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<p>Smartphone-based electrocardiograms (ECGs) are increasingly utilized for monitoring atrial fibrillation (AF) recurrence after catheter ablation (CA), referred to as smartphone AF burden (SMURDEN). The SMURDEN data often exhibit complex patterns of zero AF episodes, which may arise from either true AF-free status (structural zeros) or missed AF episodes due to intermittent monitoring (random zeros). Such a mixture of AF-free and at-risk patients can lead to zero-inflation in the data. The authors propose a novel zero-inflation test for binomial regression models to identify recurrence-free AF populations. Unlike traditional approaches requiring fully specified zero-inflated models, the proposed test utilizes a weighted average of the discrepancies between observed and expected zero proportions, with weights determined by binomial sizes. A closed-form test statistic is developed, and its asymptotic distribution is derived using estimating equations. Simulations demonstrate superior performance over existing methods, and real-world AF monitoring data validate the practical utility of our proposed test.</p>

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Testing Zero-Inflation in Binomial Regression Models with an Application to Electrocardiogram Monitoring on Atrial Fibrillation

  • 期刊论文

  • 发表日期：

    2026年2月5日

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  • Xinhui Qiao,
  • Peng Ye,
  • Hua He,
  • Han Feng,
  • Xiangzhong Fang

摘要

Smartphone-based electrocardiograms (ECGs) are increasingly utilized for monitoring atrial fibrillation (AF) recurrence after catheter ablation (CA), referred to as smartphone AF burden (SMURDEN). The SMURDEN data often exhibit complex patterns of zero AF episodes, which may arise from either true AF-free status (structural zeros) or missed AF episodes due to intermittent monitoring (random zeros). Such a mixture of AF-free and at-risk patients can lead to zero-inflation in the data. The authors propose a novel zero-inflation test for binomial regression models to identify recurrence-free AF populations. Unlike traditional approaches requiring fully specified zero-inflated models, the proposed test utilizes a weighted average of the discrepancies between observed and expected zero proportions, with weights determined by binomial sizes. A closed-form test statistic is developed, and its asymptotic distribution is derived using estimating equations. Simulations demonstrate superior performance over existing methods, and real-world AF monitoring data validate the practical utility of our proposed test.