Covariate adjusted meta-analytic predictive (CA-MAP) prior for historical borrowing using patient-level data.

Utilization of historical data is increasingly common for gaining efficiency in the drug development and decision-making processes. The underlying issue of between-trial heterogeneity in clinical trials is a barrier in making these methods standard practice in the pharmaceutical industry. Common methods for historical borrowing discount the borrowed information based on the similarity between outcomes in the historical and current data.

Bayesian Additive Regression Trees (BART) with covariate adjusted borrowing in subgroup analyses.

It is crucial in clinical trials to investigate treatment effect consistency across subgroups defined by patient baseline characteristics. However, there may be treatment effect variability across subgroups due to small subgroup sample size. Various Bayesian models have been proposed to incorporate this variability when borrowing information across subgroups.

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.

Bayesian semiparametric meta-analytic-predictive prior for historical control borrowing in clinical trials.

When designing a clinical trial, borrowing historical control information can provide a more efficient approach by reducing the necessary control arm sample size while still yielding increased power. Several Bayesian methods for incorporating historical information via a prior distribution have been proposed, for example, (modified) power prior, (robust) meta-analytic predictive prior. When utilizing historical control borrowing, the prior parameter(s) must be specified to determine the magnitude of borrowing before the current data are observed.

Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications.

Objective: To compare performance of risk prediction models for forecasting postoperative sepsis and acute kidney injury.

Design: Retrospective single center cohort study of adult surgical patients admitted between 2000 and 2010.

Patients: 50,318 adult patients undergoing major surgery.