Bayesian Hierarchical Models for Subgroup Analysis.

In conventional subgroup analyses, subgroup treatment effects are estimated using data from each subgroup separately without considering data from other subgroups in the same study. The subgroup treatment effects estimated this way may be heterogenous with high variability due to small sample sizes in some subgroups and much different from the treatment effect in the overall population. A Bayesian hierarchical model (BHM) can be used to derive more precise, and less heterogenous estimates of subgroup treatment effects that are closer to the treatment effect in the overall population.

Evidence-based objective performance criteria for the evaluation of hip and knee replacement devices and technologies.

Background: Objective performance criteria (OPC) is a novel method to provide minimum performance standards and improve the regulated introduction of original or incremental device innovations in order to prevent patients from being exposed to potentially inferior designs whilst allowing timely access to improvements. We developed 2-year safety and effectiveness OPC for total hip and knee replacement (THR and TKR).

Methods: Analyses of large databases were conducted using various data sources: a systematic literature review; a direct data analysis from The Functional Outcomes Research for Comparative Effectiveness in Total Joint Replacement and Quality Improvement Registry (FORCE-TJR) and the Kaiser Permanente Implant Registry (KPIR); and claims data analyses from longitudinal discharge data in New York and California states.

Retrieval and clinical analysis of distraction-based dual growing rod constructs for early-onset scoliosis.

Background Context: Growing rod constructs are an important contribution for treating patients with early-onset scoliosis. These devices experience high failure rates, including rod fractures.

Purpose: The objective of this study was to identify the failure mechanism of retrieved growing rods, and to identify differences between patients with failed and intact constructs.

Bayesian approaches in medical device clinical trials: a discussion with examples in the regulatory setting.

Challenging statistical issues often arise in the design and analysis of clinical trials to assess safety and effectiveness of medical devices in the regulatory setting. The use of Bayesian methods in the design and analysis of medical device clinical trials has been increasing significantly in the past decade, not only due to the availability of prior information, but mainly due to the appealing nature of Bayesian clinical trial designs. The Center for Devices and Radiological Health at the Food and Drug Administration (FDA) has gained extensive experience with the use of Bayesian statistical methods and has identified some important issues that need further exploration.