非相称多结果回归模型中的缺失数据。

IF 0.7 4区数学 Q3 STATISTICS & PROBABILITY

Revstat-Statistical Journal Pub Date : 2011-03-01

Armando Teixeira-Pinto, Sharon-Lise Normand

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引用次数: 0

摘要

生物医学研究通常涉及不同尺度(连续、二元和有序)的多个结果的测量。分析此类数据的常见方法是忽略结果之间的潜在相关性，并分别为每个结果建模。这不仅会导致效率的损失，而且还会在缺少数据的情况下导致有偏差的估计。我们解决了多个不相称结果背景下的数据缺失问题。描述了使用似然和准似然方法时缺失数据的后果，并提出了将这些方法扩展到结果中缺失观测值的情况。两个真实数据示例说明了该方法。

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MISSING DATA IN REGRESSION MODELS FOR NON-COMMENSURATE MULTIPLE OUTCOMES.

Biomedical research often involves the measurement of multiple outcomes in different scales (continuous, binary and ordinal). A common approach for the analysis of such data is to ignore the potential correlation among the outcomes and model each outcome separately. This can lead not only to loss of efficiency but also to biased estimates in the presence of missing data. We address the problem of missing data in the context of multiple non-commensurate outcomes. The consequences of missing data when using likelihood and quasi-likelihood methods are described, and an extension of these methods to the situation of missing observations in the outcomes is proposed. Two real data examples illustrate the methodology.

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来源期刊

Revstat-Statistical Journal STATISTICS & PROBABILITY-

CiteScore

1.30

自引率

11.10%

发文量

审稿时长

>12 weeks

期刊介绍： The aim of REVSTAT Statistical Journal is to publish articles of high scientific content, developing Statistical Science focused on innovative theory, methods and applications in different areas of knowledge.