Gebregziabher Mulugeta, Voronca Delia, Teklehaimanot Abeba, Santa Ana Elizabeth J
1 Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.
2 Health Equity and Rural Outreach Innovation Center, Ralph H. Johnson Department of Veterans Affairs Medical Center, Charleston, SC, USA.
Stat Methods Med Res. 2017 Jun;26(3):1476-1499. doi: 10.1177/0962280215583402. Epub 2015 Apr 22.
Continuous outcomes with preponderance of zero values are ubiquitous in data that arise from biomedical studies, for example studies of addictive disorders. This is known to lead to violation of standard assumptions in parametric inference and enhances the risk of misleading conclusions unless managed properly. Two-part models are commonly used to deal with this problem. However, standard two-part models have limitations with respect to obtaining parameter estimates that have marginal interpretation of covariate effects which are important in many biomedical applications. Recently marginalized two-part models are proposed but their development is limited to log-normal and log-skew-normal distributions. Thus, in this paper, we propose a finite mixture approach, with Weibull mixture regression as a special case, to deal with the problem. We use extensive simulation study to assess the performance of the proposed model in finite samples and to make comparisons with other family of models via statistical information and mean squared error criteria. We demonstrate its application on real data from a randomized controlled trial of addictive disorders. Our results show that a two-component Weibull mixture model is preferred for modeling zero-heavy continuous data when the non-zero part are simulated from Weibull or similar distributions such as Gamma or truncated Gauss.
在生物医学研究产生的数据中，例如成瘾性障碍的研究，零值占优势的连续结果普遍存在。众所周知，这会导致违反参数推断中的标准假设，并增加得出误导性结论的风险，除非妥善处理。两部分模型通常用于处理这个问题。然而，标准的两部分模型在获得具有协变量效应边际解释的参数估计方面存在局限性，而协变量效应在许多生物医学应用中很重要。最近提出了边际化两部分模型，但它们的发展仅限于对数正态和对数偏态正态分布。因此，在本文中，我们提出一种有限混合方法，以威布尔混合回归作为特殊情况，来处理这个问题。我们使用广泛的模拟研究来评估所提出模型在有限样本中的性能，并通过统计信息和均方误差标准与其他模型家族进行比较。我们展示了它在成瘾性障碍随机对照试验真实数据上的应用。我们的结果表明，当非零部分是从威布尔或类似分布（如伽马分布或截断高斯分布）模拟而来时，两成分威布尔混合模型更适合用于对零值占主导的连续数据进行建模。
