Vitamin D insufficiency in the first 6 months of infancy and challenge-proven IgE-mediated food allergy at 1 year of age: a case-cohort study.

BACKGROUND: Ecological evidence suggests vitamin D insufficiency (VDI) due to lower ambient ultraviolet radiation (UVR) exposure may be a risk factor for IgE-mediated food allergy. However, there are no studies relating directly measured VDI during early infancy to subsequent challenge-proven food allergy. OBJECTIVE: To prospectively investigate the association between VDI during infancy and challenge-proven food allergy at 1 year. METHODS: In a birth cohort (n = 1074), we used a case-cohort design to compare 25-hydroxyvitamin D3 (25(OH)D3 ) levels among infants with food allergy vs a random subcohort (n = 274). The primary exposures were VDI (25(OH)D3 <50 nM) at birth and 6 months of age. Ambient UVR and time in the sun were combined to estimate UVR exposure dose. IgE-mediated food allergy status at 1 year was determined by formal challenge. Binomial regression was used to examine associations between VDI, UVR exposure dose and food allergy and investigate potential confounding. RESULTS: Within the random subcohort, VDI was present in 45% (105/233) of newborns and 24% (55/227) of infants at 6 months. Food allergy prevalence at 1 year was 7.7% (61/786), and 6.5% (53/808) were egg-allergic. There was no evidence of an association between VDI at either birth (aRR 1.25, 95% CI 0.70-2.22) or 6 months (aRR 0.93, 95% CI 0.41-2.14) and food allergy at 1 year. CONCLUSIONS: There was no evidence that VDI during the first 6 months of infancy is a risk factor for food allergy at 1 year of age. These findings primarily relate to egg allergy, and larger studies are required.

Sensitivity analysis of incomplete longitudinal data departing from the missing at random assumption: Methodology and application in a clinical trial with drop-outs.

Statistical analyses of longitudinal data with drop-outs based on direct likelihood, and using all the available data, provide unbiased and fully efficient estimates under some assumptions about the drop-out mechanism. Unfortunately, these assumptions can never be tested from the data. Thus, sensitivity analyses should be routinely performed to assess the robustness of inferences to departures from these assumptions. However, each specific scientific context requires different considerations when setting up such an analysis, no standard method exists and this is still an active area of research. We propose a flexible procedure to perform sensitivity analyses when dealing with continuous outcomes, which are described by a linear mixed model in an initial likelihood analysis. The methodology relies on the pattern-mixture model factorisation of the full data likelihood and was validated in a simulation study. The approach was prompted by a randomised clinical trial for sleep-maintenance insomnia treatment. This case study illustrated the practical value of our approach and underlined the need for sensitivity analyses when analysing data with drop-outs: some of the conclusions from the initial analysis were shown to be reliable, while others were found to be fragile and strongly dependent on modelling assumptions. R code for implementation is provided.