Key result
Harmonization is a methodological approach to combine individual data from observational studies, which may increase sample size and advance multiple sclerosis research.
Answers are grounded in this paper and cite their sources.
Harmonization of observational data may help overcome funding limitations and sample size requirements for studying rare risk factors in multiple sclerosis.
Decreasing research funding is in conflict with the increasing need to conduct large studies to examine rare risk factors and interactions between risk factors. As a result, investigators are searching for strategies to stretch research funds and to design studies that will maximize investments already made. Multiple sclerosis (MS) is generally accepted as a multifactorial disease, and the assessment of interactions between risk factors and the desire to assess risk factors within particular sub-groups requires a large number of participants. Harmonization is a methodology that may help address this problem. Harmonization is a methodological approach that aims to systematize the process of combining individual data that are collected in several observational studies. Combining data will increase sample size, but the quality of the harmonized result is only as high as the quality of the individual studies and the comparability of the constructs measured. In this short report, we introduce the concept of harmonization and provide examples where harmonization may be advantageous in MS research.
No takes yet. Share an insight, caveat, or question.
Magalhaes et al. (2012) conducted a review in Multiple sclerosis. Harmonization methodology was evaluated. Harmonization is a methodological approach to combine individual data from observational studies, which may increase sample size and advance multiple sclerosis research.
Synapse has enriched 10 related papers. Consider them for comparative context: