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A reference interval represents the normative range for measurements from a healthy population. It plays an important role in laboratory testing, as well as in differentiating healthy from diseased patients. The reference interval based on a single study might not be applicable to a broader population. Meta‐analysis can provide a more generalizable reference interval based on the combined population by synthesizing results from multiple studies. However, the assumptions of normally distributed underlying study‐specific means and equal within‐study variances, which are commonly used in existing methods, are strong and may not hold in practice. We propose a Bayesian nonparametric model with more flexible assumptions to extend random effects meta‐analysis for estimating reference intervals. We illustrate through simulation studies and two real data examples the performance of our proposed approach when the assumptions of normally distributed study means and equal within‐study variances do not hold.
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