decision making. Bayesian meta-analytic approaches are a popular historical borrowing method that has been developed to leverage such data using robust hierarchical models. The model structure accounts for various degrees of between-trial heterogeneity, resulting in adaptively discounting the external information in the case of data conflict. In this article, we propose to integrate the propensity score method and Bayesian meta-analytic-predictive (MAP) prior to leverage external real-world and historical data. The propensity score methodology is applied to select a subset of patients from external data that are similar to those in the current study with regards to key baseline covariates and to stratify the selected patients together with those in the current study into more homogeneous strata. The MAP prior approach is used to obtain stratum-specific MAP prior and derive the overall propensity score integrated meta-analytic predictive (PS-MAP) prior. Additionally, we allow for tuning the prior effective sample size for the proposed PS-MAP prior, which quantifies the amount of information borrowed from external data. We evaluate the performance of the proposed PS-MAP prior by comparing it to the existing propensity score-integrated power prior approach in a simulation study and illustrate its implementation with an example of a single-arm phase II trial.

Bayesian approaches to benefit-risk assessment for diagnostic tests

Benefit-risk assessment plays an important role in the evaluation of medical devices. Unlike the therapeutic devices, the diagnostic tests usually affect patient life indirectly since subsequent therapeutic treatment interventions (such as proper treatment in time, further examination or test, no action, etc.) will depend on correct diagnosis and monitoring of the disease status. A benefit-risk score using statistical models by integrating the information from benefit (true positive and true negative) and risk (false positive and false negative) for diagnostic tests with binary outcomes (i.e., positive and negative) will help evaluation of the utility and the uncertainty of a particular diagnostic device. In this paper, we develop two types of Bayesian models with conjugate priors for constructing the benefit-risk (BR) measures with corresponding credible intervals, one based on a Multinomial model with Dirichlet prior, and the other based on independent Binomial models with independent Beta priors. We then propose a Bayesian power prior model to incorporate the historical data or the real-world data (RWD). Both the fixed and random power prior parameters are considered for Bayesian borrowing. We evaluate the performance of the methods by simulations and illustrate their implementation using a real example.

Flexible Bayesian subgroup analysis in early and confirmatory trials

Subgroup analysis is one of the most important issues in clinical trials. In confirmatory trials, it is critical to investigate consistency of the treatment effect across subgroups, which could potentially result in incorrect scientific conclusion or regulatory decision. There are many challenges and methodological complications of interpreting subgroup results beyond the regulatory setting. For the early phase or proof of concept trials, particularly in basket trials, it is also important to have reliable estimation of subgroup treatment effect in order to guide the next phase go/no-go decision making when large biases can be introduced due to small sample size or random variability. In this paper, we review several recent methods that have been proposed for subgroup analysis in the Bayesian framework to correct for bias. We present simulation results from applying various novel Bayesian hierarchical models for subgroup analysis to a phase II basket trial. For different scenarios considered, we compare the average total sample size, and frequentist-like operating characteristics of power and familywise type I error rate. We compare the precision of the model estimates of the treatment effect by assessing average relative bias and the width of the 95% credible interval for the bias. We also demonstrate flexible Bayesian hierarchical models in a case study of a phase III oncology trial for subgroup treatment effect estimation to help with regulatory decision making. Finally, we conclude our findings in the discussion section and give recommendations on how these methods could be implemented in confirmatory and early phase clinical trials.