Model-Based Meta-Analysis

What is model-based meta-analysis (MBMA)?

Since a landmark presentation at the Clinical Pharmacology Subcommittee Meeting at the US Food and Drug Administration in 2006, model-based meta-analysis has become an accepted innovative strategy to make better use of available data, resulting in increased knowledge and better (more precise) decision making in clinical development. The strategy involves a systematic search and tabulation of summary results from public and confidential clinical trials, and a regression analysis that attributes the variability in results to differences in the study population or the trial conduct. This contrasts with standard meta-analyses, which usually focuses on public data only, and selects trials with a common design and population.

It is not surprising that this method brings value. Drug development decisions are usually made with in-depth quantitative analysis of internal data from the drug candidate and a comprehensive, but less quantitative, review of public data or data from other candidates. Most decisions cannot be made with internal data alone. Model-based meta-analysis provides a quantitative framework to leverage valuable external data into the decision making process for a drug candidate.

What can we quantify with model-based meta-analysis (MBMA)?

Comparative safety and efficacy profile
There are very few active comparator trials in drug development, however it is often important to assess a compound’s safety and efficacy profile in comparison to SOC and/or competitor drugs in development. Model-based meta analysis enables indirect comparison, taking into account the impact of treatment, patient population, and trial characteristics. This type of analysis can help estimate the probability that a drug is superior than its competitors in the same drug class or across drug classes.

Endpoint-to-endpoint relationships
Our clinical outcomes databases contain large amount of data from published sources, which enables the applications of MBMA to make biomarker to clinical, and short-term to long- term endpoint predictions. Model-based meta-analysis can also be applied to scale across indications. These analysis help predict drug performance in later stage development, or in a different indication

Here are some excellent examples of the application of MBMA in drug development.