The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
New Year Round-Up: Your Favorite Posts of 2018
Happy New Year! As we look ahead to future successes and the new advancements in drug development that 2019 will bring, we are taking a moment to reflect on the topics that resonated most with our community on the Cytel blog in 2018. While these 6 most popular blogs encompass a variety of topics from across the data science, statistics, and statistical programming space, they all have in common a focus on innovative practices and application of statistical, data management, and data science excellence to achieve better outcomes in drug development.
5 Reasons to Integrate Model-Based Meta-Analyses (MBMA) Into Your Clinical Development Strategy
By Esha Senchaudhuri
An important trend in clinical development involves integrating strategic pharmacometric analysis with program level decision-making, to make the most use of available data. This can occur in various forms, from leveraging preclinical data for go-no-go decision making [1], to the need for improved comparative effectiveness frameworks [2].
Here we have five reasons why you should consider utilizing model-based meta-analyses ( MBMAs) for your program or portfolio development.
How Can We Tackle Heterogeneity in Meta-Analysis?
Health professionals and policy makers want to make healthcare decisions based on the relevant research evidence. The questions in clinical research are typically studied more than once independently by different researchers. Literature review is used for summarizing the results of these studies and strengthening the evidence. Systematic review is a type of literature review that collects and critically analyzes multiple research studies or papers that answer the same question. If the results of these multiple studies are diverse and conflicting then the clinical decision-making becomes difficult. To overcome this problem meta-analysis is used. Meta-analysis is a statistical procedure for combining the results of studies that are included in systematic review. While meta-analysis is a powerful technique, it may give misleading results due to issues like improper selection of studies, publication bias, and heterogeneity among studies. In this blog, we will focus on the problem of heterogeneity.