Speaker
Description
Assessing micronutrient status is essential in nutritional research (Allen, 2025) and typically involves estimating the population wide prevalence of micronutrient deficiencies using biomarker data collected across multiple regions. In such studies, several biomarkers are commonly analyzed to estimate the prevalence of any deficiency, defined as the probability that at least one of the biomarkers falls below its threshold. Here, we compare different methods for estimating this prevalence using either dichotomized biomarkers in a generalized linear mixed model (GLMM) or continuous biomarker data in a (multivariate) linear mixed model (LMM).
We evaluate three approaches: two GLMM-based and one LMM-based. One method, applicable to both GLMMs and LMMs, obtains univariate prevalence estimates for each biomarker and then combines them to estimate the prevalence of any deficiency (Hothorn et al., 2025). For GLMMs, a second approach constructs a composite dichotomized response variable by first dichotomizing each biomarker using biomarker-specific thresholds and then creating the composite response that equals 1 if any biomarker is below its threshold and 0 otherwise. To quantify uncertainty of the estimated prevalence, we apply both a non-parametric bootstrap, and the delta method that includes or excludes the uncertainty of the estimated variance components of the mixed model.
The three approaches are compared using a database containing multiple biomarkers measured in several population subgroups across three countries. Overall, the agreement between the methods in terms of estimated prevalence was high with a maximum absolute difference between methods of 0.028 for prevalences ranging from 0.2 to 0.3. Regarding the uncertainty of estimated prevalence, the approaches that use dichotomized data performed very similarly while using continuous data led to slightly elevated uncertainties in many cases. Nevertheless, the LMM approach offers practical advantages over the GLMM, including that prevalence estimation is simpler for a LMM as GLMMs require numerical integration (Gory et al., 2021), which becomes challenging for complicated covariance structures.
Ongoing methodological work, including the use of a multivariate LMM that accounts for correlations between biomarkers, is expected to yield additional results that will be presented.
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