DataSHIELD - shared individual-level analysis without sharing the data:A biostatistical perspective

Very large sample sizes are required for estimating effects which are known to be small, and for addressing intricate or complex statistical questions. This is often only achievable by pooling data from multiple studies, especially in genetic epidemiology where associations between individual genetic variants and phenotypes of interest are generally weak. However, the physical pooling of experimental data across a consortium is frequently prohibited by the ethico-legal constraints that govern agreements and consents for individual studies. Study level meta-analyses are frequently used so that data from multiple studies need not be pooled to conduct an analysis, though the resulting analysis is necessarily restricted by the available summary statistics. The idea of maintaining data security is also of importance in other areas and approaches to carrying out 'secure analyses' that do not require sharing of data from different sources have been proposed in the technometrics literature. Crucially, the algorithms for fitting certain statistical models can be manipulated so that an individual level meta-analysis can essentially be performed without the need for pooling individual-level data by combining particular summary statistics obtained individually from each study. DataSHIELD (Data Aggregation Through Anonymous Summary-statistics from Harmonised Individual levEL Databases) is a tool to coordinate analyses of data that cannot be pooled. In this paper, we focus on explaining why a DataSHIELD approach yields identical results to an individual level meta-analysis in the case of a generalised linear model, by simply using summary statistics from each study. It is also an efficient approach to carrying out a study level meta-analysis when this is appropriate and when the analysis can be pre-planned. We briefly comment on the IT requirements, together with the ethical and legal challenges which must be addressed.