A Comparison of Some Approximate Confidence Intervals for a Single Proportion for Clustered Binary Outcome Data

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2016-11-01 DOI:10.1515/ijb-2015-0024

Krishna K. Saha, Daniel Miller, Suojin Wang

引用次数: 10

Abstract

Abstract Interval estimation of the proportion parameter in the analysis of binary outcome data arising in cluster studies is often an important problem in many biomedical applications. In this paper, we propose two approaches based on the profile likelihood and Wilson score. We compare them with two existing methods recommended for complex survey data and some other methods that are simple extensions of well-known methods such as the likelihood, the generalized estimating equation of Zeger and Liang and the ratio estimator approach of Rao and Scott. An extensive simulation study is conducted for a variety of parameter combinations for the purposes of evaluating and comparing the performance of these methods in terms of coverage and expected lengths. Applications to biomedical data are used to illustrate the proposed methods.

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聚类二值结果数据单比例近似置信区间的比较

聚类研究中出现的二元结果数据分析中比例参数的区间估计是许多生物医学应用中的一个重要问题。在本文中，我们提出了两种基于轮廓似然和威尔逊分数的方法。我们将它们与现有的两种用于复杂调查数据的方法和其他一些方法进行了比较，这些方法是众所周知的方法的简单扩展，如似然方法，Zeger和Liang的广义估计方程以及Rao和Scott的比率估计方法。为了评估和比较这些方法在覆盖范围和预期长度方面的性能，对各种参数组合进行了广泛的模拟研究。应用于生物医学数据来说明所提出的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.