Replicating research experiments or studies can often lead to different conclusions, sometimes even contradicting each other. To resolve potential conflicts,
researchers often pool related studies to obtain a combined, more reliable result.
This pooling of information is called meta-analysis. Assuming reasonable consistency, one can combine experiments in any research area. A large number of
statistical methods for meta-analysis have been developed over the years. Most
of these methods are tailored for specific tasks, such as combining clinical trials
or genomic experiments, and cannot be immediately applied to other problems.
A more general group of meta-analytical methods are based on rank order data.
These methods work with ranks instead of the measured values themselves, the
latter of which are not always available, and therefore are not limited by the
data type and not disturbed by different data transformations, presence of outliers, or requirements regarding their statistical distribution. Nevertheless, the
generality of rank-based methods comes at a price: the relative differences between the measured values are lost and as a consequence they cannot estimate
the common study signals that have produced the observed ranks.
We developed an approach that combines the advantages of rank-based
methods, while achieving the ultimate goal of meta-analysis: estimating those
signals that are causal for the ranks. We built a stochastic model describing
the relationship between the unknown signals and the observed ranks. Using
Markov chains we estimated the parameters of the model together with their
standard errors. The stability of the observed rank positions was also assessed.
The proposed approach was tested on simulated data under various scenarios,
and applied to real data combining studies and experiments from clinical and
genomic research.
Reference
Svendova, V. and Schimek, M.G. (2017). A novel method for estimating the
common signals for consensus across multiple ranked lists. Computational
Statistics and Data Analysis, Volume 115, November 2017, pp. 122-135.