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Following a request from the European Commission, the EFSA Panel on Nutrition, Novel Foods and Food Allergens (NDA) was asked to deliver a scientific opinion on the conversion of calcium-l-methylfolate and (6S)-5-methyltetrahydrofolic acid glucosamine salt (collectively called 5-MTHF hereafter) into dietary folate equivalents (DFE). Following a systematic review, the conclusions of the opinion are based on one intervention study in adults for intakes < 400 mu g/day and three intervention studies in adults for intakes >= 400 mu g/day. At intakes below 400 mu g/day, folic acid (FA) is assumed to be linearly related to responses of biomarkers of intake and status and is an appropriate comparator for deriving a DFE conversion factor for 5-MTHF. It is proposed to use the same factor as for folic acid for conversion of 5-MTHF into DFE for intakes < 400 mu g/day. As such intake levels are unlikely to be exceeded through fortified food consumption, the conversion factor of 1.7 relative to natural food folate (NF) could be applied to 5-MTHF added to foods and to food supplements providing < 400 mu g/day. At 400 mu g/day, 5-MTHF was found to be more bioavailable than folic acid and a conversion factor of 2 is proposed for this intake level and for higher intakes. The derived DFE equations are DFE = NF + 1.7 x FA + 1.7 x 5-MTHF for fortified foods and food supplements providing intakes < 400 mu g/day; and DFE = NF + 1.7 x FA + 2.0 x 5-MTHF for food supplements providing intakes >= 400 mu g/day. Although this assessment applies to calcium-L-methylfolate and 5-MTHF glucosamine salt, it is considered that the influence of the cation on bioavailability is likely to be within the margin of error of the proposed DFE equations. Therefore, the proposed equations can also be applied to 5-MTHF associated with other cations.

The presence of one or more covariates that perfectly or almost perfectly predict the outcome of interest (which is referred to as complete or quasi-complete separation, the latter denoting the case when such perfect prediction occurs only for a subset of observations in the data) has been extensively studied in the last four decades. Since 1984, when Albert and Anderson (1984) differentiated between complete and quasi-complete separation, several authors have studied this phenomenon and tried to provide answers or ways of identifying the problem (Lesaffre and Albert, 1989; Firth, 1993; Christmann and Rousseeuw, 2001; Rousseeuw and Christmann, 2003; Allison, 2004; Zorn, 2005; Heinze, 2006). From an estimation perspective, separation leads to infinite coefficients and standard errors, which makes the algorithm collapse or give inappropriate results. As a practical matter, separation forces the analyst to choose from a number of problematic alternatives for dealing with the problem, and in the past the elimination of such problematic variables were common practice to deal with such situations. In the last decade, solutions using penalized likelihood have been proposed, but always dealing with independent binary data. Here we will propose a Bayesian solution to the problem when we deal with clustered binary data using informative priors that are supported by the data and compare it with an alternative procedure proposed by Gelman et al. (2008). The authors gratefully acknowledge support from the fund of Scientific Research (FWO, Research Grant G.0151.05) and Belgian IUAP/PAI network P6/03 'Statistical Techniques and Modeling for Complex Substantive Questions with Complex Data' of the Belgian Government (Belgian Science Policy). We would also like to thank the Yves van der Stede (CODA, from Belgium) for providing the data which motivated this research.