Quantitative Synthesis of Personalized Trials Studies: Meta-Analysis of Aggregated Data Versus Individual Patient Data.

Abstract

We have entered an era in which scientific knowledge and evidence increasingly inform research practice and policy. As there is an exponential increase in the use of personalized trials, there is a remarkable growing interest in the quantitative synthesis of personalized trials. One technique that is developed and can be applied for this purpose is meta-analysis. Meta-analysis involves the quantitative integration of effect sizes from several personalized trials. In this study, aggregated data (AD) and individual patient data (IPD) methods for meta-analysis of personalized trials are discussed, together with an empirical demonstration using a subset of a real meta-analytic data set. For the empirical demonstration, 26 personalized trials received usual care and yoga intervention in a randomized sequence. Results show a general consensus between the AD and IPD approach in terms of conclusions-that both usual care and the yoga intervention are effective in reducing pain. However, the IPD approach provides more information about the intervention effectiveness and intervention heterogeneity. IPD is a more flexible modeling approach, allowing for a variety of modeling options.